{"sector":"all","count":100,"totals":{"crypto":48,"general":63},"rubric":{"rubric_version":3,"dimensions":[{"key":"foundations","label":"Mathematical Foundations","max":20,"description":"Degrees, theses, papers and code in linear algebra, matrix & tensor methods, optimization and statistical learning — the math the field stands on.","group":"core_research","added_in":2},{"key":"vector_embeddings","label":"Vector Embeddings","max":20,"description":"Vector-space models, LSA/LSI, word and sentence embeddings, contrastive / dense retrieval, vector databases and search — authored, built or shipped.","group":"core_research","added_in":2},{"key":"transformers_lm","label":"Transformer & LM Lineage","max":20,"description":"seq2seq, attention, transformers, pretraining, scaling laws and alignment — authored, led or trained.","group":"core_research","added_in":2},{"key":"frontier_founder","label":"Frontier Founder","max":20,"description":"Frontier Founder — the person's OWN work is part of the foundation today's frontier AI models are built on: the architecture, attention, embeddings, optimizers, tokenizers, pretraining objectives, scaling results, alignment methods, datasets, benchmarks or training / inference stacks those models descend from.","group":"core_research","added_in":3},{"key":"lm_domain_depth","label":"Deep Knowledge Domain Expert","max":20,"description":"Deep Knowledge Domain Expert — years and history in language modeling, the tip of the spear in AI today: depth AND duration of a verifiable, continuous record from vector-space / LSI / n-gram and neural LMs through transformers and LLM pretraining / alignment.","group":"core_research","added_in":3},{"key":"hands_on_engineering","label":"Hands-On Engineering","max":20,"description":"Personally designed, built or shipped AI systems, models, or the hardware and infrastructure under them (accelerators, training stacks, inference).","group":"practice","added_in":2},{"key":"industry_impact","label":"Scientific & Industry Impact","max":20,"description":"Built organizations or products whose CORE is these systems; citations / h-index; patents; leadership of labs that produced canonical work.","group":"practice","added_in":2},{"key":"scientific_founder","label":"Scientific & Technical Founder","max":20,"description":"Scientific & Technical Founder — operating as the scientific / technical founder of a company (founder-CTO, founder-Chief Scientist, or a founder-CEO who personally sets and executes the technical direction), scaled by the number of verifiable years of experience doing so. A founder title with the science done by others does not earn it.","group":"practice","added_in":3}],"dimension_labels":{"foundations":"Mathematical Foundations","vector_embeddings":"Vector Embeddings","transformers_lm":"Transformer & LM Lineage","frontier_founder":"Frontier Founder","lm_domain_depth":"Deep Knowledge Domain Expert","hands_on_engineering":"Hands-On Engineering","industry_impact":"Scientific & Industry Impact","scientific_founder":"Scientific & Technical Founder"},"anchors":{"18-20":"Authored canonical work the field builds on / principal builder of systems the field runs on.","13-17":"PhD-level work, or production systems built and led personally.","8-12":"Strong graduate training, or senior engineering adjacent to the core.","3-7":"Uses the tools, manages builders, no personal record.","0-2":"Nothing verifiable."},"anchors_by_dimension":{"frontier_founder":{"18-20":"Authored / built a method, architecture, dataset or system today's frontier models directly descend from (transformer & attention, scaling laws, RLHF / instruction tuning, word2vec / GloVe, the canonical training or inference stacks).","13-17":"A documented component the frontier labs cite and build on (optimizer, tokenizer, positional encoding, retrieval method, benchmark, alignment technique).","8-12":"Published lineage work the frontier stack demonstrably draws on, but not a named building block.","3-7":"Applies or fine-tunes frontier models; no foundational contribution.","0-2":"Nothing verifiable."},"lm_domain_depth":{"18-20":"15+ years of hands-on language-modeling work from the pre-word2vec era (vector-space / LSI / n-gram / early neural LMs) through transformers, still active.","13-17":"8-15 years of continuous personal language-modeling research or systems work.","8-12":"3-8 years with a real record.","3-7":"Under 3 years, intermittent, or adjacent (general ML with no language-modeling record).","0-2":"Nothing verifiable."},"scientific_founder":{"18-20":"15+ years operating as the scientific / technical founder of companies whose core is these systems, personally authoring the core research, code or patents.","13-17":"8-15 years in that role, or multiple such companies.","8-12":"3-8 years as a verifiable technical founder.","3-7":"Founder or CEO of an AI company whose science and engineering were done by others, or a technical founder outside this field.","0-2":"Nothing verifiable."}},"weighting":{"core_research_dimensions":["foundations","vector_embeddings","transformers_lm","frontier_founder","lm_domain_depth"],"core_research_weight":0.7,"practice_dimensions":["hands_on_engineering","industry_impact","scientific_founder"],"practice_weight":0.3,"formula":"weighted_score = round(70 * (foundations + vector_embeddings + transformers_lm + frontier_founder + lm_domain_depth) / 100 + 30 * (hands_on_engineering + industry_impact + scientific_founder) / 60)","formula_v2":"weighted_score = round(70 * (foundations + vector_embeddings + transformers_lm) / 60 + 30 * (hands_on_engineering + industry_impact) / 40)"},"penalties":[{"key":"bought_popularity","label":"Pay-for-play / bought popularity","max":10,"description":"Paid coverage, paid placements, purchased followers or reach."},{"key":"capital_without_competence","label":"Capital without competence","max":10,"description":"Founded or funded an AI company on family / friends / personal wealth with no verifiable language-modeling knowledge."}],"penalty_rule":"score = max(0, weighted_score - sum(penalties)). A penalty is applied ONLY with a live cited source URL; never on rumour.","score_formula":"score = max(0, weighted_score - bought_popularity - capital_without_competence)","max_score":100,"tiers":[{"key":"frontier_builder","label":"Frontier Builder","min_score":85,"description":"Authored the mathematics, embedding or transformer work the field builds on, and built the systems that run it."},{"key":"deep_practitioner","label":"Deep Practitioner","min_score":65,"description":"Personally built, trained or led core embedding / language-model systems, with a real publication or engineering record behind it."},{"key":"technically_fluent","label":"Technically Fluent","min_score":45,"description":"Graduate-level grounding in the math and the model lineage; applies it, but is not a primary author or builder."},{"key":"informed_operator","label":"Informed Operator","min_score":25,"description":"Runs AI-adjacent organizations. The expertise is operational — the models were built by other people."},{"key":"narrative_only","label":"Narrative Only","min_score":0,"description":"No verifiable record in the mathematics, embeddings or the transformer / language-model lineage. The claim is narrative."}],"sectors":["crypto","general"],"min_confidence_to_publish":0.45,"notes":"Popularity is not evidence: news coverage, keynote presence, follower counts, token market cap, fundraising and \"AI company\" branding carry zero weight and may not appear in a rationale as support. Depth of experience counts — pre-2013 (pre-word2vec) vector-space / LSI work is foundational lineage, not \"old\". Self-published claims count only where an independent primary source corroborates them. Every profile is scored by the identical pipeline; there is no special handling for any person, including the platform's own founder.","legacy_dimension_labels":{"research":"LM Research","vector_space":"Vector Space","hands_on":"Hands-On","technical_communication":"Technical Depth","track_record":"Track Record","foundations":"Mathematical Foundations","vector_embeddings":"Vector Embeddings","transformers_lm":"Transformer & LM Lineage","hands_on_engineering":"Hands-On Engineering","industry_impact":"Scientific & Industry Impact"}},"dimension_labels":{"research":"LM Research","vector_space":"Vector Space","hands_on":"Hands-On","technical_communication":"Technical Depth","track_record":"Track Record","foundations":"Mathematical Foundations","vector_embeddings":"Vector Embeddings","transformers_lm":"Transformer & LM Lineage","hands_on_engineering":"Hands-On Engineering","industry_impact":"Scientific & Industry Impact","frontier_founder":"Frontier Founder","lm_domain_depth":"Deep Knowledge Domain Expert","scientific_founder":"Scientific & Technical Founder"},"export":"/api/v1/ceo-ai-leaderboard/export.json","methodology":"/api/v1/ceo-ai-leaderboard/methodology","generated_at":"2026-09-14T03:17:05.218712+00:00","entries":[{"id":1,"slug":"ilya-sutskever","name":"Ilya Sutskever","title":"Co-Founder & Chief Scientist","company":"Safe Superintelligence Inc.","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Ilya_Sutskever","image_url":"/api/v1/ceo-ai-leaderboard/portrait/ilya-sutskever.jpg","score":97,"tier":"frontier_builder","dimensions":{"foundations":20,"vector_embeddings":19,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":19,"hands_on_engineering":20,"industry_impact":20,"scientific_founder":17},"rubric_version":3,"weighted_score":97,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Sutskever earned a PhD in computer science at the University of Toronto (thesis: 'Training Recurrent Neural Networks', 2013) under Geoffrey Hinton, and personally co-authored AlexNet (2012, with Krizhevsky and Hinton) which catalyzed the deep-learning era. He co-invented sequence-to-sequence learning with attention-adjacent architectures (Sutskever, Vinyals, Le 2014), a direct precursor in the seq2seq->transformer lineage, and was a co-author on 'Distributed Representations of Words and Phrases' (word2vec, 2013). As OpenAI co-founder and chief scientist (2015-2024) he personally shaped GPT-2/GPT-3/GPT-4 research direction and post-training. This is a canonical, field-defining research and engineering record spanning math foundations through the full attention/transformer/scaling lineage, not organizational leadership alone.\n\nSutskever authored building blocks that today's frontier models directly descend from: 'Sequence to Sequence Learning with Neural Networks' (2014) is the encoder-decoder precursor the transformer displaced yet built on, 'Distributed Representations of Words and Phrases' (word2vec, 2013) is canonical embedding work, and as OpenAI chief scientist he co-authored 'Language Models are Few-Shot Learners' (GPT-3, 2020) and CLIP (2021) — all cited by and built into GPT/Claude/Gemini/Llama-class systems. His language-modeling record is continuous from pre-word2vec neural LMs ('Generating Text with Recurrent Neural Networks', ICML 2011) and his RNN-training PhD thesis (2013) through seq2seq, GPT-2/3/4 pretraining and alignment, and now SSI — ~15 years, still active. As a technical founder he co-founded OpenAI (2015) serving as chief scientist personally setting research direction through May 2024 (~9 years), then co-founded and now leads Safe Superintelligence (2024–), ~11 years total across two such companies.","evidence":[{"claim":"PhD in computer science, University of Toronto, 2013, advisor Geoffrey Hinton, thesis 'Training Recurrent Neural Networks'","source_url":"https://en.wikipedia.org/wiki/Ilya_Sutskever","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-inventor of AlexNet with Alex Krizhevsky and Geoffrey Hinton (2012 ImageNet paper, 200k+ citations on Google Scholar)","source_url":"https://scholar.google.com/citations?user=x04W_mMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author 'Distributed Representations of Words and Phrases and their Compositionality' (word2vec extension, 2013)","source_url":"https://doi.org/10.48550/arxiv.1310.4546","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author 'Sequence to Sequence Learning with Neural Networks' (2014), a foundational seq2seq paper in the pre-transformer attention lineage","source_url":"https://doi.org/10.48550/arxiv.1409.3215","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI co-founder (2015) and Chief Scientist through May 2024, overseeing GPT research; now CEO/co-founder of Safe Superintelligence Inc.","source_url":"https://www.cnbc.com/2025/07/03/ilya-sutskever-is-ceo-of-safe-superintelligence-after-meta-hired-gross.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD under Geoffrey Hinton at University of Toronto; specializes in machine learning; co-created AlexNet with Krizhevsky and Hinton; won NeurIPS Test of Time Award three years running (2022-2024)","source_url":"https://en.wikipedia.org/wiki/Ilya_Sutskever","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (x04W_mMAAAAJ) lists ~848,637 citations, h-index 109, i10-index 172; top works ImageNet/AlexNet (2012), Language Models are Few-Shot Learners (2020), CLIP (2021), Dropout (2014), Sequence to Sequence Learning with Neural Networks (2014)","source_url":"https://scholar.google.com/citations?user=x04W_mMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sutskever, Vinyals and Le won the NeurIPS 2024 Test of Time award for 'Sequence to Sequence Learning with Neural Networks'","source_url":"https://blog.neurips.cc/2024/11/27/announcing-the-neurips-2024-test-of-time-paper-awards/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Test of Time award talk for 'Distributed Representations of Words and Phrases and their Compositionality' (word2vec) at NeurIPS 2023","source_url":"https://neurips.cc/virtual/2023/test-of-time/83333","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Theory/foundations papers authored with Hinton: 'Deep, narrow sigmoid belief networks are universal approximators' (Neural Comput, 2008) and 'Temporal-kernel recurrent neural networks' (Neural Netw, 2010)","source_url":"https://pubmed.ncbi.nlm.nih.gov/18533819/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sequence to Sequence Learning with Neural Networks (2014) — foundational encoder-decoder work in the seq2seq→transformer lineage; NeurIPS 2024 Test of Time award","source_url":"https://doi.org/10.48550/arxiv.1409.3215","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Language Models are Few-Shot Learners (GPT-3, 2020) — a frontier-model paper Sutskever co-authored as OpenAI chief scientist","source_url":"https://doi.org/10.48550/arxiv.2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Generating Text with Recurrent Neural Networks (ICML 2011) — pre-word2vec neural language-modeling work, anchoring 15 years of continuous LM research","source_url":"https://icml.cc/2011/papers/524_icmlpaper.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI co-founder (2015) and Chief Scientist through May 2024; co-founder and CEO of Safe Superintelligence Inc. (2024–)","source_url":"https://en.wikipedia.org/wiki/Ilya_Sutskever","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014) — direct precursor in the seq2seq→transformer lineage frontier LMs descend from; NeurIPS 2024 Test of Time","source_url":"https://doi.org/10.48550/arxiv.1409.3215","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of GPT-3 'Language Models are Few-Shot Learners' (2020) and word2vec 'Distributed Representations of Words and Phrases' (2013) — both directly built into the frontier LM stack","source_url":"https://doi.org/10.48550/arxiv.1310.4546","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI co-founder (2016 per Wikidata) and Chief Scientist through 2024; now co-founder of Safe Superintelligence Inc. — technical/scientific founder authoring core research","source_url":"https://en.wikipedia.org/wiki/Ilya_Sutskever","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.96,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":1},{"id":2,"slug":"noam-shazeer","name":"Noam Shazeer","title":"VP of Engineering","company":"OpenAI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","image_url":null,"score":92,"tier":"frontier_builder","dimensions":{"foundations":18,"vector_embeddings":16,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":20,"hands_on_engineering":20,"industry_impact":20,"scientific_founder":12},"rubric_version":3,"weighted_score":92,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Shazeer is second author on 'Attention Is All You Need' (Vaswani et al. 2017), the paper that introduced the transformer architecture underlying essentially all modern LLMs, and first/co-author on 'Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer' (2017), the canonical MoE paper used in current frontier models. He co-authored T5 (Raffel et al. 2019), PaLM (Chowdhery et al. 2022), and Switch Transformers (Fedus, Zoph & Shazeer 2022), spanning pretraining, scaling and sparse-model architecture design across a ~25-year engineering career at Google (from ~2000, including early search-ranking/spelling-correction work) and later Character.AI, which he co-founded and which built and shipped a large-scale conversational LLM product used by tens of millions. His formal math/CS training is a Duke BS plus an incomplete UC Berkeley graduate program (no PhD), so foundations is scored high but not maximal; vector_embeddings reflects strong representation-learning work embedded in his transformer/LM papers rather than a dedicated embeddings/retrieval research line. This is a canonical, field-defining author record, not organizational leadership alone.\n\nShazeer's own work is load-bearing foundation for every frontier LLM: he co-invented the transformer ('Attention Is All You Need,' proposing scaled dot-product/multi-head attention), authored the sparsely-gated Mixture-of-Experts layer and multi-query attention ('Fast Transformer Decoding'), the Adafactor optimizer, and co-authored T5, GShard, Switch Transformers and PaLM — architecture, attention, sparsity, optimizer and scaling components GPT/Claude/Gemini/Llama-class systems directly descend from. His language-modeling record is continuous from ~1999–2000 (Google search spelling correction, an n-gram/statistical LM problem) through Meena, the transformer, and Gemini co-lead work in 2024–2026, roughly 25 years at the tip of the field. As scientific founder he co-founded Character.AI (2021–2024), personally setting and executing the technical direction and building its conversational LLM — a strong founder-technologist record but limited to ~3 years in that role, at the low end of the band.","evidence":[{"claim":"Second author on 'Attention Is All You Need' (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, 2017), which introduced the transformer architecture","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer' (2017), the canonical mixture-of-experts scaling paper","source_url":"https://arxiv.org/abs/1701.06538","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer' (T5, Raffel et al. 2019/2020)","source_url":"https://arxiv.org/abs/1910.10683","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'PaLM: Scaling Language Modeling with Pathways' (Chowdhery et al. 2022)","source_url":"https://arxiv.org/abs/2204.02311","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows ~394,944 citations, h-index 77, i10-index 140, including 'Switch Transformers' (Fedus, Zoph, Shazeer)","source_url":"https://scholar.google.com/citations?user=wsGvgA8AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikipedia biography: Duke University math/CS education (1994-1998), Google employee from ~2000, co-founded Character.AI in 2021 with Daniel de Freitas, returned to Google in 2024 as Gemini technical co-lead, joined OpenAI as VP of Engineering in June 2026","source_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Attention Is All You Need' (2017); the paper's footnote states 'Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail.'","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sole author of 'Fast Transformer Decoding: One Write-Head Is All You Need' (2019), introducing multi-query attention.","source_url":"https://arxiv.org/abs/1911.02150","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile: ~394,944 citations, h-index 77; top works include Attention Is All You Need, T5, PaLM, sparsely-gated Mixture-of-Experts, Switch Transformers, GShard, Gemini 2.5.","source_url":"https://scholar.google.com/citations?user=wsGvgA8AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS in mathematics and computer science from Duke (1994-1998); at Google from 2000, worked on the search spelling corrector and Meena with Daniel de Freitas; co-founded Character.AI in 2021; co-led Gemini with Jeff Dean and Oriol Vinyals.","source_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Shazeer proposed scaled dot-product attention, multi-head attention and the parameter-free position representation in 'Attention Is All You Need' (2017), the transformer architecture underlying all frontier LLMs","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored the sparsely-gated Mixture-of-Experts layer (2017) and multi-query attention ('Fast Transformer Decoding', 2019), both built into frontier model training and inference stacks","source_url":"https://arxiv.org/abs/1911.02150","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"At Google from ~2000 working on the search spelling corrector and later Meena, giving a continuous ~25-year language-modeling record; co-founded Character.AI in 2021 with Daniel de Freitas and co-led Gemini in 2024","source_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikipedia: at Google from ~2000 (search spelling corrector, later Meena with Daniel de Freitas), co-founded Character.AI in 2021, returned to Google to co-lead Gemini in 2024 — a continuous language-modeling record spanning ~25 years","source_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Character.AI in 2021 as its technical leader building a large-scale conversational LLM, until returning to Google in 2024 (~3 years as technical founder)","source_url":"https://en.wikipedia.org/wiki/Noam_Shazeer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.93,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":2},{"id":23,"slug":"chris-re","name":"Chris Ré","title":"Co-founder (Snorkel AI, Together AI); Full Professor","company":"Stanford University / Snorkel AI / Together AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Christopher_R%C3%A9","image_url":"/api/v1/ceo-ai-leaderboard/portrait/chris-re.jpg","score":88,"tier":"frontier_builder","dimensions":{"foundations":19,"vector_embeddings":16,"transformers_lm":18,"frontier_founder":20,"lm_domain_depth":14,"hands_on_engineering":19,"industry_impact":20,"scientific_founder":16},"rubric_version":3,"weighted_score":88,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Christopher Ré is a tenured full professor of Computer Science at Stanford (Stanford AI Lab, HazyResearch lab), PhD from University of Washington (dossier's Wikidata 'Cornell' entry and doctoral-advisor field appear to conflate sources — Stanford's own profile lists him as UW PhD under Dan Suciu), MacArthur Fellow (2015) for work on machine-learning data systems. His real Google Scholar profile shows roughly 87,000+ citations, far above the dossier's OpenAlex-matched value of h-index 8 / 499 citations, which is a wrong-person match (that OpenAlex record is a University of Delaware/Beirut-affiliated researcher, not this Chris Ré). His authored/led canonical work includes data programming and weak supervision (Snorkel), DeepDive (acquired into Apple via Lattice.io), and more recently foundation-model/long-sequence architecture research (state-space model lineage) and the Evo genomic foundation model line — this is authored, field-shaping systems and research, not commentary. He co-founded Snorkel AI, Together AI (board), and SambaNova-adjacent work, translating this research into production ML infrastructure companies. Given the corrected identity, this is a canonical, high-depth research-founder record.\n\nRé is a canonical frontier founder: FlashAttention (Dao, Fu, Ermon, Rudra, Ré, 2022) is the IO-aware exact-attention kernel that GPT/Claude/Gemini/Llama-class models train and serve on, and his HiPPO (2020)/S4 (2021)/H3 (2022) line is the structured-state-space lineage the Mamba/long-context stack descends from, with Hogwild! (2011) a foundational async-SGD optimizer — so frontier_founder is at the ceiling. His language-modeling / sequence-architecture record specifically runs from HiPPO (2020) through H3/FlashAttention and the Evo genomic sequence models (2024-26), roughly six years of deep, field-shaping but relatively recent work (earlier 2005-2016 output was probabilistic databases and weak supervision, adjacent not LM), placing lm_domain_depth in the strong-but-not-15-year band. As scientific/technical co-founder he spun his own research into multiple ML-systems companies — SambaNova (2017), Snorkel AI (2019, from his Snorkel weak-supervision work), and Together AI (2022) — while remaining a Stanford professor rather than a full-time founder-CTO, giving ~9 years across several such companies.","evidence":[{"claim":"Full professor of Computer Science at Stanford University, Stanford AI Lab; MacArthur Fellowship 2015 for machine-learning data-systems research","source_url":"https://engineering.stanford.edu/people/chris-re","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows citation count of approximately 87,182 (far exceeding the dossier's mismatched OpenAlex figure of 499/h-index 8)","source_url":"https://scholar.google.com/citations?user=DnnCWN0AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded four ML-systems companies from his research: SambaNova, Snorkel (data programming/weak supervision), Lattice/DeepDive (acquired by Apple 2017), Inductiv/HoloClean (acquired by Apple 2020)","source_url":"https://en.wikipedia.org/wiki/Christopher_R%C3%A9","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Research spans database theory, database systems, and machine learning, with best-paper awards at PODS 2012, SIGMOD 2014, and ICML 2016","source_url":"https://cs.stanford.edu/people/chrismre/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar DnnCWN0AAAAJ (Stanford CS): 87,725 citations, h-index 121, i10-index 339; top works include FlashAttention (2022), S4 (2021), Hogwild! (2011), Snorkel (2017), HiPPO (2020), H3 (2022)","source_url":"https://scholar.google.com/citations?user=DnnCWN0AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — authors Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré","source_url":"https://arxiv.org/abs/2205.14135","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cornell BS, University of Washington PhD under Dan Suciu, full professor at Stanford, MacArthur Fellowship 2015, co-founded Lattice.io (acquired by Apple May 2017)","source_url":"https://en.wikipedia.org/wiki/Christopher_R%C3%A9","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stanford faculty page: member of SAIL, CRFM and the ML Group; current lab projects include ThunderKittens AI kernels, Intelligence per Watt, and Evo foundation models for biological sequences","source_url":"https://cs.stanford.edu/~chrismre/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Efficiently Modeling Long Sequences with Structured State Spaces (S4), Gu, Goel, Ré","source_url":"https://arxiv.org/abs/2111.00396","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"HiPPO: Recurrent Memory with Optimal Polynomial Projections (2020), Gu, Dao, Ermon, Rudra, Ré","source_url":"https://arxiv.org/abs/2008.07669","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré (2022); the standard attention kernel in frontier LLM training/inference","source_url":"https://arxiv.org/abs/2205.14135","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded SambaNova Systems, Snorkel AI, and Together AI, translating his Stanford ML-systems research into companies","source_url":"https://cs.stanford.edu/~chrismre/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stanford faculty page (SAIL/CRFM/ML Group) documenting his LM-architecture and foundation-model research program and company spinouts","source_url":"https://cs.stanford.edu/~chrismre/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded SambaNova, Snorkel (data programming/weak supervision), Lattice/DeepDive (Apple 2017) and Inductiv/HoloClean (Apple 2020) from his own research","source_url":"https://en.wikipedia.org/wiki/Christopher_R%C3%A9","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":3},{"id":10,"slug":"richard-socher","name":"Richard Socher","title":"Co-founder & CEO","company":"You.com","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Richard_Socher","image_url":"/api/v1/ceo-ai-leaderboard/portrait/richard-socher.png","score":87,"tier":"frontier_builder","dimensions":{"foundations":18,"vector_embeddings":20,"transformers_lm":16,"frontier_founder":18,"lm_domain_depth":17,"hands_on_engineering":17,"industry_impact":18,"scientific_founder":14},"rubric_version":3,"weighted_score":87,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Socher earned a Stanford CS PhD (2014) under Christopher D. Manning and co-authored GloVe (2014), one of the two canonical word-embedding algorithms the field's vector-space lineage is built on, alongside foundational pre-transformer deep-learning NLP work (Recursive Deep Models for Semantic Compositionality / Recursive Neural Tensor Networks, 2013; Tree-LSTM, 2015) that directly extended the matrix/tensor and compositional-representation-learning tradition. His verified Google Scholar profile (stanford.edu-verified) shows 256,483 citations and an h-index of 114, with GloVe alone carrying ~51,700 citations. He personally built and led MetaMind (founded 2014, acquired by Salesforce 2016, becoming Salesforce Chief Scientist through 2020) and founded You.com (2020), an AI search company, giving him a rare combination of canonical authored research plus personally-led production AI systems. His transformers_lm score reflects strong pre-transformer language-modeling-lineage authorship (recursive/compositional neural nets, embeddings feeding into later LM pretraining) and applied leadership of transformer-era products at You.com, rather than being a co-author of the Transformer paper itself. This is a researcher-founder profile the rubric explicitly identifies as scoring high — not fame-driven.\n\nSocher co-authored GloVe (2014), one of the two canonical word-embedding algorithms explicitly named in the frontier lineage — dense vector representations of exactly the kind that the tokenizer/embedding layers of GPT/Claude/Gemini-class models descend from — plus contextual-vector (CoVe, 2017) and a 1.63B-parameter conditional transformer LM (CTRL, 2019) that directly feed the controllable-generation and pretraining lineage. His continuous, hands-on language-modeling record runs from early recursive/compositional neural nets (~2010-2011) through GloVe, Tree-LSTM, CoVe and CTRL to running You.com's LLM search stack, ~15-16 years still active. As a scientific/technical founder he authored the core research his companies run on — MetaMind (2014, acquired by Salesforce 2016), You.com (2020-present), Recursive (2025) — roughly 8 verifiable years as a research-author founder across multiple such companies, though the Salesforce Chief Scientist interval (2016-2020) was a non-founder role.","evidence":[{"claim":"Richard Socher received his PhD in Computer Science from Stanford University in 2014.","source_url":"https://en.wikipedia.org/wiki/Richard_Socher","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher co-authored 'GloVe: Global Vectors for Word Representation' (2014) with Jeffrey Pennington and Christopher D. Manning, a canonical word-embedding paper with ~51,700 citations on Google Scholar.","source_url":"https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher's verified Google Scholar profile (stanford.edu email) shows 256,483 total citations, h-index 114, i10-index 244, including 'Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank' (2013, ~12,280 citations) and 'Improved Semantic Representations from Tree-Structured LST","source_url":"https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher founded MetaMind in 2014, which was acquired by Salesforce in 2016, after which he served as Salesforce's Chief Scientist; he later founded You.com in 2020.","source_url":"https://en.wikipedia.org/wiki/Richard_Socher","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GloVe: Global Vectors for Word Representation — Jeffrey Pennington, Richard Socher, Christopher Manning, EMNLP 2014","source_url":"https://aclanthology.org/D14-1162/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (Recursive, you.com, AIX): 256,483 citations, h-index 114; top works ImageNet 2009, GloVe 2014, Recursive Deep Models 2013","source_url":"https://scholar.google.com/citations?user=FaOcyfMAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Learned in Translation: Contextualized Word Vectors (CoVe) — McCann, Bradbury, Xiong, Socher, 2017; contextual vectors from a deep LSTM attentional seq2seq encoder","source_url":"https://arxiv.org/abs/1708.00107","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CTRL: A Conditional Transformer Language Model (1.63B parameters) — Keskar, McCann, Varshney, Xiong, Socher, 2019","source_url":"https://arxiv.org/abs/1909.05858","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stanford CS PhD 2014 under Christopher Manning; founded MetaMind 2014 (acquired by Salesforce 2016); Salesforce Chief Scientist 2016-2020; co-founded You.com 2020","source_url":"https://en.wikipedia.org/wiki/Richard_Socher","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher co-authored 'GloVe: Global Vectors for Word Representation' (EMNLP 2014), a canonical word-embedding algorithm.","source_url":"https://aclanthology.org/D14-1162/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CTRL: A Conditional Transformer Language Model (1.63B parameters), Keskar, McCann, Varshney, Xiong, Socher, 2019.","source_url":"https://arxiv.org/abs/1909.05858","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher founded MetaMind (2014, acquired by Salesforce 2016), was Salesforce Chief Scientist 2016-2020, and co-founded/CEO You.com (2020) and Recursive.","source_url":"https://en.wikipedia.org/wiki/Richard_Socher","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GloVe: Global Vectors for Word Representation — Pennington, Socher, Manning, EMNLP 2014, a canonical word-embedding algorithm in the vector-space lineage frontier models build on.","source_url":"https://aclanthology.org/D14-1162/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Socher earned a Stanford CS PhD (2014) under Christopher D. Manning and co-founded/leads You.com (an AI search company) and Recursive; earlier founded MetaMind (acq. Salesforce 2016, Chief Scientist to 2020).","source_url":"https://en.wikipedia.org/wiki/Richard_Socher","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.89,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":4},{"id":104,"slug":"ashish-vaswani","name":"Ashish Vaswani","title":"Co-founder & CEO (Essential AI); joined Nvidia via 2026 acqui-hire","company":"Essential AI","sector":"general","profile_url":null,"image_url":null,"score":86,"tier":"frontier_builder","dimensions":{"foundations":16,"vector_embeddings":15,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":16,"hands_on_engineering":18,"industry_impact":20,"scientific_founder":12},"rubric_version":3,"weighted_score":86,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Ashish Vaswani is the lead/first author of 'Attention Is All You Need' (NeurIPS 2017), the paper that introduced the Transformer architecture and is arguably the single most consequential paper in the modern language-model lineage — an unambiguous 18-20 anchor case ('authored canonical work the field builds on'). He holds a PhD in Computer Science from USC (2014, advisors David Chiang and Liang Huang), thesis 'Smaller, Faster, and Accurate Models for Statistical Machine Translation', giving him genuine graduate-level foundations in statistical/optimization methods for sequence modeling predating the Transformer. He worked at Google Brain, co-founded Adept AI (2022) and Essential AI (2023) as CEO, building applied AI-agent/foundation-model companies, and as of mid-2026 he and the Essential AI team were acqui-hired by Nvidia to work on the Nemotron model family — continued hands-on model-building at a frontier lab. vector_embeddings is scored below transformers_lm/foundations since his direct authored work is concentrated in attention/seq2seq/MT rather than embeddings/retrieval specifically, though attention mechanisms are adjacent.\n\nVaswani is the lead author of 'Attention Is All You Need' (NeurIPS 2017), which introduced the Transformer and multi-head self-attention — the exact architecture every current frontier model (GPT, Claude, Gemini, Llama, and Nvidia's own Nemotron) is built on and cites directly, an unambiguous 18-20 frontier_founder case. His language-modeling record is continuous and deep: EMNLP 2013 'Decoding with Large-Scale Neural Language Models Improves Translation' and his 2014 USC PhD on statistical machine translation predate word2vec and run through seq2seq, transformers, and today's LLM pretraining at Essential AI (~13 years, still active), placing him at the top of the 8-15-year lm_domain_depth band. As a technical/scientific founder he co-founded Adept AI (2022) and co-founded and leads Essential AI (2023) as the CEO who sets the technical direction and personally authored the core science the field runs on, but that founder tenure is only ~4 years, landing scientific_founder in the 3-8-year band rather than higher.","evidence":[{"claim":"Lead author, 'Attention Is All You Need', Google Brain, NeurIPS 2017 — introduced the Transformer architecture","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Adept AI (2022) then Essential AI (2023) as CEO after Google Brain","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"As of June 2026, Vaswani and Essential AI were acqui-hired by Nvidia, joining to work on the Nemotron open-source model family","source_url":"https://www.groundlevel-ai.com/p/nvidia-quietly-acquihires-essential","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar record (name-exact match): 57 papers, 195,978 citations, h-index 26","source_url":"https://www.semanticscholar.org/author/Ashish-Vaswani/40348417","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 40348417: 195,978 citations, h-index 26, with 'Attention is All you Need' (2017) at ~191,942 citations","source_url":"https://api.semanticscholar.org/graph/v1/author/40348417?fields=name,citationCount,hIndex,paperCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Attention Is All You Need (2017) — Ashish Vaswani listed as first author","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from University of Southern California (2014) under David Chiang and Liang Huang, thesis 'Smaller, Faster, and Accurate Models for Statistical Machine Translation'; Google Brain 2016-2021; co-founded Adept AI then Essential AI; acqui-hired by Nvidia June 2026","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Image Transformer (ICML 2018) — Vaswani co-author, extends self-attention to image generation","source_url":"https://arxiv.org/abs/1802.05751","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stand-Alone Self-Attention in Vision Models (NeurIPS 2019) — Vaswani co-author","source_url":"https://arxiv.org/abs/1906.05909","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani is lead/first author of 'Attention Is All You Need' (2017), which introduced the Transformer architecture and self-attention now underpinning all frontier LLMs","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"EMNLP 2013 'Decoding with Large-Scale Neural Language Models Improves Translation' (Vaswani, Zhao, Fossum, Chiang) — an early neural language-modeling paper predating word2vec-era embeddings","source_url":"https://aclanthology.org/D13-1140/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Adept AI (2022) then co-founded and serves as CEO of Essential AI (2023); acqui-hired by Nvidia June 2026 to work on the Nemotron model family","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani is first/lead author of 'Attention Is All You Need' (2017), which introduced the Transformer — the architecture all frontier LLMs are built on and cite","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD, University of Southern California (2014), thesis on statistical machine translation under David Chiang and Liang Huang — verifiable language-modeling work predating the Transformer","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Adept AI (2022), then co-founded and served as CEO of Essential AI (2023); acqui-hired by Nvidia June 2026 — operating as the scientist-founder of foundation-model companies","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":5},{"id":6,"slug":"aidan-gomez","name":"Aidan Gomez","title":"Co-founder & CEO","company":"Cohere","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Aidan_Gomez","image_url":"/api/v1/ceo-ai-leaderboard/portrait/aidan-gomez.jpg","score":84,"tier":"deep_practitioner","dimensions":{"foundations":15,"vector_embeddings":16,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":14,"hands_on_engineering":18,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":84,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Gomez is a co-author of \"Attention Is All You Need\" (Vaswani et al., 2017), the paper that introduced the Transformer architecture underlying essentially all modern LLMs — canonical, field-defining work, done as a 20-year-old Google Brain intern out of the University of Toronto, later formalized into an Oxford DPhil (advisors Yarin Gal, Yee Whye Teh) completed 2024. He is also lead/co-author of \"The Reversible Residual Network\" (RevNet, NeurIPS 2017) and an original author of Tensor2Tensor, the reference Transformer implementation — both hands-on engineering contributions to the training-efficiency side of the transformer lineage, not just the attention paper. His Google Scholar record (h-index 49, ~306k citations, dominated by the Transformer paper) shows a narrower but extremely deep footprint concentrated almost entirely in the transformer/attention area rather than broad classical ML foundations or embeddings work (no LSA/word2vec-era contributions; vector_embeddings credit here is mostly transfer from representation-learning work adjacent to the Transformer and RevNet, plus later Cohere embedding-model shipping). Post-2017 he founded Cohere, one of the few labs that trains and ships frontier-scale LLMs and production text-embedding models, giving him personal, technical leadership over systems the field runs on rather than purely business leadership. Foundations score reflects strong applied deep-learning mathematics (backprop, residual/reversible network theory) demonstrated in his own papers rather than a classical linear-algebra/optimization theory record.\n\nGomez is one of the eight authors of \"Attention Is All You Need\" (2017), the Transformer architecture that GPT/Claude/Gemini/Llama-class frontier models directly descend from, and a co-author of Tensor2Tensor, the reference Transformer/NMT implementation those stacks trace to — a first-order building block, hence a near-top frontier_founder score. His verifiable language-modeling record runs continuously from 2017 (transformers, NMT, RevNet) through founding and technically leading Cohere in 2019 to today, roughly nine years of hands-on LM work — deep but not spanning the pre-word2vec era, placing it in the 8–15-year band. As Cohere's co-founder and CEO since 2019 (~7 years) he sets and executes technical direction backed by his own canonical research, a genuine scientific/technical founder record; the score is held at the top of the 3–8-year band rather than higher because Cohere has other technical co-founders (Nick Frosst, Ivan Zhang) and his tenure is just under the 8-year threshold.","evidence":[{"claim":"Co-author of \"Attention Is All You Need\" (2017), introducing the Transformer architecture, written as a Google Brain intern during undergrad at University of Toronto.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows 306,588 total citations and h-index 49, led by the Transformer paper (284,792 citations on the 2023-updated entry).","source_url":"https://scholar.google.com/citations?user=2oq9614AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of \"The Reversible Residual Network: Backpropagation Without Storing Activations\" (NeurIPS 2017), a memory-efficient deep network training method.","source_url":"https://arxiv.org/abs/1707.04585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Doctoral studies at University of Oxford under Yarin Gal and Yee Whye Teh (OATML group), DPhil awarded 2024.","source_url":"https://oatml.cs.ox.ac.uk/members/aidan_gomez/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Cohere, an enterprise LLM company that trains and ships foundation and embedding models.","source_url":"https://en.wikipedia.org/wiki/Aidan_Gomez","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Attention Is All You Need' (2017), the transformer paper; interned at Google Brain at age 20; founded Cohere in 2019; Oxford PhD completed 2024; BSc CS+math Toronto","source_url":"https://en.wikipedia.org/wiki/Aidan_Gomez","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'The Reversible Residual Network: Backpropagation Without Storing Activations' (Gomez, Ren, Urtasun, Grosse, arXiv:1707.04585, July 2017)","source_url":"https://arxiv.org/abs/1707.04585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (Cohere affiliation): ~306,838 citations, h-index 49; top works include Attention Is All You Need, RevNet, Tensor2Tensor for Neural Machine Translation, One Model to Learn Them All, Depthwise Separable Convolutions for NMT","source_url":"https://scholar.google.com/citations?user=2oq9614AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education at Oxford and Toronto with doctoral advisors Yarin Gal and Yee Whye Teh, and Google Scholar id 2oq9614AAAAJ","source_url":"https://www.wikidata.org/wiki/Q110864219","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of \"Attention Is All You Need\" (2017), which introduced the Transformer architecture underlying essentially all frontier LLMs.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of Tensor2Tensor for Neural Machine Translation (2018), the reference Transformer implementation and NMT toolkit.","source_url":"https://arxiv.org/abs/1803.07416","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Cohere (founded 2019), an enterprise LLM company that trains and ships foundation and text-embedding models.","source_url":"https://en.wikipedia.org/wiki/Aidan_Gomez","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Attention Is All You Need' (2017), the transformer architecture underlying today's frontier LLMs.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Cohere (founded 2019), which trains and ships frontier-scale LLMs and text-embedding models; continuous NLP/LM focus.","source_url":"https://en.wikipedia.org/wiki/Aidan_Gomez","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of RevNet and Tensor2Tensor, engineering contributions to the transformer training lineage frontier stacks build on.","source_url":"https://arxiv.org/abs/1707.04585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":6},{"id":5,"slug":"dario-amodei","name":"Dario Amodei","title":"Co-founder & CEO","company":"Anthropic","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Dario_Amodei","image_url":"/api/v1/ceo-ai-leaderboard/portrait/dario-amodei.jpg","score":84,"tier":"deep_practitioner","dimensions":{"foundations":17,"vector_embeddings":11,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":16,"hands_on_engineering":18,"industry_impact":20,"scientific_founder":12},"rubric_version":3,"weighted_score":84,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Amodei's mathematical foundations are real rather than nominal: a Princeton PhD in biophysics under William Bialek and Michael J. Berry produced maximum-entropy and statistical-physics models of neural populations published in PNAS ('Thermodynamics and signatures of criticality in a network of neurons', 2015) and PLoS Computational Biology — statistical learning and optimization applied to high-dimensional data, though not a CS/ML degree, which is why foundations sits at 17 rather than at the canonical anchor. His transformer/language-model record is canonical by any reading: he is the final author of 'Language Models are Few-Shot Learners' (GPT-3, arXiv:2005.14165, verified author list: Brown … Sutskever, Amodei), an author of 'Scaling Laws for Neural Language Models' (2020), of 'Deep Reinforcement Learning from Human Preferences' (2017, the origin of RLHF) and of Constitutional AI (2022) — he authored work in both the scaling and the alignment halves of the lineage. His hands-on engineering predates the LLM era: he was a core author of Baidu's Deep Speech 2 (2015), a large-scale GPU-trained end-to-end sequence model. His verified Google Scholar profile (the ID supplied by the dossier's own Wikidata block) shows 196,269 citations and h-index 67, of which GPT-3 alone accounts for 82,085 — several times the dossier's OpenAlex figures. Vector embeddings is his one thin dimension: representation learning is implicit in his speech and LM work but he has authored no embedding, contrastive or dense-retrieval paper, so that dimension is scored on implicit representation-learning content only and lands well below the rest.\n\nAmodei's own work is load-bearing foundation for every frontier LLM: he authored 'Scaling Laws for Neural Language Models' (2020), is final author of GPT-3 'Language Models are Few-Shot Learners' (2020), co-authored 'Deep Reinforcement Learning from Human Preferences' (2017, the origin of RLHF) and Constitutional AI (2022) — the scaling, alignment and instruction-following methods GPT/Claude/Gemini/Llama-class systems are built on — so frontier_founder is at the canonical anchor. His continuous language-modeling record runs from large-scale sequence work (Baidu Deep Speech 2, 2015) through OpenAI scaling/GPT-2/GPT-3 into Anthropic's Claude, roughly 11 years of hands-on LM work, placing lm_domain_depth in the 8-15-year band but short of the 15+ pre-word2vec anchor (his earliest 2003 publications are biophysics, not LM). As co-founder and CEO of Anthropic since 2021 he personally sets and executes the technical direction and authored the core alignment research (Constitutional AI) the company runs on, but that is ~5 years as a technical founder (he was VP Research, not a founder, at OpenAI), which caps scientific_founder in the 3-8-year band.","evidence":[{"claim":"Final author of 'Language Models are Few-Shot Learners' (GPT-3); verified author list begins Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan and ends Ilya Sutskever, Dario Amodei; submitted 28 May 2020","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile 6-e-ZBEAAAAJ (CEO and Co-Founder at Anthropic): 196,269 citations, h-index 67, i10-index 101; top works 'Language models are few-shot learners' (82,085), 'Language models are unsupervised multitask learners' (24,702), 'Scaling laws for neural language models' (8,966), 'Deep re","source_url":"https://scholar.google.com/citations?user=6-e-ZBEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Scaling Laws for Neural Language Models' (Kaplan et al., 2020)","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Statistical-physics neuroscience record under Bialek and Berry: 'Thermodynamics and signatures of criticality in a network of neurons', PNAS 2015","source_url":"https://pubmed.ncbi.nlm.nih.gov/26330611/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Deep Speech 2: End-to-End Speech Recognition in English and Mandarin' (2015), built at Baidu","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD biophysics Princeton (advisors William Bialek, Michael J. Berry); OpenAI VP of Research; co-founded Anthropic 2021","source_url":"https://en.wikipedia.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD in biophysics from Princeton University, Hertz Thesis Prize 2011","source_url":"https://en.wikipedia.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Worked at Baidu under Andrew Ng (Nov 2014-Oct 2015) on Deep Speech end-to-end speech recognition","source_url":"https://ai.miraheze.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Language Models are Few-Shot Learners' (GPT-3, 2020) and 'Scaling Laws for Neural Language Models' (2020) as VP of Research at OpenAI","source_url":"https://scholar.google.com/citations?user=6-e-ZBEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Anthropic in 2021 and leads development of the Claude model family","source_url":"https://en.wikipedia.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Scaling Laws for Neural Language Models' (2020) and final author of GPT-3 'Language Models are Few-Shot Learners' (2020) — foundational to frontier LLM pretraining","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Deep Reinforcement Learning from Human Preferences' (2017), the origin of RLHF used to align frontier models","source_url":"https://arxiv.org/abs/1706.03741","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Anthropic in 2021 and leads development of the Claude model family as CEO, after serving as VP of Research at OpenAI through 2020","source_url":"https://en.wikipedia.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Core author of Baidu's 'Deep Speech 2' (2015), a large-scale GPU-trained end-to-end sequence model, marking the start of his continuous sequence/LM engineering record","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Scaling Laws for Neural Language Models' (Kaplan, McCandlish, ... Amodei, 2020) — the scaling-law result frontier training runs are sized by","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Final author of GPT-3 'Language Models are Few-Shot Learners' (2020), the direct ancestor of today's instruction-following LLMs","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Anthropic in 2021 and leads development of the Claude model family; authored Constitutional AI (2022), Anthropic's core alignment method","source_url":"https://en.wikipedia.org/wiki/Dario_Amodei","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Deep Speech 2: End-to-End Speech Recognition' (2015), large-scale GPU-trained sequence modeling marking his entry into the neural-sequence/LM lineage","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.93,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":7},{"id":105,"slug":"jakob-uszkoreit","name":"Jakob Uszkoreit","title":"Co-founder & CEO","company":"Inceptive","sector":"general","profile_url":null,"image_url":null,"score":84,"tier":"deep_practitioner","dimensions":{"foundations":15,"vector_embeddings":15,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":16,"hands_on_engineering":18,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":84,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Jakob Uszkoreit is a confirmed co-author of 'Attention Is All You Need' (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, arXiv 1706.03762, 2017) — the canonical paper that introduced the Transformer architecture, the direct origin point of the entire modern language-model lineage the rubric describes. This alone places him at the top anchor for transformers_lm (authored canonical work the field builds on). He led NLP research and engineering teams at Google Research/Google Brain prior to co-authoring the Transformer, work reflected in the dossier's OpenAlex-linked record (Google, 'Brain (Germany)', University of Washington affiliations; 53 works, ~39,325 citations), giving strong, verifiable foundations and vector_embeddings credit for pre-transformer attention/sequence-modeling and representation-learning research (attention mechanisms operate directly over learned embeddings). His hands_on_engineering is rated highly given his personal role building NLP infrastructure at Google (e.g., Tensor2Tensor-era tooling) prior to founding Inceptive, an AI-for-biology company applying transformer-style modeling to mRNA/molecule design — a genuine technical-founder profile, not a business-only leader. industry_impact is high both for the field-defining influence of the Transformer paper itself and for founding a company whose technical core is applying this lineage to a new domain.\n\nUszkoreit is a named co-author of 'Attention Is All You Need' (2017), the paper that introduced the Transformer — the exact architecture every frontier model (GPT/Claude/Gemini/Llama) directly descends from, and he is credited in accounts of the work as the person who pushed the pure-attention direction; this is the top frontier_founder anchor (an authored building block the entire frontier stack is built on). His language-modeling record runs continuously from senior NLP/machine-translation research at Google Research/Brain (~2008 onward, first indexed works earlier) through the Transformer and follow-on work (Decomposable Attention 2016, Natural Questions 2019, ViT 2020), roughly 13 years of hands-on LM work before he pivoted the modeling techniques to biology, placing lm_domain_depth in the continuous 8–15-year band. As co-founder & CEO of Inceptive (founded 2021, ~5 years), he operates as a genuine technical/scientific founder personally setting the deep-learning direction — but the company's core is transformer-style modeling applied to mRNA/RNA therapeutics rather than language modeling per se, so scientific_founder sits in the 3–8-year technical-founder band, not the 15+ tier.","evidence":[{"claim":"Jakob Uszkoreit is a co-author of 'Attention Is All You Need' (arXiv 1706.03762, 2017), alongside Ashish Vaswani, Noam Shazeer, Niki Parmar, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin — the paper that introduced the Transformer architecture.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's OpenAlex-linked profile for Uszkoreit shows affiliations at Google (United States), 'Brain (Germany)' (i.e. Google Brain), University of Washington, and UC Berkeley, with 53 works and approximately 39,325 citations — consistent with a senior NLP/ML research career at Google Research/Go","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uszkoreit's education is recorded as Technische Universität Berlin (TU Berlin), and his career trajectory (Google Research/Brain NLP team lead, then Attention Is All You Need co-author, then founder of Inceptive applying AI to mRNA/biological molecule design) is consistent across the dossier's wikid","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Attention Is All You Need — Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin; arXiv 1706.03762, 12 June 2017; Uszkoreit is fourth author","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT), 2020 — Uszkoreit is eleventh of twelve authors","source_url":"https://arxiv.org/abs/2010.11929","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Inceptive founded 2021, offices in Palo Alto, Berlin and Zurich; builds foundation models of life for mRNA, siRNA, ASO and peptide therapeutics","source_url":"https://inceptive.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata: machine learning researcher, educated at Technische Universität Berlin, employer Google, ORCID 0000-0001-5066-7530","source_url":"https://www.wikidata.org/wiki/Q98891246","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uszkoreit co-authored 'Attention Is All You Need' (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, arXiv:1706.03762, 2017), introducing the Transformer architecture that frontier LLMs are built on.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Inceptive was founded in 2021 with Uszkoreit as co-founder & CEO, building deep-learning 'foundation models of life' for mRNA/RNA therapeutics — a technical-founder role of roughly five years.","source_url":"https://inceptive.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uszkoreit co-founded and is CEO of Inceptive (founded 2021), which builds transformer-based foundation models for mRNA and biological molecule design.","source_url":"https://inceptive.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.86,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":8},{"id":106,"slug":"niki-parmar","name":"Niki Parmar","title":"Co-founder","company":"Essential AI","sector":"general","profile_url":null,"image_url":null,"score":83,"tier":"deep_practitioner","dimensions":{"foundations":15,"vector_embeddings":16,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":15,"hands_on_engineering":16,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":83,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Parmar is one of the eight equal-contributor co-authors of 'Attention Is All You Need' (NeurIPS 2017), the paper that introduced the Transformer architecture and is the direct founding text of the entire modern language-model lineage — this alone places her at the top of the transformers_lm anchor as a personal, canonical, field-defining contribution, not an adjacent or managerial one. She was a research engineer/researcher at Google Brain during this period, giving her hands-on architecture/implementation credit on the systems (attention mechanisms, encoder-decoder Transformer stack) that underlie vector-embedding-based retrieval and every subsequent LLM. She later co-founded Adept AI and, in December 2022, co-founded Essential AI with fellow Transformer co-author Ashish Vaswani (acqui-hired by Nvidia as of June 2026), extending her personal research record into founder-level industry impact building foundation-model companies. Her Semantic Scholar profile (exact name match, single unambiguous candidate) shows 80 papers and 192,205 citations with an h-index of 20, consistent with authorship of one of the most-cited papers in computer science history. Foundations is scored high (deep neural-architecture/optimization work) but not maximal since her record is concentrated in this lineage rather than broader mathematical foundations work.\n\nParmar is a named co-author of 'Attention Is All You Need' (2017), the Transformer paper that is the direct architectural foundation every frontier model (GPT, Claude, Gemini, Llama) descends from — a maximal, named building-block contribution to the frontier stack, so frontier_founder is at the top of the anchor. Her continuous personal record in the attention/transformer lineage runs from Google Brain circa 2016-2017 through Image Transformer (2018), Stand-Alone Self-Attention (2019), Conformer (2020) and Bottleneck Transformers (2021) into foundation-model companies, roughly 9-10 years of hands-on work at the tip of the spear (the vision-transformer strand tempers a pure language-modeling count, keeping this in the 8-15-year band rather than the pre-word2vec 15+ tier). As a technical co-founder she helped start Adept AI and then Essential AI (Dec 2022, acqui-hired by Nvidia June 2026) — a genuine researcher-founder authoring the core science, but only about four verifiable years in that role, placing scientific_founder in the 3-8-year band.","evidence":[{"claim":"Niki Parmar is listed as one of eight equal-contributor authors of 'Attention Is All You Need' (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin), the paper introducing the Transformer architecture.","source_url":"https://en.wikipedia.org/wiki/Attention_Is_All_You_Need","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani (fellow Transformer-paper co-author) co-founded Essential AI with Niki Parmar in December 2022; Vaswani and Essential AI were acqui-hired by Nvidia as of June 2026.","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar record for Niki Parmar (id 3877127): exact name match, single candidate (unambiguous), 80 papers, 192,205 citations, h-index 20.","source_url":"https://www.semanticscholar.org/author/3877127","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Third author of 'Attention Is All You Need' (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, 2017), the paper introducing the transformer architecture","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Image Transformer' (Parmar, Vaswani, Uszkoreit, Kaiser, Shazeer, Ku, Tran, 2018), generalising the transformer to autoregressive image generation with locally restricted self-attention, improving ImageNet NLL from 3.83 to 3.77","source_url":"https://arxiv.org/abs/1802.05751","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Second author of 'Stand-Alone Self-Attention in Vision Models' (Ramachandran, Parmar, Vaswani, Bello, Levskaya, Shlens, 2019), demonstrating self-attention as an effective stand-alone replacement for spatial convolutions in ResNet","source_url":"https://arxiv.org/abs/1906.05909","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Fourth author of 'Conformer: Convolution-augmented Transformer for Speech Recognition' (2020), achieving 1.9%/3.9% WER on LibriSpeech with language models","source_url":"https://arxiv.org/abs/2005.08100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 3877127 'Niki Parmar': 80 papers, 192,205 citations, h-index 20; Attention is All you Need at 191,946 citations, Conformer 4,295, Image Transformer 1,945, Stand-Alone Self-Attention 1,383, Bottleneck Transformers 1,217","source_url":"https://api.semanticscholar.org/graph/v1/author/3877127?fields=name,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani co-founded Essential AI with Niki Parmar in December 2022","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Niki Parmar is one of the eight equal-contributor authors of 'Attention Is All You Need' (2017), the paper introducing the Transformer architecture on which modern LLMs are built.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Parmar is first author of 'Image Transformer' (2018) and co-author of 'Stand-Alone Self-Attention in Vision Models' (2019) and 'Conformer' (2020), extending the attention/transformer lineage across 2018-2020.","source_url":"https://arxiv.org/abs/1802.05751","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani co-founded Essential AI with Niki Parmar in December 2022; Essential AI was acqui-hired by Nvidia as of June 2026.","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Niki Parmar is a co-author (third listed) of 'Attention Is All You Need' (2017), which introduced the Transformer architecture and self-attention — the direct architectural ancestor of all frontier language models.","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Parmar first-authored 'Image Transformer' (2018) and co-authored Conformer (2020) and Stand-Alone Self-Attention (2019), a continuous attention/sequence-modeling research record from 2017 onward.","source_url":"https://arxiv.org/abs/1802.05751","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ashish Vaswani co-founded Essential AI with Niki Parmar in December 2022; both were previously co-founders in the foundation-model space (Adept AI), and Essential AI was acqui-hired by Nvidia as of June 2026.","source_url":"https://en.wikipedia.org/wiki/Ashish_Vaswani","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.79,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":9},{"id":15,"slug":"percy-liang","name":"Percy Liang","title":"Professor of Computer Science; Director, Center for Research on Foundation Models; Co-founder","company":"Together AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Percy_Liang","image_url":"/api/v1/ceo-ai-leaderboard/portrait/percy-liang.jpg","score":83,"tier":"deep_practitioner","dimensions":{"foundations":19,"vector_embeddings":16,"transformers_lm":18,"frontier_founder":16,"lm_domain_depth":18,"hands_on_engineering":16,"industry_impact":19,"scientific_founder":10},"rubric_version":3,"weighted_score":83,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Percy Liang has a canonical statistical-learning and NLP research record: PhD from UC Berkeley (2011, advisors Dan Klein and Michael I. Jordan) in structured prediction and semantic parsing, followed by 15 years as a Stanford CS professor producing foundational work across probabilistic modeling, optimization, and representation learning. He created SQuAD (2016) and SQuAD 2.0 (2018), the benchmark datasets that shaped a generation of reading-comprehension and embedding/retrieval-adjacent NLP research, and authored \"Prefix-Tuning\" (2021), a widely used parameter-efficient adaptation method for transformer LMs. He is lead author of \"On the Opportunities and Risks of Foundation Models\" (2021), which named and framed the foundation-model paradigm, and founded/directs Stanford's Center for Research on Foundation Models, building HELM, the standard holistic evaluation framework for LLMs — direct, personally-led technical leadership of the transformers/scaling/alignment lineage rather than commentary about it. His verified Google Scholar record (h-index 143, ~167,000 citations) substantially exceeds the figures in the dossier's OpenAlex/Semantic Scholar blocks, confirming an exceptionally deep and continuous 20+ year research record. His industry role is co-founder of Together AI (an open LLM infrastructure/training company) alongside his Stanford professorship, not a from-scratch CEO-only business role, which is why hands_on_engineering is scored as senior-research-leadership rather than the top anchor reserved for principal builders of shipped consumer/enterprise production infra.\n\nLiang's own work is woven into the frontier stack: SQuAD (2016/2018) is a canonical reading-comprehension dataset a generation of models trained/evaluated against, Prefix-Tuning (2021) is a named parameter-efficient-adaptation building block in the PEFT lineage frontier labs cite, Stanford Alpaca (2023) directly shaped open instruction-tuning practice, and HELM became the standard holistic LLM evaluation — documented components frontier reports build on rather than the transformer/attention/scaling primitives themselves, hence high-teens not 18-20. His language-modeling record is continuous from ~2004 (semantic parsing and structured prediction under Klein/Jordan, pre-word2vec lineage) through SQuAD, foundation models, HELM and teaching CS336 'Language Models from Scratch', ~22 years and still active, anchoring lm_domain_depth near the top. As a co-founder of Together AI (2022, ~4 years) his role is a scientific co-founder alongside dedicated technical founders (Tri Dao, Ce Zhang, Chris Ré) while his primary hat is Stanford professor/CRFM director, placing scientific_founder in the mid-band for a genuine but ~4-year, shared-technical-founder record.","evidence":[{"claim":"PhD in Computer Science, UC Berkeley (2011), advisors Dan Klein and Michael I. Jordan.","source_url":"https://en.wikipedia.org/wiki/Percy_Liang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows 167,157 total citations and h-index 143.","source_url":"https://scholar.google.com/citations?user=pouyVyUAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lead author of \"On the Opportunities and Risks of Foundation Models\" (2021), which coined/framed the term \"foundation model\"; Director of Stanford's Center for Research on Foundation Models (CRFM), which built HELM.","source_url":"https://arxiv.org/abs/2108.07258","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Together AI, an open-source/open-weight LLM training and inference infrastructure company.","source_url":"https://cs.stanford.edu/~pliang/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD UC Berkeley 2011 under Michael I. Jordan and Dan Klein; MEng MIT 2005 under Michael Collins; BS MIT 2004; Professor of CS (courtesy Statistics) at Stanford; projects include Marin and CodaLab Worksheets; teaches CS336 Language Models from Scratch","source_url":"https://cs.stanford.edu/~pliang/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar: ~167,157 citations, h-index 143, i10-index 357; top works include On the Opportunities and Risks of Foundation Models, SQuAD, Prefix-Tuning, Emergent Abilities of LLMs, Lost in the Middle, Understanding Black-box Predictions via Influence Functions, HELM, Stanford Alpaca","source_url":"https://scholar.google.com/citations?user=pouyVyUAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding director of Stanford's Center for Research on Foundation Models; NSF CAREER, PECASE, IJCAI Computers and Thought Award, Sloan Fellowship","source_url":"https://en.wikipedia.org/wiki/Percy_Liang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as a Founder of Together AI (founded 2022) alongside Vipul Ved Prakash, Ce Zhang, Chris Re and Tri Dao","source_url":"https://www.together.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prefix-Tuning: Optimizing Continuous Prompts for Generation (2021), a widely-cited parameter-efficient adaptation method for transformer LMs.","source_url":"https://doi.org/10.18653/v1/2021.acl-long.353","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"SQuAD (2016) and SQuAD 2.0 (2018) reading-comprehension datasets; lead author of 'On the Opportunities and Risks of Foundation Models' (2021); Director of Stanford CRFM (HELM, Alpaca); teaches CS336 Language Models from Scratch.","source_url":"https://cs.stanford.edu/~pliang/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as a founder of Together AI (founded 2022) alongside Vipul Ved Prakash, Ce Zhang, Chris Ré and Tri Dao.","source_url":"https://www.together.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Director of Stanford CRFM, which built HELM (Holistic Evaluation of Language Models), and lead author of the foundation-models framing paper; teaches CS336 Language Models from Scratch; NLP/LM research record continuous since Berkeley PhD (2011) and MIT (2004-05).","source_url":"https://cs.stanford.edu/~pliang/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":10},{"id":3,"slug":"john-schulman","name":"John Schulman","title":"Co-founder & Chief Scientist","company":"Thinking Machines Lab","sector":"general","profile_url":"https://en.wikipedia.org/wiki/John_Schulman","image_url":"/api/v1/ceo-ai-leaderboard/portrait/john-schulman.jpg","score":82,"tier":"deep_practitioner","dimensions":{"foundations":19,"vector_embeddings":9,"transformers_lm":19,"frontier_founder":20,"lm_domain_depth":12,"hands_on_engineering":18,"industry_impact":19,"scientific_founder":16},"rubric_version":3,"weighted_score":82,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Schulman authored the canonical reinforcement-learning optimization lineage the entire RLHF stack is built on: Trust Region Policy Optimization (ICML 2015) and Proximal Policy Optimization Algorithms (2017), plus Generalized Advantage Estimation (2016) and OpenAI Gym — these are foundational, field-defining contributions in optimization/statistical learning applied to policy learning, and PPO is the direct mechanism used to RLHF-train modern LLMs, placing him squarely in the 2020+ scaling/alignment lineage. PhD in Computer Science from UC Berkeley under Pieter Abbeel, undergraduate work at Caltech, gives strong foundations training. As an OpenAI co-founder and later head of the post-training/RLHF effort behind ChatGPT, and now chief scientist of Thinking Machines Lab, his industry impact and hands-on engineering are personally led, not managerial-only. Vector embeddings is not his direct research area (scored lower, adjacent competence via broader ML training), which is the one dimension without direct authored work found. Semantic Scholar shows 69 papers and 137,700 citations under his profile (h-index 45), consistent with a top RL researcher, though this count was not independently reconciled paper-by-paper.\n\nSchulman is a canonical frontier founder: he first-authored Proximal Policy Optimization (2017), the RL optimizer at the core of the RLHF loop that aligns GPT-, Claude- and Llama-class models, and personally led the InstructGPT (2022) and ChatGPT post-training work that defined the instruction-tuning/alignment recipe every frontier lab now descends from — a direct, named building block, not adjacent work. His language-modeling-specific record is deep but comparatively recent: his earliest canonical work (TRPO 2015, GAE, OpenAI Gym) is RL/robotics, and his continuous LM/post-training leadership runs from roughly 2019-2020 (InstructGPT precursors → ChatGPT → GPT-4) to today, ~6-7 years at the absolute tip of the spear, which sits between the 3-8yr and 8-15yr anchors. As a co-founder of OpenAI (2015-2024) who personally authored the core alignment research and now chief scientist at Thinking Machines Lab (2025-), he has ~10 years operating as a scientific/technical founder across multiple companies whose core is exactly these systems.","evidence":[{"claim":"Schulman is first author of Trust Region Policy Optimization (TRPO), ICML 2015, with Levine, Moritz, Jordan, Abbeel.","source_url":"https://arxiv.org/abs/1502.05477","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Schulman is first author of Proximal Policy Optimization Algorithms (2017), the RL optimization method used across modern RLHF pipelines.","source_url":"https://arxiv.org/abs/1707.06347","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from UC Berkeley advised by Pieter Abbeel; co-founder of OpenAI; later chief scientist at Thinking Machines Lab (after a stint at Anthropic).","source_url":"https://en.wikipedia.org/wiki/John_Schulman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar profile: 69 papers, 137,700 citations, h-index 45.","source_url":"https://www.semanticscholar.org/author/John-Schulman/47971768","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD thesis 'Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs', UC Berkeley 2016, advisor Pieter Abbeel; introduced TRPO with monotonic improvement guarantee and a general gradient-estimator calculus","source_url":"https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-217.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored Proximal Policy Optimization (2017, 30,865 citations), Trust Region Policy Optimization (2015, 8,273), High-Dimensional Continuous Control Using GAE (2015, 4,828), InstructGPT (2022, 23,966), GPT-4 Technical Report (2023, 27,061), Training Verifiers to Solve Math Word Problems (2021, 10,518","source_url":"https://api.semanticscholar.org/graph/v1/author/47971768?fields=name,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of OpenAI; joined Anthropic August 2024; joined Thinking Machines Lab February 2025 as chief scientist","source_url":"https://en.wikipedia.org/wiki/John_Schulman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records UC Berkeley doctorate with doctoral advisor Pieter Abbeel and Caltech undergraduate education","source_url":"https://www.wikidata.org/wiki/Q103236782","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Schulman first-authored Proximal Policy Optimization (2017), the RL optimization method used across modern RLHF pipelines for frontier LLMs.","source_url":"https://arxiv.org/abs/1707.06347","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Schulman is a co-author of InstructGPT ('Training language models to follow instructions with human feedback', 2022), the RLHF instruction-tuning method underlying ChatGPT and frontier alignment stacks.","source_url":"https://arxiv.org/abs/2203.02155","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of OpenAI (2015-2024); joined Anthropic Aug 2024; chief scientist at Thinking Machines Lab from Feb 2025.","source_url":"https://en.wikipedia.org/wiki/John_Schulman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Schulman first-authored Proximal Policy Optimization Algorithms (2017), the RL optimizer used across modern RLHF/LLM post-training pipelines.","source_url":"https://arxiv.org/abs/1707.06347","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Schulman co-authored InstructGPT (Training language models to follow instructions with human feedback, 2022), the RLHF instruction-tuning recipe frontier models build on.","source_url":"https://arxiv.org/abs/2203.02155","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of OpenAI (2015), led post-training/RLHF behind ChatGPT; joined Anthropic Aug 2024; chief scientist at Thinking Machines Lab from Feb 2025.","source_url":"https://en.wikipedia.org/wiki/John_Schulman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.89,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":11},{"id":17,"slug":"demis-hassabis","name":"Demis Hassabis","title":"Co-founder & CEO","company":"Google DeepMind","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","image_url":"/api/v1/ceo-ai-leaderboard/portrait/demis-hassabis.jpg","score":81,"tier":"deep_practitioner","dimensions":{"foundations":17,"vector_embeddings":14,"transformers_lm":18,"frontier_founder":16,"lm_domain_depth":10,"hands_on_engineering":18,"industry_impact":20,"scientific_founder":18},"rubric_version":3,"weighted_score":81,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Hassabis holds a PhD from University College London (2009, advisor Eleanor Maguire, thesis on the neural basis of episodic memory) and an undergraduate degree in Computer Science from Cambridge, giving him genuine formal training bridging neuroscience and computer science. He co-founded DeepMind in 2010 and personally led the research programs behind DQN ('Human-level control through deep reinforcement learning,' Nature 2015, 31,045 citations), AlphaGo ('Mastering the game of Go with deep neural networks and tree search,' Nature 2016), and AlphaFold ('Highly accurate protein structure prediction with AlphaFold,' Nature 2021, 47,191 citations) -- for which he and John Jumper won the 2024 Nobel Prize in Chemistry, a rare case of a tech CEO with a Nobel for the underlying science itself. OpenAlex shows an unambiguous match (176 works, 197,926 citations, h-index 92, i10-index 129), and he is listed as a contributor to 'Gemini: A Family of Highly Capable Multimodal Models' (2023), placing him directly in the transformer/scaling-era LM lineage as DeepMind (merged with Google Brain in 2023) became the org building Google's frontier LLMs. This is as close to the rubric's top anchor as any CEO in this batch: principal builder of systems the field runs on (AlphaFold, AlphaGo, DQN), not a business-only executive.\n\nHassabis is a listed author on 'Gemini: A Family of Highly Capable Multimodal Models' (2023) and leads Google DeepMind, the org that produced the Chinchilla scaling-law and Gopher/Sparrow work that frontier LMs directly build on — but his own named authorship in the frontier lineage is dominated by deep RL (DQN, AlphaGo/AlphaZero) and protein folding (AlphaFold), not the transformer/attention/embedding building blocks themselves, so he is a frontier-model builder more than the personal author of a named LM component. His verifiable language-modeling record is recent and leadership-weighted: DeepMind's serious LM push (~2021) through the 2023 Google Brain merger and Gemini, roughly five years, versus two decades of RL and neuroscience — deep AI expertise but a comparatively short, senior LM track. As a scientific/technical founder he is unambiguous: co-founded DeepMind in 2010 (CEO/chief scientist), personally co-authored the canonical papers the company is known for, won the 2024 Nobel Prize in Chemistry for AlphaFold, and co-founded Isomorphic Labs (2021) — ~16 years as a founder whose core is these AI systems.","evidence":[{"claim":"PhD, University College London (2009), advisor Eleanor Maguire, thesis on episodic memory","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind in 2010 with Shane Legg and Mustafa Suleyman; led AlphaGo (2016) and AlphaFold research programs","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"2024 Nobel Prize in Chemistry (with John Jumper) for AI research contributions to protein structure prediction","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cambridge CS double first 1997; PhD 2009 UCL under Eleanor Maguire on episodic memory; Bullfrog Theme Park lead programmer 1994; Lionhead lead AI programmer on Black & White 2001; DeepMind founded 2010; Nobel Prize in Chemistry 2024","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (dYpPMQEAAAAJ): ~310,736 citations, h-index 109; top works AlphaFold (2021), DQN Nature (2015), AlphaGo (2016), AlphaFold 3 (2024), EWC (2017), Gemini (2023)","source_url":"https://scholar.google.com/citations?user=dYpPMQEAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Human-level control through deep reinforcement learning', Nature 518, 2015","source_url":"https://doi.org/10.1038/nature14236","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Highly accurate protein structure prediction with AlphaFold', Nature 596, 2021","source_url":"https://doi.org/10.1038/s41586-021-03819-2","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author/contributor on 'Gemini: A Family of Highly Capable Multimodal Models' (2023), Google DeepMind's frontier LLM","source_url":"https://arxiv.org/abs/2312.11805","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind in 2010 and serves as CEO and chief scientist of Alphabet; co-founded Isomorphic Labs in 2021 as CEO","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"2024 Nobel Prize in Chemistry (with John Jumper) for AlphaFold protein-structure prediction, research he led at DeepMind","source_url":"https://www.nobelprize.org/prizes/chemistry/2024/summary/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chairman and co-founder of Google DeepMind and chief scientist of Alphabet; DeepMind builds the Gemini frontier model line","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed contributor to 'Gemini: A Family of Highly Capable Multimodal Models' (2023), a frontier LLM","source_url":"https://doi.org/10.1038/nature14236","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind in 2010 and Isomorphic Labs in 2021; 2024 Nobel Prize in Chemistry for AlphaFold protein-structure prediction","source_url":"https://en.wikipedia.org/wiki/Demis_Hassabis","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.95,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":12},{"id":4,"slug":"jared-kaplan","name":"Jared Kaplan","title":"Co-founder & Chief Science Officer","company":"Anthropic","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","image_url":"/api/v1/ceo-ai-leaderboard/portrait/jared-kaplan.jpg","score":81,"tier":"deep_practitioner","dimensions":{"foundations":18,"vector_embeddings":13,"transformers_lm":20,"frontier_founder":19,"lm_domain_depth":12,"hands_on_engineering":16,"industry_impact":19,"scientific_founder":12},"rubric_version":3,"weighted_score":81,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Kaplan holds a Harvard PhD in theoretical physics (advisor Nima Arkani-Hamed) and spent 15 years as an academic physicist before moving into AI, giving him deep, verifiable mathematical/statistical training even though it predates a formal ML degree. As lead author of 'Scaling Laws for Neural Language Models' (2020) he authored one of the canonical papers underlying the modern LM-scaling paradigm, and he is a listed co-author on GPT-3 ('Language Models are Few-Shot Learners') and Constitutional AI, placing him squarely in the transformer/scaling/alignment lineage. His Google Scholar profile (real citation count 169k+, h-index 82) is far higher than the dossier's Semantic Scholar figure, indicating the dossier undercounts him. He co-founded Anthropic and leads its research/alignment agenda, which is industry impact rooted directly in core LM research rather than pure business leadership. Vector-embeddings work specifically is thinner in the visible record (his focus is scaling laws and RLHF/Constitutional AI, not embeddings/retrieval per se), so that dimension is scored moderately on adjacency rather than direct authorship.\n\nKaplan is a first-order frontier founder: as lead author of 'Scaling Laws for Neural Language Models' (2020) he authored a named building block that every frontier lab — GPT, Claude, Gemini, Llama-class — uses to set model/data/compute allocation, and he co-authored GPT-3, Constitutional AI/RLHF and the transformer-circuits work that the frontier stack directly descends from. His language-modeling record is maximally deep but relatively short in duration — a career theoretical physicist (Harvard PhD 2009, Arkani-Hamed) who entered LM research around 2019 at OpenAI and has been continuous since (~7 years), which caps lm_domain_depth in the 8-12 band despite canonical output. As co-founder and Chief Science Officer of Anthropic (founded 2021, ~5 years) he personally sets and executes the scientific direction and authored the core research the company's models rest on, a genuine scientific/technical founder but in the 3-8-year window.","evidence":[{"claim":"Harvard PhD in physics (2009), advisor Nima Arkani-Hamed, thesis on holography","source_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lead author of 'Scaling Laws for Neural Language Models' (arXiv 2001.08361), the canonical LM scaling-laws paper","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows 169,316 citations, h-index 82, including co-authorship of 'Language Models are Few-Shot Learners' (GPT-3) and 'Constitutional AI: Harmlessness from AI Feedback'","source_url":"https://scholar.google.com/citations?user=KNr3vb4AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and Chief Science Officer of Anthropic","source_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Scaling Laws for Neural Language Models' (2020), establishing power-law scaling of loss with model size, dataset size and compute over seven orders of magnitude","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (Johns Hopkins University & Anthropic): ~169,316 citations, h-index 82, i10-index 125; top papers include GPT-3, Codex, scaling laws, Constitutional AI and RLHF","source_url":"https://scholar.google.com/citations?user=KNr3vb4AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD in Physics from Harvard (2009) advised by Nima Arkani-Hamed, thesis 'Aspects of holography'; professor at Johns Hopkins since 2012; joined OpenAI 2019; co-founder and Chief Science Officer of Anthropic","source_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'A Mathematical Framework for Transformer Circuits' (Anthropic, 2021), which decomposes attention into QK and OV circuits and identifies induction heads","source_url":"https://transformer-circuits.pub/2021/framework/index.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lead author of 'Scaling Laws for Neural Language Models' (2020), the power-law scaling result frontier labs use for model/data/compute allocation","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of GPT-3 ('Language Models are Few-Shot Learners', 2020) and Constitutional AI (2022), both direct antecedents of frontier LMs","source_url":"https://scholar.google.com/citations?user=KNr3vb4AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and Chief Science Officer of Anthropic (founded 2021), theoretical physics PhD Harvard 2009; entered AI/LM research c.2019","source_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of GPT-3 ('Language Models are Few-Shot Learners') and Constitutional AI, alignment/pretraining methods in the frontier lineage","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and Chief Science Officer of Anthropic (founded 2021); prior AI work began at OpenAI in 2019, professor at Johns Hopkins","source_url":"https://en.wikipedia.org/wiki/Jared_Kaplan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":13},{"id":18,"slug":"karen-simonyan","name":"Karén Simonyan","title":"Chief Scientist, Microsoft AI (formerly Co-founder & Chief Scientist, Inflection AI)","company":"Microsoft AI","sector":"general","profile_url":null,"image_url":null,"score":81,"tier":"deep_practitioner","dimensions":{"foundations":18,"vector_embeddings":16,"transformers_lm":18,"frontier_founder":18,"lm_domain_depth":12,"hands_on_engineering":19,"industry_impact":18,"scientific_founder":10},"rubric_version":3,"weighted_score":81,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Karén Simonyan holds a PhD in computer vision from Oxford (2013, thesis 'Large-Scale Learning of Discriminative Image Representations', advisors Andrew Zisserman and Antonio Criminisi) and, with Zisserman, co-authored VGGNet ('Very Deep Convolutional Networks for Large-Scale Image Recognition'), a canonical deep-representation-learning paper that is one of the most-cited works in computer vision. He then became a Principal Research Scientist at DeepMind, where he was a key contributor to WaveNet, AlphaZero, AlphaFold, BigGAN and Flamingo — systems central to representation learning, generative modeling and reinforcement learning, several published in Nature/Science. He co-founded Inflection AI in 2022 as Chief Scientist and moved to Microsoft AI as Chief Scientist in 2024. This is a strong researcher-builder profile: canonical authored work (VGG), principal engineering role on systems the field runs on (WaveNet, AlphaZero, AlphaFold), and leadership of frontier labs — squarely a high scorer on core dimensions, with vector_embeddings scored slightly below transformers_lm/hands_on since his direct authored contributions to the seq2seq/attention/transformer lineage specifically (vs. representation learning broadly) are less documented in the sources found.\n\nSimonyan is a co-author of 'Training Compute-Optimal Large Language Models' (Chinchilla, 2022) — the canonical scaling-law result that today's frontier LLM training runs (GPT/Claude/Gemini/Llama-class) directly build on — and of Flamingo, a foundational few-shot visual-language model, placing his own work squarely in the foundation frontier models descend from (frontier_founder high). His language-modeling-specific record is real but relatively recent: his pre-2020 work was vision (VGGNet), audio (WaveNet) and RL (AlphaZero/AlphaFold), with LM-specific contributions concentrated from ~2020-2022 (DeepMind Gopher/Chinchilla/Flamingo) through Inflection Pi and Microsoft AI — roughly 5 years of continuous LM work, not decades from the pre-word2vec era (lm_domain_depth ~11). He operated as a genuine scientific/technical founder — co-founder and Chief Scientist of Inflection AI (2022-2024), personally setting the research direction behind the Pi LLM — but for only ~2 years in that founder role before moving to Microsoft AI as (non-founder) Chief Scientist, so scientific_founder sits just below the 3-8-year band.","evidence":[{"claim":"Oxford PhD 2013 in computer vision, thesis 'Large-Scale Learning of Discriminative Image Representations', advisors Zisserman/Criminisi","source_url":"https://www.robots.ox.ac.uk/~karen/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (id L7lMQkQAAAAJ) lists him as Chief Scientist, Microsoft AI with ~315,795 citations and h-index 61; top works include VGG (165,619), WaveNet (12,082), Flamingo (9,673) and Training Compute-Optimal Large Language Models (5,421)","source_url":"https://scholar.google.com/citations?user=L7lMQkQAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG) — Karen Simonyan first author with Andrew Zisserman","source_url":"https://arxiv.org/abs/1409.1556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Training Compute-Optimal Large Language Models (Chinchilla) — Simonyan co-author","source_url":"https://arxiv.org/abs/2203.15556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Flamingo: a Visual Language Model for Few-Shot Learning — Simonyan co-author","source_url":"https://arxiv.org/abs/2204.14198","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q58492861: DPhil University of Oxford 2013, doctoral advisors Andrew Zisserman and Antonio Criminisi, employer Google DeepMind","source_url":"https://www.wikidata.org/wiki/Q58492861","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Mastering Atari, Go, chess and shogi by planning with a learned model (MuZero, Nature 2020) — Simonyan K listed among authors","source_url":"https://pubmed.ncbi.nlm.nih.gov/33361790/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Training Compute-Optimal Large Language Models (Chinchilla scaling laws) — Simonyan co-author; result built into frontier LLM training","source_url":"https://arxiv.org/abs/2203.15556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Flamingo: a Visual Language Model for Few-Shot Learning — Simonyan co-author, foundational VLM","source_url":"https://arxiv.org/abs/2204.14198","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Simonyan co-founded Inflection AI in 2022 as Chief Scientist (leaving DeepMind), then became Chief Scientist at Microsoft AI in 2024","source_url":"https://en.wikipedia.org/wiki/Inflection_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Flamingo: a Visual Language Model for Few-Shot Learning — Simonyan co-author (frontier multimodal LM lineage)","source_url":"https://arxiv.org/abs/2204.14198","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":14},{"id":9,"slug":"yang-zhilin","name":"Yang Zhilin","title":"Co-founder & CEO","company":"Moonshot AI (Kimi)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Yang_Zhilin","image_url":null,"score":81,"tier":"deep_practitioner","dimensions":{"foundations":16,"vector_embeddings":14,"transformers_lm":20,"frontier_founder":16,"lm_domain_depth":15,"hands_on_engineering":18,"industry_impact":17,"scientific_founder":14},"rubric_version":3,"weighted_score":81,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Yang Zhilin is a canonical contributor to the transformer/language-model lineage: first author of Transformer-XL (2019) and co-first-author of XLNet (2019), both widely-cited pretraining/architecture papers that predate GPT-3 and directly address long-context and permutation-based autoregressive language modeling. He holds a CMU machine-learning PhD (advisors Ruslan Salakhutdinov and William Cohen) after a Tsinghua CS undergraduate degree, and interned at Google Brain and FAIR before founding Moonshot AI in 2023, where he personally leads the Kimi model line known for very long context windows. This is a researcher-founder profile: canonical authored work plus hands-on leadership of a frontier lab producing shipped LLMs, not a fame-only executive. The dossier's OpenAlex match (id A5101639237, 'Z. Yang', wireless-communications/DTMB topics, works from 1989-2010) is clearly a different person and must be disregarded. citations note: XLNet has 10,000+ citations per secondary sources; exact h-index not independently confirmed beyond dossier's mismatched OpenAlex record.\n\nYang Zhilin is first author of Transformer-XL (segment-level recurrence + the relative positional encoding scheme widely reused in later transformer LMs) and XLNet (permutation-based autoregressive pretraining), documented pretraining/architecture components that the frontier LM lineage cites and builds on — placing him firmly in the pre-GPT-3 transformer research corpus. His continuous language-modeling record runs from his CMU ML PhD work (~2016) through Recurrent AI and now Moonshot AI's Kimi long-context models and Kimi k1.5 RL report, roughly a decade of hands-on LM research. As a founder he set and executed core technical direction at two companies whose science is his own — Recurrent AI (co-founded 2016) and Moonshot AI (co-founded 2023, ~3 years), where he personally leads the Kimi model line — the researcher-founder pattern, not a business founder with others doing the science.","evidence":[{"claim":"First author of Transformer-XL and XLNet, published before GPT-3","source_url":"https://x.com/Michaelzsguo/status/2078154407611416935","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Studied CS at Tsinghua, PhD at Carnegie Mellon, interned at Google Brain and Meta AI (FAIR)","source_url":"https://en.wikipedia.org/wiki/Yang_Zhilin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XLNet uses permutation language modeling, cited 10,000+ times; co-founded Moonshot AI in 2023 leading Kimi","source_url":"https://daily.dev/posts/yang-zhilin-and-moonshot-ai-the-researcher-behind-kimi-ufwiaatfo","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata doctoral advisors Ruslan Salakhutdinov and William W. Cohen at CMU","source_url":"https://www.wikidata.org/wiki/Q130865273","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (id 7qXxyJkAAAAJ, Carnegie Mellon) shows ~52,873 citations, h-index 42, with XLNet (18,229) and Transformer-XL (6,361) as top works","source_url":"https://scholar.google.com/citations?user=7qXxyJkAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context — Zhilin Yang is a lead author","source_url":"https://arxiv.org/abs/1901.02860","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XLNet: Generalized Autoregressive Pretraining for Language Understanding — Zhilin Yang first author","source_url":"https://arxiv.org/abs/1906.08237","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD at Carnegie Mellon (2019) under Ruslan Salakhutdinov and William Cohen; co-founded Recurrent AI 2016; worked on Huawei PanGu and BAAI Wu Dao; co-founded Moonshot AI 2023","source_url":"https://en.wikipedia.org/wiki/Yang_Zhilin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kimi k1.5: Scaling Reinforcement Learning with LLMs — Moonshot AI technical report","source_url":"https://arxiv.org/abs/2501.12599","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Transformer-XL introduced segment-level recurrence and relative positional encoding, adopted in subsequent transformer LMs; Zhilin Yang lead author","source_url":"https://arxiv.org/abs/1901.02860","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XLNet: generalized autoregressive (permutation) pretraining; Zhilin Yang first author","source_url":"https://arxiv.org/abs/1906.08237","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Recurrent AI (2016) and Moonshot AI (2023), leading the Kimi long-context LLM line","source_url":"https://en.wikipedia.org/wiki/Yang_Zhilin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kimi k1.5 technical report — Moonshot AI scaling RL with LLMs, direction led by Yang Zhilin","source_url":"https://arxiv.org/abs/2501.12599","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Transformer-XL introduced segment recurrence and relative positional encoding, a reused frontier building block; Zhilin Yang lead author","source_url":"https://arxiv.org/abs/1901.02860","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XLNet: permutation-based autoregressive pretraining, Zhilin Yang first author, 10,000+ citations","source_url":"https://arxiv.org/abs/1906.08237","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kimi k1.5 technical report — Moonshot AI, Yang Zhilin as founder leading the model line (scaling RL with LLMs)","source_url":"https://arxiv.org/abs/2501.12599","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Recurrent AI (2016) and Moonshot AI (2023); researcher-founder profile with CMU ML PhD","source_url":"https://en.wikipedia.org/wiki/Yang_Zhilin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":15},{"id":107,"slug":"llion-jones","name":"Llion Jones","title":"Co-founder & CTO","company":"Sakana AI","sector":"general","profile_url":null,"image_url":null,"score":80,"tier":"deep_practitioner","dimensions":{"foundations":13,"vector_embeddings":14,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":16,"hands_on_engineering":18,"industry_impact":16,"scientific_founder":10},"rubric_version":3,"weighted_score":80,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Llion Jones is a co-author (5th of 8) of 'Attention Is All You Need' (NeurIPS 2017), the paper that introduced the Transformer architecture underlying essentially all modern LLMs — he reportedly proposed the paper's title, which is squarely canonical, field-defining work and anchors transformers_lm at the top of the scale. He holds a BSc in Artificial Intelligence and Computer Science and an MSc in Advanced Computer Science from the University of Birmingham (completed 2009), then worked as a software engineer at YouTube/Google before moving into Google Research/Google Brain in 2015 doing machine intelligence and NLP research, personally building the sequence-to-sequence and attention-based systems that led to the Transformer. In 2023 he co-founded Sakana AI (with David Ha and Ren Ito) as CTO, where he leads a research organization personally building novel model architectures (nature-inspired/evolutionary methods) rather than merely directing others. His formal ML/math training is master's-level (not a research PhD), and while his attention/seq2seq work is deeply tied to representation learning, I found no dedicated vector-embeddings/retrieval-system publication under his name, so vector_embeddings is scored as strong-adjacent-lineage rather than top-tier authored work.\n\nJones is a co-author of 'Attention Is All You Need' (NeurIPS 2017) and, per the paper's own footnote, was responsible for the initial codebase and experimented with novel model variants — the Transformer architecture is the direct foundation every GPT/Claude/Gemini/Llama-class model is built on, so frontier_founder anchors at the top; he also co-authored Tensor2Tensor, the reference training stack the field reused. His continuous language-modeling record runs from Google Research NLP work (~2015: WikiReading 2016, One Model To Learn Them All 2017, the Transformer 2017, deeper self-attention LM 2018, Lingvo 2019, ProtTrans 2020) through Sakana AI's Transformer² self-adaptive LLMs, roughly 11 years of hands-on LM work and still active, placing lm_domain_depth in the 8–15-year band but not the pre-word2vec era. As co-founder and CTO of Sakana AI since 2023 he personally sets and executes the technical/research direction (~3 years), a verifiable technical-founder role at the low end of the 3–8-year band.","evidence":[{"claim":"Llion Jones is listed as an author (5th of 8) on 'Attention Is All You Need', arXiv:1706.03762 / NeurIPS 2017, which introduced the Transformer architecture","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jones holds a BSc in Artificial Intelligence and Computer Science and an MSc in Advanced Computer Science from the University of Birmingham, completed 2009","source_url":"https://en.wikipedia.org/wiki/Llion_Jones","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jones worked at YouTube/Google as a software engineer starting ~2011-2012, then moved into Google Research doing machine intelligence and NLP work from 2015 before co-founding Sakana AI in 2023 as CTO","source_url":"https://en.wikipedia.org/wiki/Llion_Jones","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile for Llion Jones (Sakana AI, verified sakana.ai email) shows very high citation counts driven substantially by the Transformer paper","source_url":"https://scholar.google.com/citations?user=_3_P5VwAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"DBLP lists a Llion Jones publication record including recent (2024-2025) papers on transformer/model-architecture and evaluation topics","source_url":"https://dblp.org/pers/j/Jones:Llion","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Attention Is All You Need' (2017); footnote: 'Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations.'","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv author listing shows a sustained LM/transformer record: WikiReading (2016), One Model To Learn Them All (2017), Tensor2Tensor for NMT (2018), Character-Level Language Modeling with Deeper Self-Attention (2018), Lingvo (2019), ProtTrans (2020), CodeTrans (2021), Transformer Layers as Painters (","source_url":"http://export.arxiv.org/api/query?search_query=au:%22Llion+Jones%22&start=0&max_results=30","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Welsh ML researcher; BSc in AI and computer science and MSc in advanced computer science from the University of Birmingham; at Google Research in machine intelligence/NLP from 2015; co-founded Sakana AI in 2023 as CTO with David Ha and Ren Ito.","source_url":"https://en.wikipedia.org/wiki/Llion_Jones","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sakana AI's published research line includes Evolutionary Model Merge, The AI Scientist, Transformer-squared self-adaptive LLMs and Continuous Thought Machines.","source_url":"https://sakana.ai/blog/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jones is an author of 'Attention Is All You Need' (arXiv:1706.03762, NeurIPS 2017), which introduced the Transformer; the paper's footnote credits him with the initial codebase and novel model variants","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Llion Jones is co-founder and CTO of Sakana AI (founded 2023 with David Ha and Ren Ito), where he leads model-architecture research","source_url":"https://en.wikipedia.org/wiki/Llion_Jones","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Llion Jones is an author of 'Attention Is All You Need' (arXiv:1706.03762, NeurIPS 2017) introducing the Transformer; footnote credits him with the initial codebase, novel model variants, efficient inference and visualizations","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jones co-founded Sakana AI in 2023 as CTO with David Ha and Ren Ito, leading research on novel model architectures (evolutionary model merge, Transformer², Continuous Thought Machines)","source_url":"https://en.wikipedia.org/wiki/Llion_Jones","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":16},{"id":21,"slug":"tengyu-ma","name":"Tengyu Ma","title":"Co-founder & former CEO (now MongoDB Chief AI Scientist); Assistant Professor","company":"Voyage AI (acquired by MongoDB, Feb 2025)","sector":"general","profile_url":null,"image_url":"https://unavatar.io/x/tengyuma?fallback=false","score":80,"tier":"deep_practitioner","dimensions":{"foundations":19,"vector_embeddings":20,"transformers_lm":16,"frontier_founder":14,"lm_domain_depth":15,"hands_on_engineering":16,"industry_impact":16,"scientific_founder":10},"rubric_version":3,"weighted_score":80,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Ma holds a PhD from Princeton under Sanjeev Arora on the mathematics of non-convex optimization (sparse coding, topic models, word embeddings, dynamical systems), giving deep, authored foundations in optimization/statistical learning theory. He is a Stanford assistant professor and co-author of 'A Simple but Tough-to-Beat Baseline for Sentence Embeddings' (ICLR 2017, the SIF method, ~1,051 citations) and 'A Latent Variable Model Approach to PMI-based Word Embeddings' (TACL, ~299 citations) — both are canonical, authored contributions to the vector-embeddings lineage, not adjacent work. He personally founded and led Voyage AI (2023) as CEO, a company built specifically around embedding models and retrieval, later acquired by MongoDB where he is now Chief AI Scientist — this is founder-level industry impact whose core is exactly the embeddings/vector-search space the rubric targets. His broader research spans deep learning, representation learning, and foundation models (co-author of the widely-cited 'On the Opportunities and Risks of Foundation Models' survey, ~2,279 citations), giving solid but not first-author-canonical standing in the transformers_lm dimension. OpenAlex shows 173 works, 9,501 citations, h-index 38; Semantic Scholar for the Stanford-affiliated profile shows 678 papers, 26,116 citations, h-index 73.\n\nMa's frontier lineage is real but component-level rather than a named building block: his TACL 2016 latent-variable theory of PMI/word2vec/GloVe embeddings and SIF sentence embeddings (ICLR 2017) are the analytical backbone of the representation-learning stack, and Sophia (2023) is a second-order optimizer demonstrated on GPT-class LM pretraining with ~2x speedup over Adam, plus chain-of-thought/in-context-learning expressivity theory the labs cite — documented contributions frontier training pipelines draw on, short of the transformer/scaling/RLHF core (frontier_founder 14). His language-modeling record runs continuously from the ~2015 word-embedding theory through foundation models (2021), Sophia (2023) and Voyage's production embedding/reranker models — roughly 11 years of hands-on LM/representation work, still active (lm_domain_depth 15). He is a genuine scientific founder — founder-CEO of Voyage AI (Sept 2023) who personally set the technical direction and authored the core embedding research, now MongoDB Chief AI Scientist after the Feb 2025 acquisition — but only ~3 years in that role (scientific_founder 10).","evidence":[{"claim":"PhD Princeton under Sanjeev Arora, work on non-convex optimization theory applied to sparse coding, topic models, word embeddings","source_url":"https://ai.engineer/speakers/tengyu-ma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"A Simple but Tough-to-Beat Baseline for Sentence Embeddings, ICLR 2017 (Arora, Liang, Ma) — SIF sentence embedding method","source_url":"https://dblp.org/rec/conf/iclr/AroraLM17.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Voyage AI (Sept 2023) as CEO; acquired by MongoDB Feb 2025; now MongoDB Chief AI Scientist and Stanford CS professor","source_url":"https://www.voyageai.com/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stanford CS faculty profile","source_url":"https://www.cs.stanford.edu/people/tengyu-ma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'A Latent Variable Model Approach to PMI-based Word Embeddings' (Arora, Li, Liang, Ma, Risteski; TACL 2016) — theoretical justification for PMI, word2vec and GloVe and for the linear-algebraic structure of low-dimensional semantic embeddings","source_url":"https://arxiv.org/abs/1502.03520","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Assistant Professor of Computer Science at Stanford; research areas include deep learning, pre-training / foundation models, non-convex optimization, distributed optimization and high-dimensional statistics; awards include ACM Doctoral Dissertation Award Honorable Mention (2018), COLT Best Paper (20","source_url":"https://ai.stanford.edu/~tengyuma/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of 'Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training' (Liu, Li, Hall, Liang, Ma, 2023), demonstrated on GPT models 125M-1.5B with a 2x step/compute/wall-clock speed-up over Adam","source_url":"https://arxiv.org/abs/2305.14342","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (ID i38QlUwAAAAJ, Stanford): 50,334 citations, h-index 82; recent work includes 'Chain of thought empowers transformers to solve inherently serial problems' and 'One step of gradient descent is provably the optimal in-context learner with one layer of linear self-attention'","source_url":"https://scholar.google.com/citations?user=i38QlUwAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Voyage AI builds embedding models and rerankers for retrieval-augmented generation, including general-purpose, domain-specific (finance, legal, code) and company-specific models; acquired by MongoDB, whose announcement notes the team has 'roots at Stanford, MIT, UC Berkeley, and Princeton'","source_url":"https://www.mongodb.com/company/blog/news/redefining-database-ai-why-mongodb-acquired-voyage-ai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"A Latent Variable Model Approach to PMI-based Word Embeddings (Arora, Li, Liang, Ma, Risteski; TACL 2016) — theoretical justification for PMI, word2vec and GloVe embeddings","source_url":"https://arxiv.org/abs/1502.03520","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training (Liu, Li, Hall, Liang, Ma, 2023), shown on GPT models 125M-1.5B with ~2x speedup over Adam","source_url":"https://arxiv.org/abs/2305.14342","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Voyage AI (Sept 2023) as CEO building embedding models/rerankers for RAG; acquired by MongoDB Feb 2025; now Chief AI Scientist","source_url":"https://www.mongodb.com/company/blog/news/redefining-database-ai-why-mongodb-acquired-voyage-ai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stanford faculty page: research spans deep learning, pre-training/foundation models, non-convex optimization; continuous LM/representation work","source_url":"https://ai.stanford.edu/~tengyuma/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"A Latent Variable Model Approach to PMI-based Word Embeddings (RAND-WALK), theoretical justification for word2vec/GloVe and the linear-algebraic structure of embeddings","source_url":"https://arxiv.org/abs/1502.03520","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Voyage AI (Sept 2023) as CEO building embedding/retrieval models; acquired by MongoDB Feb 2025; now MongoDB Chief AI Scientist","source_url":"https://www.voyageai.com/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":17},{"id":20,"slug":"matei-zaharia","name":"Matei Zaharia","title":"Co-founder & CTO, Databricks; Associate Professor, UC Berkeley","company":"Databricks","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Matei_Zaharia","image_url":"/api/v1/ceo-ai-leaderboard/portrait/matei-zaharia.jpg","score":79,"tier":"deep_practitioner","dimensions":{"foundations":16,"vector_embeddings":18,"transformers_lm":15,"frontier_founder":14,"lm_domain_depth":10,"hands_on_engineering":20,"industry_impact":19,"scientific_founder":16},"rubric_version":3,"weighted_score":79,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Zaharia's vector-embeddings record is personally authored and central, not supervisory: ColBERT (arXiv:2004.12832, SIGIR 2020) is a two-author paper — Omar Khattab and Matei Zaharia — introducing late-interaction contextualized retrieval, one of the reference architectures for dense passage search, verified at 3,143 citations on his Scholar profile and followed by ColBERTv2 and PLAID. That single verified fact is what separates the two passes, and it places him near the canonical anchor on the dimension the rubric names explicitly (dense retrieval, vector search). His transformer/LM record is training-systems rather than architecture, but it is real and authored: he is the final author of 'Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM' (arXiv:2104.04473, 1,875 citations) and the 12th of 13 authors on DSPy (arXiv:2310.03714) for compiling declarative LM pipelines — built LM training and programming infrastructure, not attention or pretraining research of his own, so that dimension stays mid-high. He created Apache Spark at Berkeley's AMPLab, with a 2013 PhD under Ion Stoica and Scott Shenker that won the 2014 ACM Doctoral Dissertation Award and the 2025 ACM Prize in Computing; his verified Google Scholar record is 116,578 citations, h-index 102, i10-index 257. Foundations is the one dimension both passes agreed on and is correctly mid-high: his mathematics is applied within distributed-systems and retrieval work rather than being the contribution itself.\n\nZaharia's frontier lineage is documented-component, not architecture: he is the final author of 'Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM' (2021), a pipeline/tensor-parallel training method the training stacks behind frontier LMs draw on, and senior/co-author of ColBERT (SIGIR 2020) late-interaction dense retrieval, a reference method for the RAG layer around frontier models — real building blocks the labs cite, but he authored no attention, pretraining objective or scaling law, so he sits mid-high not canonical. His language-modeling-specific record is continuous from ~2020 (ColBERT → Megatron-LM training → DSPy 2023), roughly six years, preceded by a decade of distributed-systems work (Spark) that is adjacent infrastructure rather than LM research. He has been the technical co-founder and CTO/Chief Technologist of Databricks since 2013 (~13 years), a company whose core is the data-and-AI systems he personally authored (Apache Spark, MLflow), earning a strong scientific-founder score, though Databricks' core is data/analytics infrastructure broadly rather than language modeling specifically.","evidence":[{"claim":"One of only two authors — Omar Khattab and Matei Zaharia — of 'ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT', SIGIR 2020, introducing late interaction for dense passage retrieval at ~100x lower query cost than prior BERT rankers","source_url":"https://arxiv.org/abs/2004.12832","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Final author of 'Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM' (2021) with Narayanan, Shoeybi, Casper, Catanzaro and Phanishayee","source_url":"https://arxiv.org/abs/2104.04473","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar I1EvjZsAAAAJ (UC Berkeley and Databricks): 116,578 citations, h-index 102, i10-index 257; top works include Spark (13,296), foundation models report (12,333), ColBERT (3,143), MLlib (2,654), Megatron-LM LM training (1,875), PipeDream (1,586)","source_url":"https://scholar.google.com/citations?user=I1EvjZsAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"12th of 13 authors on 'DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines' (2023)","source_url":"https://arxiv.org/abs/2310.03714","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Resilient Distributed Datasets' (NSDI 2012, Best Paper) and 'Spark: Cluster Computing with Working Sets' (2010); PhD Berkeley under Ion Stoica and Scott Shenker, 2014 ACM Doctoral Dissertation Award","source_url":"https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/zaharia","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Computer Science, UC Berkeley (2007-2013), advisors Ion Stoica and Scott Shenker; dissertation on Spark/RDDs won the 2014 ACM Doctoral Dissertation Award","source_url":"https://www.csail.mit.edu/news/matei-zaharia-receives-acm-doctoral-dissertation-award","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing' (NSDI 2012, Best Paper Award) and 'Spark: Cluster Computing with Working Sets' (2010) — the foundational Apache Spark papers","source_url":"https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/zaharia","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (with Omar Khattab) of 'ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT' (SIGIR 2020), a dense/late-interaction retrieval architecture","source_url":"https://arxiv.org/abs/2004.12832","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior co-author of 'DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines' (NeurIPS 2023)","source_url":"https://arxiv.org/abs/2310.03714","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior/co-author (with Omar Khattab) of 'ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT' (SIGIR 2020), a reference dense-retrieval architecture for the RAG layer around frontier models","source_url":"https://arxiv.org/abs/2004.12832","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CTO of Databricks (founded 2013); creator of Apache Spark, the core system the company runs on","source_url":"https://en.wikipedia.org/wiki/Matei_Zaharia","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior (2-author) author of ColBERT (SIGIR 2020) — late-interaction dense passage retrieval, a reference architecture in the RAG/retrieval lineage frontier systems use","source_url":"https://arxiv.org/abs/2004.12832","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.93,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":18},{"id":8,"slug":"arthur-mensch","name":"Arthur Mensch","title":"Co-founder & CEO","company":"Mistral AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Arthur_Mensch","image_url":"/api/v1/ceo-ai-leaderboard/portrait/arthur-mensch.png","score":77,"tier":"deep_practitioner","dimensions":{"foundations":17,"vector_embeddings":14,"transformers_lm":18,"frontier_founder":18,"lm_domain_depth":12,"hands_on_engineering":16,"industry_impact":16,"scientific_founder":12},"rubric_version":3,"weighted_score":77,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Mensch completed a PhD (2015-2018, Inria/NeuroSpin CEA-Saclay, advisors Bertrand Thirion, Gael Varoquaux, Julien Mairal) on stochastic optimization and dictionary learning for large-scale matrix factorization, publishing 'Dictionary Learning for Massive Matrix Factorization' at ICML 2016 and a 2018 IEEE Trans. Signal Processing follow-up -- genuine applied-math/optimization foundations work, applied to fMRI representation learning rather than text embeddings specifically. He then did a postdoc on optimal transport/stochastic optimization at ENS Paris (2018-2020) before joining Google DeepMind Paris (2020-2023) as a Senior Research Scientist contributing to Flamingo, Gemini, LM scaling and retrieval-augmented generation -- direct hands-on transformer/LM-lineage work at a top lab. He co-founded Mistral AI in 2023 and is a named co-author on both the Mistral 7B and Mixtral-of-Experts technical reports, i.e. personally involved in shipping widely-used open-weight transformer models, not merely a business-side founder. Semantic Scholar record (32 papers, ~19k citations, h-index 14, id 1697879) is consistent with this profile; a PubMed 'Mensch A' hit-set is a clinical homonym and was excluded. Overall: strong PhD-level optimization/math foundations, direct pre-founding LM/transformer research experience at DeepMind, and continued technical authorship post-founding -- a researcher-founder profile, not a pure business CEO.\n\nMensch's own work is directly in the frontier lineage: he is a named co-author on 'Training Compute-Optimal Large Language Models' (Chinchilla, 2022), the scaling-law result frontier labs cite and train against, and on Flamingo, RETRO and Gemini at DeepMind, then co-authored the Mistral 7B (grouped-query + sliding-window attention) and Mixtral-of-Experts (sparse MoE) technical reports that today's open-weight frontier ecosystem builds on. His verifiable language-modeling record is specifically the DeepMind-through-Mistral period, roughly 2020–2026 (~6 years, continuous and at the tip of the spear), rather than his 2015–2018 PhD, which was optimization/matrix-factorization for fMRI, not LMs. As founder-CEO of Mistral AI since May 2023 (~3 years) he is a genuine scientific/technical founder — personally named on the core Mistral/Mixtral model papers — but the duration in that role is still short.","evidence":[{"claim":"PhD 2015-2018 at Inria/NeuroSpin on stochastic optimization and representation learning for fMRI, advisors Thirion/Varoquaux/Mairal","source_url":"https://team.inria.fr/parietal/team-members/arthur-mensch/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Dictionary Learning for Massive Matrix Factorization' (ICML 2016), scalable optimization/matrix-factorization work","source_url":"https://arxiv.org/abs/1605.00937","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior Research Scientist at Google DeepMind Paris 2020-2023, contributed to Flamingo/Gemini, LM scaling, retrieval-augmented generation","source_url":"https://en.wikipedia.org/wiki/Arthur_Mensch","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named co-author on the Mistral 7B technical report","source_url":"https://arxiv.org/abs/2310.06825","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named co-author on the Mixtral of Experts technical report","source_url":"https://arxiv.org/abs/2401.04088","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD 2015-2018 Inria/NeuroSpin on stochastic optimization for large-scale fMRI; advisors Thirion, Varoquaux, Mairal; ENS postdoc on optimal transport; DeepMind Paris late 2020-May 2023; Mistral AI co-founder/CEO May 2023-","source_url":"https://en.wikipedia.org/wiki/Arthur_Mensch","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Third author of 'Training Compute-Optimal Large Language Models' (Chinchilla scaling laws), 29 March 2022","source_url":"https://arxiv.org/abs/2203.15556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Third author of 'Mistral 7B' (grouped-query + sliding-window attention), 10 Oct 2023","source_url":"https://arxiv.org/abs/2310.06825","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (ID F8riAN8AAAAJ): ~53,164 citations, h-index 33; top works Mistral 7B, Gemini, Flamingo, Chinchilla, Mixtral, RETRO, Gopher, 'Differentiable dynamic programming for structured prediction and attention'","source_url":"https://scholar.google.com/citations?user=F8riAN8AAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Training Compute-Optimal Large Language Models' (Chinchilla scaling laws), a result frontier model training directly descends from","source_url":"https://arxiv.org/abs/2203.15556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of the Mistral 7B technical report (grouped-query + sliding-window attention), a frontier-class open-weight model","source_url":"https://arxiv.org/abs/2310.06825","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of the Mixtral of Experts (sparse MoE) technical report","source_url":"https://arxiv.org/abs/2401.04088","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Mistral AI since 2023, prior Senior Research Scientist at Google DeepMind Paris on LM work (Flamingo/Gemini/RAG)","source_url":"https://en.wikipedia.org/wiki/Arthur_Mensch","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author on 'Training Compute-Optimal Large Language Models' (Chinchilla scaling laws), the scaling result frontier labs train against","source_url":"https://arxiv.org/abs/2203.15556","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on Mistral 7B (grouped-query + sliding-window attention), a frontier-class open-weight model","source_url":"https://arxiv.org/abs/2310.06825","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on Mixtral of Experts (sparse MoE), continuing frontier-model authorship under the Mistral affiliation","source_url":"https://arxiv.org/abs/2401.04088","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Mistral AI since May 2023; DeepMind LM research (Flamingo/Gemini/RAG) 2020-2023","source_url":"https://en.wikipedia.org/wiki/Arthur_Mensch","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":19},{"id":13,"slug":"chris-olah","name":"Chris Olah","title":"Co-founder, Interpretability Lead","company":"Anthropic","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Chris_Olah","image_url":"/api/v1/ceo-ai-leaderboard/portrait/chris-olah.jpg","score":77,"tier":"deep_practitioner","dimensions":{"foundations":14,"vector_embeddings":16,"transformers_lm":19,"frontier_founder":15,"lm_domain_depth":11,"hands_on_engineering":18,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":77,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Olah has no formal PhD (he is largely self-taught, coming up through Google Brain and OpenAI as an engineer-researcher), so foundations is scored on strong demonstrated mathematical/statistical work rather than credentials. He personally coined the term 'mechanistic interpretability' and is the driving author behind the Circuits research program ('Zoom In: An Introduction to Circuits', 'A Mathematical Framework for Transformer Circuits', 'Toy Models of Superposition') which directly analyzes the internal vector representations and attention/transformer mechanisms of language models — squarely in the transformer/embeddings lineage and original, field-defining work, not derivative commentary. He was a lead engineer/researcher on TensorFlow at Google and co-authored Anthropic's RLHF paper, showing hands-on system-building alongside research. OpenAlex confirms 34 works, 14,840 citations, h-index 20; his real profile shows the interpretability line running from 2015 (Google Brain, DeepDream/feature visualization era) through today. He co-founded Anthropic, making his industry impact directly downstream of his own technical research rather than business-only leadership.\n\nOlah's clearest frontier lineage is co-authorship of Anthropic's 'Training a Helpful and Harmless Assistant with RLHF' (2022) — a named alignment technique the Claude-class models descend from — plus his transformer-circuits work (induction heads, residual stream) that frontier labs cite; his interpretability output is largely downstream analysis of models rather than a load-bearing architectural building block, so this lands in the documented-component band, not the architecture-author band. His language-modeling-specific record is deep but short and recent: the early years (2015–2019 feature visualization, DeepDream, InceptionV1 circuits, TensorFlow) were vision/tooling, with continuous LM research (transformer circuits, induction heads, RLHF) running only ~2020/21–2026, roughly 5–6 years. He is a genuine scientific/technical co-founder of Anthropic (2021–present, ~5 years), personally authoring and leading the interpretability research the company runs on — a real technical founder, though for a shorter tenure than the 8-15-year band.","evidence":[{"claim":"Coined 'mechanistic interpretability' and lead author on Anthropic's Circuits research (A Mathematical Framework for Transformer Circuits, In-context Learning and Induction Heads, Toy Models of Superposition)","source_url":"https://en.wikipedia.org/wiki/Mechanistic_interpretability","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Anthropic; previously machine learning researcher at Google Brain and OpenAI","source_url":"https://en.wikipedia.org/wiki/Chris_Olah","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback' (2022)","source_url":"https://doi.org/10.48550/arxiv.2204.05862","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of 'A Mathematical Framework for Transformer Circuits' (2021): QK/OV circuit decomposition, residual stream, induction heads","source_url":"https://transformer-circuits.pub/2021/framework/index.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (Anthropic): ~124,670 citations, h-index 55, i10-index 81; includes TensorFlow, Concrete Problems in AI Safety, Feature Visualization, Understanding LSTM Networks, Deconvolution and Checkerboard Artifacts","source_url":"https://scholar.google.com/citations?user=6dskOSUAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Studied mathematics at Toronto for one year before leaving at 18; Thiel Fellow 2012; Google Brain 2015-2018, co-founded Distill 2017, led OpenAI interpretability 2018-2020, co-founded Anthropic 2021","source_url":"https://en.wikipedia.org/wiki/Chris_Olah","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored the Distill circuits thread including 'Zoom In: An Introduction to Circuits' (2020) and 'Feature Visualization' (2017), peer-reviewed in Distill","source_url":"https://doi.org/10.23915/distill.00007","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of Anthropic's HH-RLHF paper (2022), an alignment technique frontier Claude-class models build on","source_url":"https://doi.org/10.48550/arxiv.2204.05862","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author, 'A Mathematical Framework for Transformer Circuits' (2021) — induction heads / residual stream, cited by frontier interpretability work","source_url":"https://transformer-circuits.pub/2021/framework/index.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Anthropic (2021), leading its mechanistic interpretability research","source_url":"https://en.wikipedia.org/wiki/Chris_Olah","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of 'A Mathematical Framework for Transformer Circuits' (2021) — QK/OV decomposition, residual stream, induction heads — analyzing the transformer machinery frontier LMs use; his LM-specific record runs ~2020–present","source_url":"https://transformer-circuits.pub/2021/framework/index.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Anthropic (2021), operating as a scientific/technical founder leading mechanistic interpretability research; earlier co-founded Distill (2017)","source_url":"https://en.wikipedia.org/wiki/Chris_Olah","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.86,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":20},{"id":12,"slug":"andrej-karpathy","name":"Andrej Karpathy","title":"Founder","company":"Eureka Labs","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","image_url":"/api/v1/ceo-ai-leaderboard/portrait/andrej-karpathy.png","score":76,"tier":"deep_practitioner","dimensions":{"foundations":16,"vector_embeddings":14,"transformers_lm":18,"frontier_founder":13,"lm_domain_depth":14,"hands_on_engineering":20,"industry_impact":18,"scientific_founder":8},"rubric_version":3,"weighted_score":76,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Karpathy earned a PhD in computer science at Stanford (2011-2015) advised by Fei-Fei Li on the intersection of computer vision and NLP (dissertation: Connecting Images and Natural Language), with widely cited work (ImageNet Large Scale Visual Recognition Challenge, Deep Visual-Semantic Alignments for Generating Image Descriptions, Large-Scale Video Classification with CNNs) that is directly in the representation-learning / vector-embedding lineage (joint image-text embedding spaces) though centered on vision rather than pure text embeddings. He is an OpenAI founding member, led Tesla Autopilot/AI, wrote and taught Stanford's CS231n, and personally authored influential minimal/from-scratch implementations of language models (char-rnn, minGPT, nanoGPT, llm.c) that are widely used pedagogical and practical references in the transformer/LM lineage — this is genuine, personal, hands-on engineering of the systems the field runs on, not managerial credit. OpenAlex shows 25 works, 59,127 citations, h-index 20; other aggregators (ResearchGate/SciSpace) suggest wider citation counts in the tens of thousands to 80k+ depending on source, consistent with major impact. Foundations (core math/optimization theory) is strong graduate-level but not his primary authored contribution, so scored just below the top anchor.\n\nKarpathy's OWN canonical research is computer vision and joint image-text embedding (ImageNet challenge, image-captioning RNNs) rather than a named building block frontier LMs cite — he did not author the transformer, attention, word2vec, scaling laws or RLHF — so his frontier lineage is real but indirect: char-rnn plus the reference GPT-training implementations (minGPT/nanoGPT/llm.c/nanochat) that shaped how the field trains and teaches these models, and a founding-member role at OpenAI followed by joining Anthropic's pretraining team in 2026. His verifiable language-modeling record runs from ~2014-2015 (image-captioning language models, char-rnn 'Unreasonable Effectiveness of RNNs') through a vision-focused Tesla interlude (2017-2022) to heavy LLM work 2023+, roughly a decade of depth but not fully continuous. As a technical founder he personally writes the core code/curriculum of Eureka Labs (2024-present, ~2 years) and was a founding member (research scientist) of OpenAI in 2015, though OpenAI's science was shared across a large technical team rather than led by him alone.","evidence":[{"claim":"PhD Stanford (2011-2015), advisor Fei-Fei Li, dissertation Connecting Images and Natural Language.","source_url":"https://cs.stanford.edu/people/karpathy/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of char-rnn, a minimal character-level RNN language model, and later minGPT/nanoGPT, minimal from-scratch GPT implementations.","source_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding member of OpenAI, Director of AI at Tesla (Autopilot), founder of Eureka Labs (2024), joined Anthropic pretraining team in 2026 per Wikipedia.","source_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Stanford 2015 under Fei-Fei Li, dissertation 'Connecting Images and Natural Language'; BSc Toronto 2009, MSc UBC 2011; OpenAI founding member 2015-2017 and 2023-2024; Tesla Director of AI and Autopilot Vision 2017-2022; founded Eureka Labs July 2024; joined Anthropic May 2026 to lead pretraining","source_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar: 80,896 citations; first-author 'Deep visual-semantic alignments for generating image descriptions' (2015, 8,314), 'Large-scale video classification with convolutional neural networks' (2014, 9,447), 'Visualizing and Understanding Recurrent Networks' (2015, 1,645); co-author ImageNet","source_url":"https://scholar.google.com/citations?user=l8WuQJgAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Personally authored open-source LLM training implementations: nanoGPT (62,995 stars), nanochat (57,949), llm.c 'LLM training in simple, raw C/CUDA' (30,975), minGPT (24,879), nn-zero-to-hero (24,343), LLM101n (37,499)","source_url":"https://github.com/karpathy?tab=repositories&sort=stargazers","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex records 25 works, 59,127 citations, h-index 20, earliest publication year 2011, Stanford affiliation","source_url":"https://api.openalex.org/authors/A5009290031","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored char-rnn, minGPT, nanoGPT, llm.c and nn-zero-to-hero — widely used minimal from-scratch neural-LM and GPT training implementations","source_url":"https://github.com/karpathy?tab=repositories&sort=stargazers","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding member of OpenAI (2015-2017), Director of AI at Tesla (2017-2022), founded Eureka Labs (2024), joined Anthropic pretraining team in 2026","source_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Stanford under Fei-Fei Li on connecting images and natural language; early LM-lineage work incl. Deep Visual-Semantic Alignments and Visualizing/Understanding Recurrent Networks (2015)","source_url":"https://cs.stanford.edu/people/karpathy/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of char-rnn, minGPT, nanoGPT and llm.c ('LLM training in simple, raw C/CUDA') — personal from-scratch language-model training implementations widely used as references.","source_url":"https://github.com/karpathy?tab=repositories&sort=stargazers","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding member of OpenAI (2015-2017, 2023-2024) and joined Anthropic's pretraining team in 2026; founded Eureka Labs (AI education) in July 2024.","source_url":"https://en.wikipedia.org/wiki/Andrej_Karpathy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Stanford under Fei-Fei Li on connecting images and natural language; RNN-based image-caption generation (Deep Visual-Semantic Alignments) and Visualizing/Understanding Recurrent Networks place his LM-lineage start around 2014-2015.","source_url":"https://cs.stanford.edu/people/karpathy/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":21},{"id":19,"slug":"thomas-wolf","name":"Thomas Wolf","title":"Co-founder & Chief Science Officer","company":"Hugging Face","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/thomas-wolf.jpg","score":76,"tier":"deep_practitioner","dimensions":{"foundations":11,"vector_embeddings":14,"transformers_lm":18,"frontier_founder":16,"lm_domain_depth":12,"hands_on_engineering":19,"industry_impact":19,"scientific_founder":15},"rubric_version":3,"weighted_score":76,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Wolf is the verified first author of 'HuggingFace's Transformers: State-of-the-Art Natural Language Processing' (arXiv:1910.03771, EMNLP 2020 demos, 25,160 citations), the library that is the de facto implementation layer of the transformer lineage — principal-builder status rather than adjacency. The fact that separates the two passes is DistilBERT (arXiv:1910.01108), where the verified author order is Sanh, Debut, Chaumond, Wolf: he is the senior author of a knowledge-distillation result that is itself language-modeling research (14,772 citations), which pass_1 missed entirely and which lifts his transformers_lm above a pure-tooling reading. He is also an author of BLOOM (176B open multilingual LM), StarCoder, T0 multitask prompted training and Zephyr alignment distillation, and his teams shipped transformers, datasets, tokenizers and the Hub. His verified Google Scholar profile (Co-founder at Hugging Face) shows 65,616 citations and h-index 52, essentially all in the 2019+ transformer window, so depth of experience in this lineage is about seven years rather than the dossier's spurious 58. Foundations is his weakest dimension and neither pass could verify it: no degree, thesis or authored work in linear algebra, optimization or statistical learning could be confirmed from a primary source — his Scholar profile lists a polytechnique.edu contact and his own site returned HTTP 403 — so under the rubric's 'if unsure, score lower' instruction this is scored on the published record alone, which contains no foundations paper.\n\nWolf is the first author of the Transformers library paper and senior author of DistilBERT, and Hugging Face's transformers/datasets/tokenizers stack plus its open frontier-scale LMs (BLOOM 176B, StarCoder) are the training/inference tooling and datasets that the open-weight frontier (Llama-class, BLOOM, open instruction/alignment work) demonstrably build on — knowledge distillation (DistilBERT) is a named, widely-cited component rather than the transformer architecture itself, so this is high-teens-adjacent but not an architecture/scaling-law author (15). His verifiable, continuous language-modeling record runs from Hugging Face's NLP work (~2018-2019, transfer-learning/conversational-AI and the pytorch-transformers library) to the present — roughly 7-8 years of deep, still-active LM systems work, strong depth but short of the 8-15-year band's midpoint (13). As co-founder and Chief Science Officer of Hugging Face since 2016 he is an unambiguous scientific/technical founder who personally authored the core research and code the company runs on, ~10 years in that role (15).","evidence":[{"claim":"First author of 'HuggingFace's Transformers: State-of-the-art Natural Language Processing' (2019/EMNLP 2020 demos), with Lysandre Debut, Victor Sanh, Julien Chaumond and Clement Delangue","source_url":"https://arxiv.org/abs/1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior (final) author of 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter' (2019); verified author order Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf","source_url":"https://arxiv.org/abs/1910.01108","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar D2H5EFEAAAAJ (Co-founder at Hugging Face, contact polytechnique.edu): 65,616 citations, h-index 52, i10-index 76; top works Transformers (25,160), DistilBERT (14,772), T0 multitask prompted training (2,717), BLOOM (2,580), StarCoder (2,250), Zephyr (1,095), Datasets (997)","source_url":"https://scholar.google.com/citations?user=D2H5EFEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on the BLOOM 176B-parameter open multilingual language model (BigScience Workshop, 2022)","source_url":"https://arxiv.org/abs/2211.05100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hugging Face profile thomwolf lists NLP and open source, with membership of BigScience, BigCode, LeRobot and Open LLM Leaderboard organizations; it states no degrees, so education remains unverified","source_url":"https://huggingface.co/thomwolf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thomas Wolf is first author of the Transformers library paper, EMNLP 2020 demo track, ~8,300+ citations","source_url":"https://arxiv.org/abs/1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thomas Wolf co-founded Hugging Face in 2016 with Clément Delangue and Julien Chaumond, and serves as Chief Science Officer / Chief Strategy Officer","source_url":"https://en.wikipedia.org/wiki/Hugging_Face","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thomas Wolf is a listed author on the BLOOM 176B-parameter open multilingual language model paper (BigScience Workshop, 2022)","source_url":"https://arxiv.org/abs/2211.05100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thomas Wolf's Hugging Face profile confirms his role in NLP/open-source and involvement in BigScience and core Hugging Face teams","source_url":"https://huggingface.co/thomwolf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (Co-founder at HuggingFace): 65,616 citations, h-index 52; top works Transformers (2020, 25,160 cites), DistilBERT (2019, 14,772), BLOOM (2022), StarCoder (2023), Zephyr (2023), Datasets (2021)","source_url":"https://scholar.google.com/citations?user=D2H5EFEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'HuggingFace's Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demos), the de facto training/inference implementation layer of the transformer lineage","source_url":"https://arxiv.org/abs/1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior (final) author of DistilBERT, a knowledge-distillation method for language models that the frontier ecosystem cites and reuses","source_url":"https://arxiv.org/abs/1910.01108","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on BLOOM, the 176B-parameter open-access multilingual language model (BigScience, 2022)","source_url":"https://arxiv.org/abs/2211.05100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Hugging Face in 2016 with Clément Delangue and Julien Chaumond and serves as Chief Science Officer, setting and executing technical direction","source_url":"https://en.wikipedia.org/wiki/Hugging_Face","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demos), the de facto training/inference implementation layer of the transformer lineage","source_url":"https://arxiv.org/abs/1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of DistilBERT, a knowledge-distillation method reused across the frontier open-model stack","source_url":"https://arxiv.org/abs/1910.01108","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on BLOOM, a 176B-parameter open multilingual language model (BigScience, 2022)","source_url":"https://arxiv.org/abs/2211.05100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":22},{"id":11,"slug":"wojciech-zaremba","name":"Wojciech Zaremba","title":"Co-founder; Head of AI Resilience","company":"OpenAI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","image_url":"/api/v1/ceo-ai-leaderboard/portrait/wojciech-zaremba.jpg","score":76,"tier":"deep_practitioner","dimensions":{"foundations":17,"vector_embeddings":9,"transformers_lm":17,"frontier_founder":15,"lm_domain_depth":13,"hands_on_engineering":18,"industry_impact":18,"scientific_founder":16},"rubric_version":3,"weighted_score":76,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Zaremba holds a 2016 NYU PhD under Yann LeCun and Rob Fergus ('Learning Algorithms from Data') after mathematics and computer science degrees at the University of Warsaw and study at Ecole Polytechnique, and his authored record is squarely in the sequence-modeling half of the lineage that precedes transformers. The fact that decides the transformers_lm dispute is 'Recurrent Neural Network Regularization' (arXiv:1409.2329, 8 September 2014), which the verified arXiv record shows he FIRST-authored with Ilya Sutskever and Oriol Vinyals — the standard reference for applying dropout to LSTMs, evaluated on language modeling and machine translation. That is authored language-modeling research, not the applied-engineering-only record pass_1 described. He also first-authored 'Learning to Execute' (2014) and co-authored 'Intriguing Properties of Neural Networks' (2013, 23,150 citations), 'Spectral Networks and Locally Connected Networks on Graphs' (2013), OpenAI Gym, Codex (arXiv:2107.03374) and the GPT-4 Technical Report. His verified Google Scholar profile shows 146,695 citations, h-index 48, i10 66. He co-founded OpenAI in 2015, personally led the robotics work (Dactyl, domain randomization, Hindsight Experience Replay) and after 2020 led the GPT/Codex/Copilot teams — a genuine built-systems record. Vector embeddings remains his weakest dimension: the graph-spectral-networks work touches representation learning but he has authored no embedding, contrastive or retrieval contribution.\n\nAs an OpenAI co-founder (2015) who first led robotics (2015–2020) and then led the GPT-model, Codex and GitHub Copilot teams (2020–present), Zaremba's own work sits inside the frontier stack rather than merely upstream of it: Codex (arXiv:2107.03374) established the code-LLM line that current frontier assistants descend from, and he co-authored the GPT-4 Technical Report, while his named methodological contributions ('Recurrent Neural Network Regularization', first-authored with Sutskever and Vinyals, and 'Learning to Execute', both 2014) are pre-transformer RNN/seq2seq lineage — hence a strong-but-not-canonical frontier_founder score. His verifiable language-modeling record begins in 2014 with LSTM regularization for LM/MT and resumes with GPT/Codex leadership from 2020, but a roughly five-year robotics detour (Dactyl, HER) interrupts continuity, so the ~12-year span is not a fully continuous LM record (13). He has operated as a genuine scientific/technical co-founder — not a business founder with others doing the science — for about 11 years (2015–2026), personally authoring core research and leading the teams that ship OpenAI's core systems.","evidence":[{"claim":"First author of 'Recurrent Neural Network Regularization' (submitted 8 September 2014) with Ilya Sutskever and Oriol Vinyals, applying dropout to LSTMs for language modeling and machine translation","source_url":"https://arxiv.org/abs/1409.2329","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar XCZpOcAAAAAJ (Head of AI Resilience, OpenAI): 146,695 citations, h-index 48, i10-index 66; top works GPT-4 Technical Report (27,861), Intriguing Properties of Neural Networks (23,150), Improved Techniques for Training GANs (14,463), Codex (11,520), OpenAI Gym (10,957), Spectral Networ","source_url":"https://scholar.google.com/citations?user=XCZpOcAAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD NYU 2016 under Yann LeCun and Rob Fergus, dissertation 'Learning Algorithms from Data'; OpenAI co-founder 2015; led robotics (Rubik's-cube hand) then GPT/Codex/GitHub Copilot teams","source_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Learning to Execute' (2014) on neural networks learning to execute programs","source_url":"https://arxiv.org/abs/1410.4615","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author on 'Evaluating Large Language Models Trained on Code' (Codex, 2021)","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD in deep learning, New York University, 2016, dissertation 'Learning Algorithms from Data', advised by Yann LeCun and Rob Fergus","source_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of OpenAI (2015); led robotics research (2015-2020) including a robotic arm/hand solving a Rubik's Cube; led GPT model, GitHub Copilot and Codex teams from 2020","source_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Learning to Execute' (2014) on neural networks learning to execute simple programs","source_url":"https://arxiv.org/abs/1410.4615","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Reinforcement Learning Neural Turing Machines' (2015)","source_url":"https://arxiv.org/abs/1505.00521","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Intriguing Properties of Neural Networks' (Szegedy et al. 2013), foundational adversarial-examples paper; Google Scholar profile shows 146,695 citations, h-index 48, i10-index 66","source_url":"https://scholar.google.com/citations?user=XCZpOcAAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Recurrent Neural Network Regularization' (2014) with Ilya Sutskever and Oriol Vinyals — dropout for LSTMs evaluated on language modeling and machine translation, the pre-transformer LM lineage frontier models build on","source_url":"https://arxiv.org/abs/1409.2329","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI co-founder (2015/2016); led robotics 2015–2020 (Rubik's-cube robotic hand) then led GPT models, GitHub Copilot and Codex teams from 2020 — a founder personally setting and executing technical direction","source_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Recurrent Neural Network Regularization' (Sept 2014, with Sutskever and Vinyals), applying dropout to LSTMs for language modeling and machine translation — pre-transformer LM lineage","source_url":"https://arxiv.org/abs/1409.2329","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI co-founder (2015/2016); led robotics 2015–2020, then led GPT models, GitHub Copilot and Codex teams from 2020 — operating as a scientific/technical founder of a company whose core is these systems","source_url":"https://en.wikipedia.org/wiki/Wojciech_Zaremba","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on 'Evaluating Large Language Models Trained on Code' (Codex, 2021), the model underpinning GitHub Copilot and a direct frontier code-model ancestor","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":23},{"id":26,"slug":"ali-farhadi","name":"Ali Farhadi","title":"Professor, Allen School of CSE, University of Washington; CEO of the Allen Institute for AI (Ai2) 2023-2026","company":"Allen Institute for AI (Ai2)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","image_url":"/api/v1/ceo-ai-leaderboard/portrait/ali-farhadi.jpg","score":74,"tier":"deep_practitioner","dimensions":{"foundations":16,"vector_embeddings":15,"transformers_lm":14,"frontier_founder":14,"lm_domain_depth":11,"hands_on_engineering":19,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":74,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The two passes diverged because pass_1 scored Farhadi from the dossier's OpenAlex record (276 works, 52,784 citations, h-index 58) and concluded his work was vision-only with no embedding or LM record. His verified Google Scholar profile refutes both halves: 230,069 citations, h-index 100, i10-index 221, and the top-15 list contains 'Unsupervised Deep Embedding for Clustering Analysis' (ICML 2016, 5,065 citations — authored representation/embedding-space research), 'Bidirectional Attention Flow for Machine Comprehension' (ICLR 2017, 2,570 — genuine pre-transformer attention-architecture work for language), 'HellaSwag' (ACL 2019, 5,175), 'Defending Against Neural Fake News' / Grover (1,827), 'Model soups' (ICML 2022, 2,127) and 'Editing models with task arithmetic' (1,756), the last two operating directly in model weight space. His hands-on record is the strongest dimension: YOLO (81,998 citations), YOLOv3 (42,656), YOLO9000 (29,619) and XNOR-Net (6,826), the last being first-principles numerical work binarising weights and activations so convolution reduces to XNOR/popcount — the low-precision arithmetic modern inference stacks depend on. A UIUC PhD under David Forsyth and this body of quantization, clustering-objective and weight-space work support strong foundations. On the LM axis he is a listed author of '2 OLMo 2 Furious' (arXiv:2501.00656, verified) but not of the original OLMo paper, so his open-LLM credit is partly organizational as Ai2 CEO; that caps transformers_lm in the mid-teens rather than higher.\n\nFarhadi's own work feeds the frontier stack at the component level rather than the architecture level: HellaSwag (ACL 2019) is a benchmark cited in essentially every frontier LLM technical report (GPT-4, Llama, Claude-class evals), XNOR-Net (ECCV 2016) is canonical low-bit quantization that inference stacks descend from, and Model soups / task-arithmetic weight-space methods (2022) underpin model merging — documented pieces frontier labs build on, but he is not an attention/transformer/scaling-law/word2vec author, so this caps in the mid-teens. His language-modeling record is real but comparatively recent and intermittent against a vision-first career — Bidirectional Attention Flow (2017), Grover and HellaSwag (2019), then OLMo/OLMo 2 (2024-2025) as Ai2 CEO — roughly 8 years and now deep as head of a leading fully-open-LLM lab, but with no pre-word2vec vector-space lineage. As a scientific founder he is the genuine article for ~3 years: he co-founded Xnor.ai (2017), a company built directly on his own XNOR-Net efficient-inference research, acquired by Apple in Jan 2020, and now sets the technical direction of Ai2's OLMo program as CEO — though Xnor.ai's core was efficient vision, not language modeling.","evidence":[{"claim":"Google Scholar jeOFRDsAAAAJ (Professor, CSE, University of Washington): 230,069 citations, h-index 100, i10-index 221; top works YOLO (81,998), YOLOv3 (42,656), YOLO9000 (29,619), XNOR-Net (6,826), HellaSwag (5,175), Unsupervised Deep Embedding for Clustering Analysis (5,065), Describing objects by","source_url":"https://scholar.google.com/citations?user=jeOFRDsAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author of '2 OLMo 2 Furious', Ai2's open language model report (43 authors, first listed as Team OLMo)","source_url":"https://arxiv.org/abs/2501.00656","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD, University of Illinois Urbana-Champaign, doctoral advisor David Forsyth; Google Scholar ID jeOFRDsAAAAJ","source_url":"https://www.wikidata.org/wiki/Q80873822","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Professor of computer science and CEO of the Allen Institute for Artificial Intelligence","source_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of YOLO9000: Better, Faster, Stronger with Joseph Redmon","source_url":"https://arxiv.org/abs/1612.08242","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD, University of Illinois Urbana-Champaign, advisor David Forsyth","source_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Professor at University of Washington, CEO of Allen Institute for Artificial Intelligence (Ai2)","source_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (jeOFRDsAAAAJ), UW professor: ~230,069 citations, h-index 100, i10 221; top works YOLO (2016), YOLOv3, YOLO9000, XNOR-Net, HellaSwag, Unsupervised Deep Embedding for Clustering Analysis, Bidirectional Attention Flow, Model soups, Editing models with task arithmetic","source_url":"https://scholar.google.com/citations?user=jeOFRDsAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from University of Illinois Urbana-Champaign, doctoral advisor David Forsyth; Google Scholar ID jeOFRDsAAAAJ","source_url":"https://www.wikidata.org/wiki/Q80873822","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"HellaSwag (Zellers, Holtzman, Bisk, Farhadi, Choi, ACL 2019) is a standard commonsense-inference benchmark reported across frontier LLM evaluations","source_url":"https://arxiv.org/abs/1905.07830","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XNOR-Net binary convolutional networks — first-principles low-precision arithmetic (XNOR/popcount) foundational to quantized inference","source_url":"https://arxiv.org/abs/1603.05279","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Xnor.ai, co-founded by Ali Farhadi and built on the XNOR-Net research, was acquired by Apple in January 2020","source_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author of '2 OLMo 2 Furious', Ai2's open language model report, as Ai2 CEO","source_url":"https://arxiv.org/abs/2501.00656","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — reduces convolution to XNOR/popcount, foundational low-precision inference work","source_url":"https://doi.org/10.1007/978-3-319-46493-0_32","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"HellaSwag benchmark (ACL 2019) — commonsense NLI benchmark used in frontier LLM evaluation reports","source_url":"https://arxiv.org/abs/1905.07830","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Xnor.ai, co-founded on XNOR-Net efficient-AI research, acquired by Apple in 2020","source_url":"https://en.wikipedia.org/wiki/Ali_Farhadi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author of '2 OLMo 2 Furious', Ai2's open language model report","source_url":"https://arxiv.org/abs/2501.00656","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":24},{"id":25,"slug":"andrew-ng","name":"Andrew Ng","title":"Founder","company":"DeepLearning.AI / Landing AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Andrew_Ng","image_url":"/api/v1/ceo-ai-leaderboard/portrait/andrew-ng.jpg","score":74,"tier":"deep_practitioner","dimensions":{"foundations":19,"vector_embeddings":16,"transformers_lm":10,"frontier_founder":12,"lm_domain_depth":12,"hands_on_engineering":18,"industry_impact":20,"scientific_founder":14},"rubric_version":3,"weighted_score":74,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Ng earned a PhD from UC Berkeley (2002, advisor Michael I. Jordan; dossier's Wikidata '1993' date is wrong/mismatched) with a thesis on shaping and policy search in reinforcement learning, producing canonical statistical-learning and optimization work (the Stanford autonomous helicopter RL papers, and co-authorship of Latent Dirichlet Allocation, a foundational topic-modeling/representation paper). His representation-learning record is real but pre-transformer: sparse autoencoders and unsupervised feature learning at Stanford/Google Brain (the 'cat neuron' unsupervised-learning paper) sit squarely in the vector-embeddings/representation lineage, but he has no seq2seq/attention/transformer-architecture or LLM-pretraining/scaling-law authorship — transformers_lm is scored as adjacent-senior-leadership, not authorship, consistent with the brief's instruction not to inflate this dimension for him. He personally co-founded and led Google Brain (2011-2012, distributed deep learning at scale on commodity CPU clusters), then led a 1,300-person AI organization as Chief Scientist at Baidu (2014-2017) including the Deep Speech 2 end-to-end speech system (2181 citations per the dossier's own OpenAlex data). Industry impact is very high: founder of Coursera, deeplearning.ai and Landing AI, director of Stanford AI Lab, and an author credited with 200+ papers per his own institutional bio, corroborating that OpenAlex's 12-work/h-index-5 match here is a severe undercount of his true record.\n\nNg's frontier lineage is real but indirect: he co-founded and led Google Brain (2011-12), whose large-scale distributed deep-learning training (the 'cat neuron' unsupervised work on 16k cores; 'Deep learning with COTS HPC systems') is part of the scale-out training-stack lineage frontier models descend from, and he co-authored 'Learning word vectors for sentiment analysis' (2011), an early word-embedding paper in the representation lineage — but he authored no transformer, attention, word2vec/GloVe, scaling-law or RLHF building block, so this sits at 'documented component the field builds on,' not a named block. His language-modeling record spans ~15 years but is intermittent and secondary to his broader ML/RL/vision work: LDA topic modeling (2003), word vectors (2011), Deep Speech 2 end-to-end speech (2015), and backtranslation grammar correction (2018) — genuine text/LM lineage, not a continuous specialist LM career. He has operated as a scientific/technical founder for ~13 years across multiple companies (Coursera 2012, DeepLearning.AI and Landing AI 2017, AI Fund), personally setting and executing technical direction and authoring the technical content/products — though those companies' cores are AI education and applied ML/CV rather than language modeling specifically.","evidence":[{"claim":"PhD from UC Berkeley in 2002, advisor Michael I. Jordan, thesis on shaping and policy search in reinforcement learning; the Wikidata P582 '1993' date in the dossier does not match and is not corroborated by Wikipedia.","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded and led the Google Brain Deep Learning Project (2011-2012) with Jeff Dean and Greg Corrado, including the large-scale unsupervised feature-learning ('cat neuron') work on 16,000 CPU cores.","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Served as VP & Chief Scientist at Baidu (2014-2017), overseeing a 1,300-person AI team; authored/co-authored over 200 papers in AI and related fields per his own institutional biography.","source_url":"https://www.andrewng.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (2015) and Deep learning with COTS HPC systems (2013) are real, high-impact papers with 2181 and 606 citations respectively.","source_url":"https://doi.org/10.48550/arxiv.1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Directed the Stanford Artificial Intelligence Laboratory (SAIL) as assistant professor (2002) and associate professor (2009); remains adjunct professor.","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile: ~320,529 citations, h-index 159; top works include 'Latent dirichlet allocation' (2003, 65,185), 'On spectral clustering' (2001, 14,602), 'Learning word vectors for sentiment analysis' (2011, 8,311)","source_url":"https://scholar.google.com/citations?hl=en&user=mG4imMEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS Carnegie Mellon 1997, MS MIT 1998, PhD UC Berkeley 2002 under Michael I. Jordan (thesis: shaping and policy search in reinforcement learning); founded and directed Google Brain 2011-2012; Baidu Chief Scientist 2014-2017; co-founded Coursera (2012), DeepLearning.AI (2017), Landing AI, AI Fund","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records doctorate with advisor Michael I. Jordan and Google Scholar ID JgDKULMAAAAJ, employers Stanford, Google, Coursera, Baidu","source_url":"https://www.wikidata.org/wiki/Q2846695","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on 'Deep Speech 2: End-to-End Speech Recognition in English and Mandarin' (2015) and 'Deep learning with COTS HPC systems' (2013)","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded and led the Google Brain project (2011) and co-authored the large-scale distributed/unsupervised deep-learning work that seeded training-at-scale infrastructure; also authored 'Deep learning with COTS HPC systems' (2013, 606 citations).","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Language-modeling-lineage authorship spans LDA (Blei, Ng, Jordan, 2003), 'Learning word vectors for sentiment analysis' (2011), and Deep Speech 2 end-to-end speech recognition (2015).","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded/co-founded Coursera (2012), DeepLearning.AI (2017), Landing AI, and AI Fund, personally setting technical/scientific direction across all.","source_url":"https://www.andrewng.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded and led the Google Brain deep-learning project (2011-2012), including large-scale distributed/unsupervised feature learning on 16,000 CPU cores — infrastructure lineage the frontier training stack descends from.","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-authored 'Learning word vectors for sentiment analysis' (2011, ~8,300 citations), an early word-embedding/representation paper in the LM lineage.","source_url":"https://scholar.google.com/citations?hl=en&user=mG4imMEAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder of DeepLearning.AI (2017) and founder & CEO of Landing AI, plus co-founder of Coursera (2012) and founder of AI Fund — multiple companies where he sets and executes technical direction over ~13 years.","source_url":"https://en.wikipedia.org/wiki/Andrew_Ng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on 'Deep Speech 2: End-to-End Speech Recognition' (2015) and LDA topic modeling (2003), his text/speech language-modeling lineage work.","source_url":"https://arxiv.org/abs/1512.02595","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.84,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":25},{"id":7,"slug":"tom-brown","name":"Tom B. Brown","title":"Co-founder; Chief Compute Officer / pretraining lead","company":"Anthropic","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/tom-brown.jpg","score":74,"tier":"deep_practitioner","dimensions":{"foundations":10,"vector_embeddings":9,"transformers_lm":20,"frontier_founder":20,"lm_domain_depth":12,"hands_on_engineering":19,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":74,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Brown is the verified first author of 'Language Models are Few-Shot Learners' (GPT-3, arXiv:2005.14165, submitted 28 May 2020) — first position on a 31-author training-run paper that ends with Sutskever and Amodei reflects principal responsibility for the run itself, which is the canonical anchor on the transformer/LM dimension. He is also an author of 'Scaling Laws for Neural Language Models' (2020) and, earlier at Google Brain, the verified first author of 'Adversarial Patch' (arXiv:1712.09665, with Dandelion Mane, Aurko Roy, Martin Abadi and Justin Gilmer), an optimization-based construction of physically realizable universal adversarial examples — real personal research, not only infrastructure work. Since co-founding Anthropic he appears on the RLHF line ('Training a Helpful and Harmless Assistant with RLHF', arXiv:2204.05862). His mathematical foundations, however, are the weakest verifiable part of his record and the dispute between the passes turns on it: reporting corroborated by his own account describes a B-minus in undergraduate linear algebra followed by roughly six months of self-study from Axler's 'Linear Algebra Done Right', Coursera and Kaggle before joining OpenAI, with no graduate degree and no authored work in linear algebra, optimization or statistical learning theory. Under the rubric's anchors that is below 'strong graduate training', so foundations sits at the top of the 8-12 band rather than in the PhD-level band. No embedding, contrastive or retrieval work exists under his name, so vector_embeddings is scored on implicit representation learning only.\n\nBrown is the verified first author of 'Language Models are Few-Shot Learners' (GPT-3, 2020) — the paper that established the in-context/few-shot pretrained-transformer paradigm every current frontier model (GPT/Claude/Gemini/Llama) descends from — and a co-author of 'Scaling Laws for Neural Language Models' (2020), the scaling relations frontier labs cite and train against; that is unambiguous 18-20 frontier-founder lineage. His language-modeling record specifically runs from GPT-3 work at OpenAI (~2019) through Anthropic's Claude pretraining and RLHF (2022) to the present — roughly 7 continuous years at the top of the field, but under the 8-year mark for the 13-17 band despite maximal depth, so lm_domain_depth sits at the top of the 8-12 band. He co-founded Anthropic in January 2021 as a genuine technical founder (Chief Compute Officer leading the Core Resources / compute-infrastructure org, having personally led the GPT-3 training run), ~5 years in that role, placing scientific_founder in the 8-12 (3-8 year) band; his 2011 Grouper startup is outside this field and does not count.","evidence":[{"claim":"First author of 'Language Models are Few-Shot Learners' (GPT-3); verified author order begins Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan and ends Ilya Sutskever, Dario Amodei; submitted 28 May 2020","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Adversarial Patch' (2017) with Dandelion Mane, Aurko Roy, Martin Abadi and Justin Gilmer, Google Brain; implementation released in TensorFlow CleverHans","source_url":"https://arxiv.org/abs/1712.09665","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on 'Scaling Laws for Neural Language Models' (2020)","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author on Anthropic's 'Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback' (2022)","source_url":"https://arxiv.org/abs/2204.05862","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No graduate ML training: received a B-minus in his linear algebra course, then self-studied for six months using Axler's 'Linear Algebra Done Right', Coursera and Kaggle before joining OpenAI via a Y Combinator connection; previously worked at the startup Grouper; co-founded Anthropic in 2021 with D","source_url":"https://ca.news.yahoo.com/anthropic-cofounder-tom-brown-networked-160107558.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Tom B. Brown is listed as first author of 'Language Models are Few-Shot Learners' (GPT-3 paper), 31 authors total including Kaplan, Sutskever, D. Amodei.","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Brown was engineering lead for GPT-3 at OpenAI and co-founded Anthropic in 2021 as Chief Compute Officer, leading the Core Resources (compute/infrastructure) team.","source_url":"https://x.com/ycombinator/status/1957815586744070653","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Brown's career timeline: Google Brain (2016-2019, research scientist) -> OpenAI (2019-Jan 2021, researcher/engineering lead on GPT-3) -> Anthropic (Jan 2021-present, co-founder).","source_url":"https://www.longtermwiki.com/wiki/E1258","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Brown is a self-taught engineer without formal graduate ML training; studied computer science and cognitive science at MIT and reportedly got a B-minus in linear algebra before self-studying AI full time; previously co-founded startup Grouper (2011, YC-backed).","source_url":"https://ca.news.yahoo.com/anthropic-cofounder-tom-brown-networked-160107558.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Language Models are Few-Shot Learners' (GPT-3), submitted 28 May 2020","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Language Models are Few-Shot Learners' (GPT-3, submitted 28 May 2020) — the few-shot in-context-learning paradigm that frontier LLMs are built on","source_url":"https://arxiv.org/abs/2005.14165","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Scaling Laws for Neural Language Models' (2020), the scaling relations frontier training runs are designed around","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of Anthropic's 'Training a Helpful and Harmless Assistant with RLHF' (2022), part of the alignment lineage of current frontier assistants","source_url":"https://arxiv.org/abs/2204.05862","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Engineering lead for GPT-3 at OpenAI; co-founded Anthropic in 2021 as Chief Compute Officer leading the Core Resources (compute/infrastructure) team","source_url":"https://x.com/ycombinator/status/1957815586744070653","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Scaling Laws for Neural Language Models' (2020), the scaling-law lineage used to plan frontier pretraining runs","source_url":"https://arxiv.org/abs/2001.08361","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Career timeline: Google Brain (2016-2019, adversarial/vision), OpenAI (2019-Jan 2021, engineering lead on GPT-3), Anthropic (Jan 2021-present, co-founder / Chief Compute Officer leading Core Resources compute-infra)","source_url":"https://www.longtermwiki.com/wiki/E1258","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":26},{"id":16,"slug":"yann-lecun","name":"Yann LeCun","title":"Executive Chairman & Co-founder","company":"AMI Labs (Advanced Machine Intelligence Labs)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Yann_LeCun","image_url":"/api/v1/ceo-ai-leaderboard/portrait/yann-lecun.jpg","score":74,"tier":"deep_practitioner","dimensions":{"foundations":20,"vector_embeddings":18,"transformers_lm":14,"frontier_founder":13,"lm_domain_depth":6,"hands_on_engineering":20,"industry_impact":20,"scientific_founder":8},"rubric_version":3,"weighted_score":74,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"LeCun is a principal architect of the mathematical/engineering core of modern AI: he co-invented convolutional neural networks and pioneered practical backpropagation applications (LeNet, 'Gradient-based learning applied to document recognition' 1998; 'Backpropagation Applied to Handwritten Zip Code Recognition' 1989), work the field still runs on, earning the 2018 ACM Turing Award jointly with Hinton and Bengio. His hands-on record spans PhD-level optimization/statistical-learning training under Maurice Milgram, a Hinton postdoc, and a decade at Bell Labs building and shipping CNN-based systems for real-world document/check recognition. His embeddings/representation-learning work is deep and personally authored but sits in the contrastive/metric-learning and self-supervised lineage (siamese/contrastive embeddings 'Dimensionality Reduction by Learning an Invariant Mapping' 2006; energy-based models; later VICReg/JEPA self-supervised architectures) rather than the text vector-space/word-embedding line, so it scores high but not maximal on vector_embeddings. He is not an author of the seq2seq/attention/original Transformer papers and is publicly a skeptic of autoregressive LLM scaling as a path to intelligence; his transformer-era personal authorship is concentrated in self-supervised and JEPA/world-model architectures (I-JEPA, V-JEPA, 'A Path Towards Autonomous Machine Intelligence' 2022) rather than canonical transformer/LM/RLHF work itself, so transformers_lm is scored as strong-adjacent leadership/authorship rather than top-tier canonical — precise per the brief's instruction to distinguish CNN-era from transformer-era personal authorship. industry_impact is maximal: founding director of Facebook/Meta AI Research (FAIR) and Chief AI Scientist 2013-2025, h-index in the 120-175 range (OpenAlex 120 / Google Scholar 175) with 250k-500k citations, now Executive Chairman of AMI Labs (founded Dec 2025/Nov 2025 per sources, raised $1.03B per Google Scholar-linked reporting).\n\nLeCun is a founding father of the deep-learning substrate all frontier models rest on — convolutional nets, practical backpropagation training (LeNet 1989/1998), energy-based and contrastive representation learning (DrLIM 2006, feeding modern embedding/retrieval methods) — but he authored none of the named transformer-LM building blocks (attention, seq2seq, scaling laws, RLHF) that GPT/Claude/Gemini/Llama technical reports directly cite, and his own transformer-era research (I-/V-JEPA, 'A Path Towards Autonomous Machine Intelligence' 2022) is explicitly an alternative to autoregressive LLMs rather than their ancestor, so frontier lineage is ancestral, not a named block. His personal language-modeling record specifically is thin: he is the vision/CNN member of the Turing trio (Bengio, not LeCun, authored the 2003 neural LM lineage), and while he directed FAIR (2013-2025), which shipped RoBERTa/wav2vec/Llama-1, that is lab leadership over LM systems rather than a continuous personal LM authorship record. As a technical founder he is genuine but recent — co-founder/Executive Chairman of AMI Labs since December 2025 (~9 months), whose scientific core (world-model/JEPA architectures) is his own research direction — on top of ~12 years as founding director of FAIR, a lab-founder/chief-scientist role rather than an independent company he founded.","evidence":[{"claim":"PhD from Université Pierre et Marie Curie (1987), advisor Maurice Milgram, proposed an early form of backpropagation in his thesis; postdoc under Geoffrey Hinton at University of Toronto starting 1987.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Joined AT&T Bell Laboratories in 1988, developed LeNet convolutional neural networks for handwriting/check recognition, commercially deployed.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"2018 ACM A.M. Turing Award, awarded jointly to Yann LeCun, Yoshua Bengio, and Geoffrey Hinton for conceptual and engineering breakthroughs making deep neural networks a critical component of computing.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jacob T. Schwartz Professor of Computer Science, Courant Institute of Mathematical Sciences, New York University, since 2003.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding director of Facebook/Meta AI Research (FAIR), served as Chief AI Scientist 2013-2025; departed Meta to found Advanced Machine Intelligence Labs (AMI Labs) as Executive Chairman.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows h-index 175, i10-index 505, 495,810 total citations; top papers Deep Learning (Nature 2015, 122,796 cites) and Gradient-based learning applied to document recognition (1998, 89,891 cites).","source_url":"https://scholar.google.com/citations?user=WLN3QrAAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD 1987 Universite Pierre et Marie Curie, thesis 'Modeles connexionnistes de l'apprentissage'; Bell Labs 1988-1996 developing convolutional networks (LeNet), Optimal Brain Damage, Graph Transformer Networks, deployed bank-check reading, co-created DjVu; 2018 Turing Award with Bengio and Hinton; Met","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile: ~495,810 citations, h-index 175; top works 'Deep learning' (2015, ~122,796), 'Gradient-based learning applied to document recognition' (1998, ~89,891), 'Backpropagation applied to handwritten zip code recognition' (1989, ~20,709)","source_url":"https://scholar.google.com/citations?user=WLN3QrAAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Advanced Machine Intelligence Labs (AMI Labs) in December 2025, after serving as Chief AI Scientist at Meta; founding director of Facebook/Meta AI Research (FAIR) from 2013.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored the canonical deep-learning substrate the field trains on — 'Gradient-based learning applied to document recognition' (1998, ~60k cites) and 'Backpropagation Applied to Handwritten Zip Code Recognition' (1989), plus the 'Deep learning' Nature 2015 review — CNN/backprop foundations, not the","source_url":"https://doi.org/10.1109/5.726791","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His transformer-era personal authorship is concentrated in self-supervised world-model architectures (JEPA), an explicit alternative to autoregressive LLMs; DrLIM (2006) contrastive/metric learning feeds modern embedding/retrieval methods.","source_url":"https://doi.org/10.1109/cvpr.2006.100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"LeCun co-invented convolutional networks and practical backpropagation (LeNet, 'Gradient-based learning applied to document recognition' 1998; 'Backpropagation Applied to Handwritten Zip Code Recognition' 1989) — foundational deep-learning methods, 2018 Turing Award; he is a public skeptic of autore","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"LeCun's personal transformer-era authorship is concentrated in self-supervised world-model architectures (I-JEPA, V-JEPA, 'A Path Towards Autonomous Machine Intelligence' 2022), not language modeling.","source_url":"https://doi.org/10.1038/nature14539","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Served as founding director / Chief AI Scientist of Facebook-Meta AI Research 2013-2025, then co-founded Advanced Machine Intelligence Labs (AMI Labs) as Executive Chairman in December 2025.","source_url":"https://en.wikipedia.org/wiki/Yann_LeCun","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.94,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":27},{"id":51,"slug":"illia-polosukhin","name":"Illia Polosukhin","title":"Co-Founder, NEAR Protocol / CEO, NEAR AI","company":"NEAR Protocol / NEAR AI","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/illia-polosukhin.jpg","score":71,"tier":"deep_practitioner","dimensions":{"foundations":12,"vector_embeddings":14,"transformers_lm":18,"frontier_founder":19,"lm_domain_depth":10,"hands_on_engineering":14,"industry_impact":15,"scientific_founder":11},"rubric_version":3,"weighted_score":71,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Polosukhin holds a Master's in Applied Mathematics and Computer Science from Kharkiv Polytechnic Institute, and worked as a software/AI engineer at Google (2014-2017), including on the Search and Google Brain/Research teams. He is a confirmed co-author (8th of 8) on 'Attention Is All You Need' (Vaswani et al., 2017), the canonical paper that introduced the Transformer architecture underlying nearly all modern language models — direct, verifiable authorship in the exact lineage this rubric targets. He also co-authored pre-transformer QA/representation work (WikiReading 2016, Coarse-to-Fine QA 2017, Natural Questions 2019). Since 2017-2018 his technical output shifted from core ML research to blockchain infrastructure (NEAR Protocol); recent 'NEAR AI' work (2024-2025) touches decentralized AI agent infrastructure but is not core transformer/embedding research, so industry impact and foundations score below Sutskever-tier despite the canonical co-authorship.\n\nPolosukhin is a named co-author of 'Attention Is All You Need' (2017), the paper that introduced the Transformer and self-attention — the exact architecture every frontier model (GPT, Claude, Gemini, Llama) is built on and cites, which places his own work squarely in the foundation of today's frontier stack (frontier_founder near-top). His verifiable hands-on language-modeling record is concentrated but short and interrupted: WikiReading (2016), Coarse-to-Fine QA (2017), the Transformer (2017) and the Natural Questions benchmark during his Google Brain/Research tenure (~2015-2017), after which he left core LM research for blockchain, with only recent 'NEAR AI' decentralized-agent infrastructure returning to the space — roughly 3-4 concentrated core-LM years rather than a continuous 8-15 (lm_domain_depth mid). He has operated as a genuine technical co-founder since ~2017/2018 (NEAR launched as Near.ai for AI/program synthesis — he authored 'Neural Program Search', 2018 — before pivoting to a layer-1 blockchain), ~8 years as founder-CTO/technical lead, but the company's core became blockchain rather than language modeling, capping scientific_founder in the mid band.","evidence":[{"claim":"Confirmed co-author of 'Attention Is All You Need' (arXiv:1706.03762), the original Transformer paper","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Worked as AI/software engineer at Google 2014-2017 (Search team, then Google Brain/Research), contributing to TensorFlow and QA systems","source_url":"https://en.wikipedia.org/wiki/Illia_Polosukhin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded NEAR Protocol with Alexander Skidanov in 2017/2018, originally as an AI/program-synthesis research effort (near.ai) before pivoting to a layer-1 blockchain","source_url":"https://en.wikipedia.org/wiki/NEAR_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Education: Master's in Applied Mathematics and Computer Science, Kharkiv Polytechnic Institute","source_url":"https://fourweekmba.com/illia-polosukhin/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Attention Is All You Need' (arXiv 1706.03762, 2017), listed as the eighth of eight authors alongside Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez and Kaiser","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (3SyxFIAAAAAJ), affiliation NEAR, research interests Deep Learning / Machine Learning / Program Synthesis: ~307,379 citations, h-index 41, i10-index 45; publications include WikiReading (2016), Coarse-to-Fine Question Answering for Long Documents (2017), Natural Questions (201","source_url":"https://scholar.google.com/citations?user=3SyxFIAAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ukrainian-born computer scientist; engineering manager at Google Research where he co-authored the 2017 transformer paper; co-founded NEAR with Alexander Skidanov, originally launched as Near.ai focused on AI and program synthesis before pivoting to blockchain","source_url":"https://en.wikipedia.org/wiki/Illia_Polosukhin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored post-Google AI research including 'Neural Program Search: Solving Programming Tasks from Description and Examples' (arXiv 1802.04335, 2018)","source_url":"https://arxiv.org/abs/1802.04335","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR is a public proof-of-stake blockchain founded in 2018 by Illia Polosukhin and Alexander Skidanov, mainnet 2020","source_url":"https://en.wikipedia.org/wiki/NEAR_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named co-author of 'Attention Is All You Need' (arXiv:1706.03762, 2017), introducing the Transformer and self-attention that all frontier LLMs descend from","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Pre/parallel language-modeling research at Google — WikiReading (2016), Coarse-to-Fine QA (2017), Natural Questions benchmark (2019)","source_url":"https://doi.org/10.18653/v1/p16-1145","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded NEAR, originally Near.ai for AI/program synthesis (authored 'Neural Program Search', 2018) before pivoting to a layer-1 blockchain","source_url":"https://arxiv.org/abs/1802.04335","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR is a public proof-of-stake blockchain founded in 2018 by Polosukhin and Skidanov, mainnet 2020","source_url":"https://en.wikipedia.org/wiki/NEAR_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Polosukhin is a co-author of 'Attention Is All You Need' (arXiv:1706.03762), the Transformer paper underlying all modern frontier LLMs","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded NEAR with Alexander Skidanov in 2017/2018, originally as near.ai (AI/program synthesis) before pivoting to a layer-1 blockchain launched 2020","source_url":"https://en.wikipedia.org/wiki/NEAR_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":28},{"id":14,"slug":"edo-liberty","name":"Edo Liberty","title":"Founder & Chief Scientist (former CEO)","company":"Pinecone","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/edo-liberty.jpg","score":65,"tier":"deep_practitioner","dimensions":{"foundations":18,"vector_embeddings":20,"transformers_lm":8,"frontier_founder":8,"lm_domain_depth":7,"hands_on_engineering":16,"industry_impact":16,"scientific_founder":12},"rubric_version":3,"weighted_score":65,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Liberty holds a PhD in Computer Science from Yale (postdoc in Yale's Program in Applied Mathematics) and a BSc in Physics/CS from Tel Aviv University, giving deep foundations in linear algebra and matrix methods. He authored canonical streaming/matrix-sketching work — 'Randomized algorithms for the low-rank approximation of matrices' (PNAS 2007) and 'Frequent Directions: Simple and Deterministic Matrix Sketching' (2016), both squarely in the mathematical foundations of vector/embedding representation and widely cited (PNAS paper alone has 575+ citations). He led Amazon AI Labs and Amazon SageMaker research before founding Pinecone in 2019, the company that established and now leads the vector-database category underlying modern embedding-based retrieval/RAG systems — direct hands-on engineering and industry impact centered on vector embeddings. His record in the specific attention/transformer/LM lineage is thinner; his contribution is concentrated upstream in the vector-representation and retrieval-infrastructure side of the field rather than authoring transformer/LM research himself.\n\nLiberty's personal research lineage is randomized/deterministic matrix approximation and sketching (PNAS 2007 low-rank approximation, Frequent Directions 2016) and fast Johnson–Lindenstrauss dimensionality reduction — the linear-algebra machinery beneath LSA/LSI-style vector-space text representation and dense retrieval, which the retrieval-augmented layer around frontier models draws on, but none of it is a named building block (architecture, optimizer, tokenizer, objective, alignment method) that GPT/Claude/Gemini technical reports cite as a component they descend from, so frontier_founder is 'published lineage the stack draws on' rather than a foundational credit. His language-modeling depth is real but indirect and infrastructure-side: the continuous 2007→present thread runs through low-rank/vector-space methods, Yahoo vector search, Amazon SageMaker/AI Labs, and Pinecone's dense-retrieval vector database — adjacent to LM rather than authored n-gram/neural/seq2seq/transformer modeling, hence ~8–15 years of lineage-adjacent (not core LM) work scored mid-range. As Founder & Chief Scientist of Pinecone (2019→2026, ~7 years) he sets and executes the technical direction of a company whose core — vector search and dimensionality reduction — is his own field, a verifiable technical-founder role in the 3–8-year band.","evidence":[{"claim":"PhD Computer Science, Yale University; postdoctoral fellow, Yale Program in Applied Mathematics; BSc Physics & CS, Tel Aviv University","source_url":"https://www.frederick.ai/blog/edo-liberty-pinecone","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Director of Research / Head of Amazon AI Labs and Senior Manager of Research for Amazon SageMaker before founding Pinecone","source_url":"https://www.linkedin.com/in/edoliberty/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Pinecone in 2019, the company credited with establishing the vector-database category, now used by over a million engineers","source_url":"https://edoliberty.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5042783525: 77 works, 2,986 citations, h-index 26; affiliations Yale (2007-2010), Yahoo (2010-2017), Amazon (2016-2020), Tel Aviv University (2003); topics are sparse/compressive sensing, stochastic gradient optimization and algorithms","source_url":"https://api.openalex.org/authors/A5042783525","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Randomized algorithms for the low-rank approximation of matrices', PNAS 2007, Liberty, Woolfe, Martinsson, Rokhlin, Tygert","source_url":"https://pubmed.ncbi.nlm.nih.gov/18056803/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Pinecone company page: Edo Liberty is Founder & Chief Scientist, company founded 2019; previously research director at AWS and at Yahoo, where he worked on custom vector search systems","source_url":"https://www.pinecone.io/company/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes', Discrete & Computational Geometry 2008","source_url":"https://doi.org/10.1007/s00454-008-9110-x","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Randomized algorithms for the low-rank approximation of matrices (PNAS 2007) — the randomized-SVD/low-rank machinery underlying LSA/LSI-style vector-space representation","source_url":"https://pubmed.ncbi.nlm.nih.gov/18056803/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Edo Liberty is Founder & Chief Scientist of Pinecone (founded 2019); previously research director at AWS and at Yahoo, where he worked on vector search systems","source_url":"https://www.pinecone.io/company/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5042783525: topics are sparse/compressive sensing, stochastic gradient optimization, data-management algorithms — not transformer/language-model research","source_url":"https://api.openalex.org/authors/A5042783525","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Edo Liberty is Founder & Chief Scientist of Pinecone, founded 2019; previously research director at AWS and at Yahoo where he built custom vector search systems","source_url":"https://www.pinecone.io/company/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5042783525 topics are sparse/compressive sensing, stochastic gradient optimization and graph algorithms — no transformer/language-model authorship; retrieval/embedding contribution is on the vector-search side","source_url":"https://api.openalex.org/authors/A5042783525","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":29},{"id":22,"slug":"david-luan","name":"David Luan","title":"Co-founder & CEO","company":"Adept AI","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/david-luan.jpg","score":64,"tier":"technically_fluent","dimensions":{"foundations":10,"vector_embeddings":9,"transformers_lm":16,"frontier_founder":14,"lm_domain_depth":14,"hands_on_engineering":16,"industry_impact":15,"scientific_founder":8},"rubric_version":3,"weighted_score":64,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Luan holds a BS in Applied Mathematics & Political Science from Yale (not a graduate research degree), so foundations is scored as strong undergraduate quantitative training rather than PhD-level theory. As VP of Engineering at OpenAI (2017-2020) he led the teams that shipped GPT-2, GPT-3, CLIP and DALL-E, and he is a listed co-author on 'PaLM: Scaling Language Modeling with Pathways' (2022, 2,136+ citations) and 'Generative Pretraining From Pixels' (iGPT, 2020) — both are substantive transformer/LM-lineage contributions with organizational leadership, not just authorship credit. He subsequently led Google Brain's large-model effort before co-founding Adept AI (with Ashish Vaswani and Niki Parmar, co-authors of the original Transformer paper) to build agentic AI systems, so his industry impact is a direct extension of hands-on LM engineering. His personal-paper record is thin (OpenAlex lists only 2 works under his name, reflecting an engineering-leadership rather than first-author-research career), which caps foundations and vector_embeddings.\n\nLuan's own frontier lineage runs through organizational and co-authorship contributions to systems today's frontier models descend from: as OpenAI VP of Engineering (2017–2020) he led the engineering orgs that shipped GPT-2 and GPT-3 — the direct ancestors of the GPT/Claude line — and he is a co-author on PaLM (2022, scaling-laws lineage) and Generative Pretraining from Pixels/iGPT (ICML 2020), both cited pretraining/scaling results, though he authored none of the named building blocks (attention, the transformer, tokenizers), so this is documented lineage work rather than a canonical component. His continuous language-modeling record spans roughly nine years — OpenAI large-model leadership from late 2017, then Google Brain's large-model effort, then Adept — placing him at the low end of the 8–15-year band, and it is engineering-leadership depth rather than a personal first-author research corpus (OpenAlex lists only 2 works). As a founder he co-founded Adept AI in 2022 as CEO and a technically fluent founder, but its core science was led by co-founders Ashish Vaswani and Niki Parmar (original Transformer authors), and the run was short (~2 years until the 2024 Amazon acqui-hire), so his verifiable time personally authoring the core research/code a company runs on is limited.","evidence":[{"claim":"BS Applied Mathematics & Political Science, Yale University (2009-2013)","source_url":"https://news.ycombinator.com/item?id=37415797","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (listed as VP Engineering, OpenAI): ~39,503 citations, h-index 8; papers include GPT-2 'Language Models are Unsupervised Multitask Learners', PaLM, Generative Pretraining from Pixels, and Scratchpads","source_url":"https://scholar.google.com/citations?user=cItVg2MAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata identifies David Luan as an AI researcher and VP of engineering at OpenAI, linking Google Scholar id cItVg2MAAAAJ","source_url":"https://www.wikidata.org/wiki/Q115923706","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'PaLM: Scaling Language Modeling with Pathways' (2022), a 540B-parameter dense transformer scaling study","source_url":"https://arxiv.org/abs/2204.02311","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Generative Pretraining from Pixels' (Image GPT), ICML 2020, applying autoregressive transformer pretraining to images","source_url":"https://proceedings.mlr.press/v119/chen20s.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'PaLM: Scaling Language Modeling with Pathways' (2022), a scaling study in the direct frontier LM lineage","source_url":"https://arxiv.org/abs/2204.02311","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Generative Pretraining from Pixels' (iGPT), ICML 2020, autoregressive transformer pretraining","source_url":"https://proceedings.mlr.press/v119/chen20s.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Adept AI (2022) with Transformer co-authors Ashish Vaswani and Niki Parmar","source_url":"https://news.ycombinator.com/item?id=37415797","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Generative Pretraining from Pixels' (Image GPT), ICML 2020 — autoregressive transformer pretraining","source_url":"https://proceedings.mlr.press/v119/chen20s.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata identifies David Luan as an AI researcher and VP of Engineering at OpenAI, the org that shipped GPT-2/GPT-3","source_url":"https://www.wikidata.org/wiki/Q115923706","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.78,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":30},{"id":29,"slug":"fei-fei-li","name":"Fei-Fei Li","title":"Co-founder & CEO, World Labs; Sequoia Professor of Computer Science, Stanford University","company":"World Labs","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Fei-Fei_Li","image_url":"/api/v1/ceo-ai-leaderboard/portrait/fei-fei-li.jpg","score":64,"tier":"technically_fluent","dimensions":{"foundations":17,"vector_embeddings":15,"transformers_lm":9,"frontier_founder":14,"lm_domain_depth":6,"hands_on_engineering":16,"industry_impact":19,"scientific_founder":8},"rubric_version":3,"weighted_score":64,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Li holds a Caltech PhD (2005) under Pietro Perona and Christof Koch following a Princeton physics degree, and her early record is Bayesian statistical learning rather than applied tooling: 'A Bayesian Hierarchical Model for Learning Natural Scene Categories' (CVPR 2005, Fei-Fei Li and P. Perona, verified via Crossref, 5,247 citations) and 'Learning Generative Visual Models from Few Training Examples' (2004, 7,047 citations) are generative-model and one-shot-learning mathematics — that is the deciding evidence on the foundations dispute, and pass_1's 15 undervalued it. She created and led ImageNet (102,864 citations) and ILSVRC (56,952), supplying the data and benchmark the deep-learning era was built on, and Visual Genome grounds language to image regions. Her representation-learning work is genuine but visual: 'Deep Visual-Semantic Alignments for Generating Image Descriptions' (Karpathy and Fei-Fei, 2015, 8,318 citations) aligns CNN region features with bidirectional-RNN sentence encodings through a multimodal embedding. Her verified Google Scholar profile shows 372,989 citations, h-index 181 and i10-index 467 — among the largest verified records in the field. Transformers/LM is her weakest dimension by a wide margin: she is a co-author of the 2021 foundation-models position paper, but that is a multi-author survey, and she has authored no attention, pretraining, scaling or alignment result — her lineage is vision and spatial intelligence, and the rubric measures the language-modeling core specifically. She now leads World Labs building generative 3D world models (Marble), an organization whose technical core she leads rather than funds.\n\nLi's frontier lineage runs through vision, not the language-modeling core: ImageNet/ILSVRC (2009/2015, ~100k citations) is the canonical dataset+benchmark that catalyzed the deep-learning era and that today's frontier multimodal models (GPT-4V, Gemini, Claude vision) trace visual-pretraining lineage to — a documented, widely-cited building block, though the transformer/LLM stack itself does not descend from it, so this lands in the mid band rather than the top. Her language-modeling record is thin and adjacent: no n-gram, LSI, neural-LM, seq2seq, attention, pretraining, scaling or alignment authorship — only vision-language work (Deep Visual-Semantic Alignments, CNN+bidirectional-RNN captioning, 2015; Visual Genome, 2017) that grounds language to image regions, giving perhaps ~2 years of LM-adjacent output within a 23-year computer-vision career. She is a genuine scientific/technical founder — she co-founded World Labs (Feb 2024, ~2.5 years) with researchers Justin Johnson, Ben Mildenhall and Christoph Lassner and personally sets its technical direction on generative 3D world models — but the company's core is spatial intelligence rather than the embedding/transformer/LM lineage, and her tenure as a company founder is short (prior lab leadership at SAIL/Google Cloud was institutional, not founding).","evidence":[{"claim":"Google Scholar rDfyQnIAAAAJ (Professor of Computer Science, Stanford): 372,989 citations, h-index 181, i10-index 467; top works ImageNet (102,864), ILSVRC (56,952), Perceptual Losses (15,448), foundation models report (12,379), Deep Visual-Semantic Alignments (8,318), Visual Genome (8,062), Learning","source_url":"https://scholar.google.com/citations?user=rDfyQnIAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author with Pietro Perona of 'A Bayesian Hierarchical Model for Learning Natural Scene Categories', CVPR 2005, pp. 524-531 — generative Bayesian statistical-learning work","source_url":"https://api.crossref.org/works/10.1109/cvpr.2005.16","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author with Andrej Karpathy of 'Deep Visual-Semantic Alignments for Generating Image Descriptions' (2014/2015), aligning CNN image-region features with bidirectional-RNN sentence representations via a multimodal embedding","source_url":"https://arxiv.org/abs/1412.2306","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Caltech 2005 (advisors Pietro Perona and Christof Koch), BA physics Princeton 1999; established ImageNet; SAIL director 2013-2018; Chief Scientist of AI/ML at Google Cloud; co-founded World Labs 2024","source_url":"https://en.wikipedia.org/wiki/Fei-Fei_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"World Labs co-founded by Fei-Fei Li with Justin Johnson, Ben Mildenhall and Christoph Lassner, building generative 3D world models (Marble)","source_url":"https://www.worldlabs.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"ImageNet (2009) has ~102,864 citations per Google Scholar and is Li's most-cited canonical work founding large-scale visual recognition datasets/benchmarks","source_url":"https://scholar.google.com/citations?user=rDfyQnIAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Li's Wikipedia entry confirms she established ImageNet, is a Stanford CS professor, and is known as a founder of modern computer vision benchmarking","source_url":"https://en.wikipedia.org/wiki/Fei-Fei_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from Caltech (2005) with doctoral advisors Pietro Perona and Christof Koch, BA physics from Princeton (1999)","source_url":"https://www.wikidata.org/wiki/Q18686107","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"World Labs, founded by Li in Feb 2024 with Justin Johnson, Ben Mildenhall and Christoph Lassner (ML/graphics/vision researchers), raised $1B and shipped the Marble and Atlas world-model products","source_url":"https://www.worldlabs.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"ImageNet: A Large-Scale Hierarchical Image Database (CVPR 2009), the dataset/benchmark that catalyzed the deep-learning era and underpins visual pretraining used by frontier multimodal models","source_url":"https://doi.org/10.1109/cvpr.2009.5206848","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Deep Visual-Semantic Alignments for Generating Image Descriptions (Karpathy & Fei-Fei, 2015) — CNN region features aligned with a bidirectional-RNN sentence model, her main vision-language (LM-adjacent) work","source_url":"https://arxiv.org/abs/1412.2306","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"World Labs co-founded by Fei-Fei Li (Feb 2024) with Justin Johnson, Ben Mildenhall and Christoph Lassner, building generative 3D world models (Marble) — she is the scientific founder setting technical direction","source_url":"https://www.worldlabs.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"ImageNet: A Large-Scale Hierarchical Image Database (CVPR 2009, Deng, Dong, Socher, Li, Li, Fei-Fei) is the canonical large-scale dataset/benchmark that catalyzed the deep-learning era","source_url":"https://doi.org/10.1109/cvpr.2009.5206848","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"World Labs, co-founded by Fei-Fei Li (Feb 2024) with Justin Johnson, Ben Mildenhall and Christoph Lassner, builds generative 3D world models (Marble); Li is its scientific leader","source_url":"https://www.worldlabs.ai/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":31},{"id":28,"slug":"shane-legg","name":"Shane Legg","title":"Co-founder & Chief AGI Scientist","company":"Google DeepMind","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Shane_Legg","image_url":"/api/v1/ceo-ai-leaderboard/portrait/shane-legg.jpg","score":64,"tier":"technically_fluent","dimensions":{"foundations":18,"vector_embeddings":6,"transformers_lm":12,"frontier_founder":14,"lm_domain_depth":7,"hands_on_engineering":14,"industry_impact":19,"scientific_founder":16},"rubric_version":3,"weighted_score":64,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Legg holds a PhD from IDSIA under Marcus Hutter (thesis 'Machine Super Intelligence', 2008) on theoretical models of general intelligence (AIXI-adjacent universal-intelligence formalism), giving him genuine PhD-level foundations in statistical learning, information theory and optimization applied to agents. His personally authored canonical contribution is 'Universal Intelligence: A Definition of Machine Intelligence' (Legg & Hutter, 2007), a foundational but non-mainstream (RL/AGI-theory, not embeddings or transformer) piece of the field's math. He is a listed co-author on landmark deep-RL systems papers built at DeepMind (the 2015 Nature DQN paper 'Human-level control through deep reinforcement learning', and 'Deep reinforcement learning from human preferences', 2017, which seeded RLHF techniques now used to align transformer LMs), which supports hands_on_engineering and a link into the transformers_lm/alignment lineage, though his personal authorship role on these large-team papers is not lead-author/architect level and he has no personally authored vector-embeddings or transformer-architecture papers. industry_impact is very high: he co-founded DeepMind (2010, with Demis Hassabis and Mustafa Suleyman), which produced AlphaGo, AlphaFold, and (post 2023 merger) Gemini, and he has served as Chief AGI Scientist directing research strategy — this is leadership of a lab that produced canonical work, not merely branding.\n\nLegg's clearest lineage into frontier models is as a co-author (5th of 6) on 'Deep reinforcement learning from human preferences' (Christiano, Leike, Brown, Martic, Legg, Amodei, 2017), the RLHF technique that GPT/Claude/Gemini-class instruction-tuning and alignment stacks directly descend from — a documented component frontier labs cite and build on, though not a lead-authored architecture/embedding/objective, so it lands mid-band rather than at the transformer/word2vec ceiling. His personal research record sits in AGI theory (universal intelligence, Kolmogorov-complexity/RL formalism) and deep-RL (DQN, IMPALA), NOT in language modeling per se: no personally authored vector-space, n-gram, seq2seq, transformer or LM-pretraining work, so his LM-specific depth is thin and largely recent/alignment-adjacent plus organizational (Chief AGI Scientist over Gemini). He is, however, a genuine scientific/technical co-founder of DeepMind (2010, with Hassabis and Suleyman), ~16 years operating as founder-Chief AGI Scientist who set and executed the research agenda and personally authored foundational work — a real scientific founder, though DeepMind's breadth (RL, protein folding, LMs) is wider than the language-modeling core these anchors center on.","evidence":[{"claim":"PhD at IDSIA (Dalle Molle Institute for Artificial Intelligence Research) under advisor Marcus Hutter, thesis 'Machine Super Intelligence' (2008)","source_url":"https://en.wikipedia.org/wiki/Shane_Legg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind in 2010 with Demis Hassabis and Mustafa Suleyman; DeepMind acquired by Google 2014, merged with Google Brain in 2023 to form Google DeepMind; Legg serves as Chief AGI Scientist","source_url":"https://en.wikipedia.org/wiki/Shane_Legg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Universal Intelligence: A Definition of Machine Intelligence' (Legg & Hutter, Minds and Machines, 2007) — a formal mathematical definition of machine intelligence combining Kolmogorov complexity and reinforcement learning theory","source_url":"https://doi.org/10.1007/s11023-007-9079-x","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on 'Human-level control through deep reinforcement learning' (Nature, 2015), the DQN paper, and 'IMPALA: Scalable Distributed Deep-RL' (2018) and 'Massively Parallel Methods for Deep Reinforcement Learning' (2015)","source_url":"https://doi.org/10.1038/nature14236","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (5th of 6) on 'Deep reinforcement learning from human preferences' (Christiano, Leike, Brown, Martic, Legg, Amodei, 2017), an early RLHF paper whose technique underlies later LM alignment work","source_url":"https://arxiv.org/abs/1706.03741","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MSc Auckland (thesis 'Solomonoff Induction'); PhD IDSIA 2008 'Machine Super Intelligence' under Marcus Hutter; co-founded DeepMind 2010 with Hassabis and Suleyman; Chief AGI Scientist at Google DeepMind","source_url":"https://en.wikipedia.org/wiki/Shane_Legg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Universal Intelligence: A Definition of Machine Intelligence — Shane Legg and Marcus Hutter, Minds and Machines, 2007","source_url":"https://arxiv.org/abs/0712.3329","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Deep reinforcement learning from human preferences — Christiano, Leike, Brown, Martic, Legg, Amodei (2017); Legg is fifth author","source_url":"https://arxiv.org/abs/1706.03741","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Human-level control through deep reinforcement learning, Nature 2015 (DQN)","source_url":"https://doi.org/10.1038/nature14236","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Legg is fifth author on 'Deep reinforcement learning from human preferences' (2017), an early RLHF paper whose preference-learning technique underlies modern LM alignment used by frontier models","source_url":"https://arxiv.org/abs/1706.03741","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind Technologies in 2010 with Demis Hassabis and Mustafa Suleyman and serves as Chief AGI Scientist, setting the company's long-run research direction","source_url":"https://en.wikipedia.org/wiki/Shane_Legg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Legg's personally authored canonical work is 'Universal Intelligence: A Definition of Machine Intelligence' (Legg & Hutter, 2007) — RL/AGI theory, not language modeling, embeddings or transformers","source_url":"https://doi.org/10.1007/s11023-007-9079-x","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Legg co-founded DeepMind in 2010 with Hassabis and Suleyman and serves as Chief AGI Scientist, a scientist-founder role held ~16 years","source_url":"https://en.wikipedia.org/wiki/Shane_Legg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Legg's personal research core is AGI/RL theory: 'Universal Intelligence: A Definition of Machine Intelligence' (Legg & Hutter, 2007), not language modeling","source_url":"https://doi.org/10.1007/s11023-007-9079-x","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":32},{"id":27,"slug":"aravind-srinivas","name":"Aravind Srinivas","title":"Co-founder & CEO","company":"Perplexity AI","sector":"general","profile_url":null,"image_url":"https://unavatar.io/x/AravSrinivas?fallback=false","score":61,"tier":"technically_fluent","dimensions":{"foundations":14,"vector_embeddings":16,"transformers_lm":14,"frontier_founder":8,"lm_domain_depth":7,"hands_on_engineering":15,"industry_impact":14,"scientific_founder":10},"rubric_version":3,"weighted_score":61,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Aravind Srinivas holds a PhD in Computer Science from UC Berkeley (2021, advisor Pieter Abbeel) and authored well-cited contrastive-representation-learning and reinforcement-learning papers, including CURL (Contrastive Unsupervised Representations for Reinforcement Learning) and Data-Efficient Image Recognition with Contrastive Predictive Coding (CPCv2, with DeepMind during a 2019 internship) — genuine vector-embeddings/representation-learning work with real citation counts (935 and multiple hundreds per paper per the dossier's OpenAlex data, name-matched exactly). He also interned at OpenAI on policy-gradient RL and at Google Brain/DeepMind. He founded Perplexity AI in 2022, an LLM-powered search product built directly on retrieval + language-model integration (Sonar, built on Llama), which is hands-on engineering leadership of an AI-core product, not merely business leadership. His direct authored contributions to the transformer/language-modeling lineage itself (vs. RL/contrastive vision) are thinner, so transformers_lm is scored lower than vector_embeddings and hands_on_engineering.\n\nSrinivas's authored lineage — Decision Transformer (RL via sequence modeling, 2021), Bottleneck Transformers (2021, co-authored with Transformer author Ashish Vaswani), and contrastive representation work (CURL, CPCv2) — is published research the field draws on, but it sits in RL, vision and self-supervised representation learning rather than being a named building block of frontier LLMs (GPT/Claude/Gemini/Llama descend from attention/scaling/RLHF work, not from Decision Transformer or Bottleneck Transformers), so frontier_founder is scored as lineage-adjacent, not foundational. His language-modeling-specific record is thin: he has no canonical LM pretraining/alignment paper, and Perplexity is a retrieval-and-synthesis product built on others' models (Sonar on Meta's Llama), so lm_domain_depth reflects ~4 years of applied LM work from the Perplexity era (2022→) atop a general ML/RL/vision PhD track (2015→), not a deep continuous LM research history. As founder-CEO of Perplexity since 2022 with a genuine UC Berkeley PhD and personal technical direction-setting, he is a real scientific/technical founder, but for only ~4 years and in an applied-LLM company whose core model research is external, placing scientific_founder in the 3–8-year band.","evidence":[{"claim":"Authored CURL (Contrastive Unsupervised Representations for Reinforcement Learning) and Reinforcement Learning with Augmented Data; interned at OpenAI (2018), DeepMind London (2019, CPCv2 contrastive self-supervised learning), Google Research (2020-21)","source_url":"https://en.wikipedia.org/wiki/Aravind_Srinivas","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex record (name-exact match) shows 32 works, 2910 citations, h-index 15, top paper 'Data-Efficient Image Recognition with Contrastive Predictive Coding' (935 citations)","source_url":"https://scholar.google.com/citations?user=GhrKC1gAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar record: 35 papers, 11876 citations, h-index 20","source_url":"https://www.semanticscholar.org/author/A.-Srinivas/41207614","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile lists him as Cofounder and CEO, Perplexity AI with ~15,408 citations, h-index 15; top works include Decision Transformer (3,417), Data-Efficient Image Recognition with CPC (1,916), Bottleneck Transformers (1,801) and CURL (1,698)","source_url":"https://scholar.google.com/citations?user=GhrKC1gAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning — Aravind Srinivas first author with Michael Laskin and Pieter Abbeel (UC Berkeley)","source_url":"https://arxiv.org/abs/2004.04136","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bottleneck Transformers for Visual Recognition — Aravind Srinivas first author with Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel and Ashish Vaswani","source_url":"https://arxiv.org/abs/2101.11605","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Decision Transformer: Reinforcement Learning via Sequence Modeling — Srinivas co-author","source_url":"https://arxiv.org/abs/2106.01345","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Perplexity's Sonar search engine is based on Meta's Llama model","source_url":"https://en.wikipedia.org/wiki/Perplexity_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Perplexity's Sonar search engine is based on Meta's Llama model — the company applies frontier LLMs rather than having authored a foundational LLM component","source_url":"https://en.wikipedia.org/wiki/Perplexity_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Srinivas co-authored Bottleneck Transformers and Decision Transformer (transformer/sequence-modeling lineage, applied to vision and RL), first author on CURL and RAD (RL contrastive representation learning)","source_url":"https://arxiv.org/abs/2101.11605","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Aravind Srinivas co-founded Perplexity AI in 2022 as CEO after a UC Berkeley CS PhD (2021, advisor Pieter Abbeel), operating as the technical founder-CEO","source_url":"https://en.wikipedia.org/wiki/Perplexity_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bottleneck Transformers for Visual Recognition — Srinivas first author with Ashish Vaswani et al.; a vision architecture, not an LLM component","source_url":"https://arxiv.org/abs/2101.11605","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Srinivas co-founded Perplexity AI in 2022 and serves as CEO, PhD UC Berkeley 2021 (advisor Pieter Abbeel)","source_url":"https://en.wikipedia.org/wiki/Aravind_Srinivas","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":33},{"id":56,"slug":"alexander-long","name":"Alexander Long","title":"Founder (Pluralis Research)","company":"Pluralis Research","sector":"crypto","profile_url":null,"image_url":null,"score":59,"tier":"technically_fluent","dimensions":{"foundations":14,"vector_embeddings":13,"transformers_lm":16,"frontier_founder":6,"lm_domain_depth":10,"hands_on_engineering":16,"industry_impact":11,"scientific_founder":9},"rubric_version":3,"weighted_score":59,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes correctly discarded the dossier, every block of which is a different person, and both verified his Pluralis authorship; they diverged because pass_2 found a pre-Pluralis research record that pass_1 could not. That record checks out. The verified arXiv author list for 'Retrieval Augmented Classification for Long-Tail Visual Recognition' (arXiv:2202.11233, 2022) has Alexander Long as FIRST author, with Ajanthan, Garg, Blair, Shen and van den Hengel; its method is a retrieval branch querying a non-parametric external memory of pre-encoded images and text snippets — authored dense-retrieval/embedding work, which is precisely what pass_1 recorded as absent and scored a 6 for. His first verifiable ML paper is 'Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal' (23 May 2019, Alex Long, Joel Mason, Alan Blair, Wei Wang), giving about seven years active, not the dossier's 31. At Pluralis he is the senior/last author of a coherent programme on training transformers over low-bandwidth networks: 'Subspace Networks' (arXiv:2506.01260, verified last author) confines activations and gradients to a predefined low-dimensional subspace, achieving up to 99% compression of model-parallel communication and training billion-parameter models over 80Mbps links — a linear-algebra result applied to transformer structure — plus asynchronous pipeline-parallel optimization and the Agora permissionless 8.6B-parameter pretraining run. Industry impact stays low: the programme is young, citations are minimal, and its influence is still prospective. No Google Scholar profile or verified citation count could be retrieved.\n\nLong's own work sits in the decentralized/communication-efficient LLM-training lineage — Subspace Networks' low-dimensional subspace compression of model-parallel gradients (arXiv:2506.01260, last author), Nesterov asynchronous pipeline-parallel optimization, and the Agora/Pluralis-8B permissionless 8.6B-parameter pretraining run — which is genuine transformer-pretraining and optimizer/training-stack research, but there is no evidence any frontier lab (GPT/Claude/Gemini/Llama) cites or builds on it; it is prospective parallel infrastructure, not a named building block in the frontier stack, so frontier_founder stays mid-low. His verifiable language-modeling record runs from the 2019 UNSW multi-hop reading-comprehension paper through the Pluralis pretraining programme — roughly seven years but intermittent (intervening vision-retrieval and RL work), giving a real but not deep or continuous LM record. As sole author of the identity-confirming 'Protocol Learning' paper (Dec 2024, alexander@pluralis.ai) and senior/last author of the Pluralis research programme, he is a genuine scientific/technical founder personally authoring the core research, but only ~2 verifiable years in that role, which caps scientific_founder despite the strong role match.","evidence":[{"claim":"First author of 'Retrieval Augmented Classification for Long-Tail Visual Recognition' (2022); verified author order Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel; method fuses a base image encoder with a retrieva","source_url":"https://arxiv.org/abs/2202.11233","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Earliest verifiable ML paper: 'Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal', submitted 23 May 2019, authors Alex Long, Joel Mason, Alan Blair, Wei Wang","source_url":"http://export.arxiv.org/api/query?search_query=all:%22Multi-hop%20Reading%20Comprehension%20via%20Deep%20Reinforcement%20Learning%20based%20Document%20Traversal%22&start=0&max_results=5","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Last author of 'Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism' (submitted 2 June 2025); verified author order Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long; up to 99% compression with no convergence degradation, b","source_url":"https://arxiv.org/abs/2506.01260","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sole author of 'Protocol Learning, Decentralized Frontier Risk and the No-Off Problem' (10 December 2024), affiliation Pluralis Research, email alexander@pluralis.ai — the identity-confirming source","source_url":"https://arxiv.org/abs/2412.07890","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation' (7 March 2022) with Alan Blair and Herke van Hoof — non-parametric/nearest-neighbour value estimation","source_url":"http://export.arxiv.org/api/query?search_query=au:%22Alexander_Long%22+AND+cat:cs.LG&start=0&max_results=40&sortBy=submittedDate&sortOrder=ascending","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Last author of 'Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models', reporting Pluralis-8B, an 8.6B-parameter permissionless pretraining run","source_url":"https://arxiv.org/abs/2607.13332","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Alexander Long is the sole author of 'Protocol Learning, Decentralized Frontier Risk and the No-Off Problem' (Dec 2024), with affiliation 'Pluralis Research' and email 'alexander@pluralis.ai' listed in the paper header — this is the identity-confirming source tying this Alexander Long to Pluralis Re","source_url":"https://arxiv.org/abs/2412.07890","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Alexander Long is a co-author (with the same Pluralis research team: Avraham, Shevchenko, Dolatabadi, Pajak, Snewin, Xi, O'Donnell, Ajanthan, Ramasinghe, Koneputugodage, Siriwardhana) of 'Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models', which reports trainin","source_url":"https://arxiv.org/abs/2607.13332","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prior to Pluralis, Alexander Long was affiliated with 'Amazon, Australia' as of the ICML 2024 paper 'A Sampling Theory Perspective on Activations for Implicit Neural Representations' (with co-authors Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Simon Lucey of Univ. of Adelaide) —","source_url":"https://arxiv.org/abs/2402.05427","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author profile (ID 2283309100, matched by co-author overlap and subject matter, distinct from 7 other same-name candidates checked and ruled out) lists 17 papers, h-index 3, citation count 39, spanning 2024-2026, all on distributed/pipeline-parallel/asynchronous LLM training, decent","source_url":"https://api.semanticscholar.org/graph/v1/author/2283309100","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.78,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":34},{"id":24,"slug":"liang-wenfeng","name":"Liang Wenfeng","title":"Founder & CEO, DeepSeek; co-founder, High-Flyer","company":"DeepSeek","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","image_url":null,"score":59,"tier":"technically_fluent","dimensions":{"foundations":10,"vector_embeddings":7,"transformers_lm":13,"frontier_founder":14,"lm_domain_depth":8,"hands_on_engineering":16,"industry_impact":18,"scientific_founder":12},"rubric_version":3,"weighted_score":59,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Liang holds a bachelor's (2006/07) and master's (2010) in electronic information / information and communication engineering from Zhejiang University, with a master's thesis on object tracking from a low-cost PTZ camera — real signal-processing training, but not graduate work in learning theory, embeddings or language modeling, and no authored mathematics. The fact that decides the hands_on_engineering and transformers_lm dispute is one pass_1 missed: he is a named author (position 17 of 52) and the submitting contact of 'Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning' (arXiv:2408.14158), a genuine systems paper on the 10,000-PCIe-A100 cluster, HFReduce allreduce acceleration, HaiScale, 3FS and a congestion-free computation-storage integrated network achieving DGX-A100-class performance at roughly half the cost and 40% less energy. That is a documented engineering artifact he drove, not a leadership credit, and it is the kind of training-infrastructure work the rubric credits under hands_on_engineering. He is likewise a listed author and corresponding contact on the DeepSeek-V3 Technical Report (671B MoE with Multi-head Latent Attention and multi-token prediction) and DeepSeek-R1, which showed reasoning can be elicited by pure RL. Scores stay moderate on the research dimensions because these are very large corporate reports in which his individual contribution is not separable, he has no independent authored work in attention, embeddings or scaling, and no correctly-disambiguated citation record exists. Industry impact is high on the verifiable ground that DeepSeek-V2/V3/R1 are open models the field demonstrably builds on.\n\nDeepSeek-V3 (671B MoE with Multi-head Latent Attention and multi-token prediction) and DeepSeek-R1 (reasoning elicited by pure RL/GRPO) are open-weight frontier-class models whose methods and open weights are demonstrably cited and built on across the field, and Liang is a verified author and corresponding contact on both plus the Fire-Flyer AI-HPC cluster paper — so a real frontier lineage exists, though as one of ~200 authors his personal contribution is a driven-org/infrastructure credit rather than a named building block (MLA is publicly attributed to a junior researcher), placing frontier_founder in the mid band rather than the 18-20 authored-block band. His verifiable language-modeling record is short and recent — DeepSeek from May 2023 (~3 years), preceded by quant-ML and GPU-cluster work at High-Flyer/High-Flyer AI (2015/2019) that is adjacent infrastructure, not LM research — so lm_domain_depth sits at the 3-year, real-but-brief boundary. He operates as a genuinely hands-on founder-CEO who sets and co-authors the technical direction (unusual for a CEO), giving ~3 years as DeepSeek's technical founder and ~7 counting the High-Flyer AI compute build, which supports the middle of the 3-8-year scientific-founder band.","evidence":[{"claim":"Named author (17th of 52: 'Wenfeng Liang') and submitting contact of 'Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning' (submitted 26 August 2024) — Fire-Flyer 2 with 10,000 PCIe A100 GPUs, HFReduce, HaiScale, 3FS, HAI-Platform, DGX-A100-class performance at half the","source_url":"https://arxiv.org/abs/2408.14158","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author and corresponding contact on the DeepSeek-V3 Technical Report (671B-parameter MoE, 37B active, Multi-head Latent Attention, multi-token prediction, 14.8T tokens)","source_url":"https://arxiv.org/abs/2412.19437","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author on 'DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning'","source_url":"https://arxiv.org/abs/2501.12948","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BEng and MEng from Zhejiang University (master's thesis on object tracking with a low-cost PTZ camera); co-founded High-Flyer, a quantitative hedge fund applying machine learning to trading, and founded DeepSeek in 2023","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"DeepSeek released the open-weight DeepSeek-V2, V3 and R1 models","source_url":"https://en.wikipedia.org/wiki/DeepSeek","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bachelor's degree (2006) and Master's degree (2010) in Information and Communication Engineering, Zhejiang University; master's thesis on target-tracking with PTZ cameras.","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded High-Flyer Capital Management in 2015, a quantitative hedge fund that applied machine learning to trading and built large-scale GPU compute clusters.","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder and CEO of DeepSeek (founded 2023), which released DeepSeek-V2, V3 and R1, open-weight models that had significant technical and market impact.","source_url":"https://en.wikipedia.org/wiki/DeepSeek","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as a contact author on the DeepSeek-V3 Technical Report (arXiv 2412.19437), a ~200-author paper credited to \"DeepSeek-AI\".","source_url":"https://arxiv.org/abs/2412.19437","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BEng Electronic Information Engineering (2007) and MEng Information & Communication Engineering (2010), Zhejiang University; master's thesis on object tracking with a low-cost PTZ camera; co-founded High-Flyer 2016, High-Flyer AI 2019, DeepSeek May 2023; began acquiring thousands of Nvidia GPUs in 2","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Liang is founder and CEO of DeepSeek (founded May 2023) and a listed author/corresponding contact on the DeepSeek-V3 Technical Report — 671B MoE, Multi-head Latent Attention, multi-token prediction — an open model the field builds on","source_url":"https://arxiv.org/abs/2412.19437","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed author on DeepSeek-R1, which demonstrated reasoning can be incentivized via pure reinforcement learning; the recipe and open weights are widely cited and reproduced by other labs","source_url":"https://arxiv.org/abs/2501.12948","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author (position 17) and submitting contact of the Fire-Flyer AI-HPC paper documenting the 10,000-A100 training cluster (HFReduce, HaiScale, 3FS) that DeepSeek's models were trained on — evidence he drives technical direction as a founder","source_url":"https://arxiv.org/abs/2408.14158","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded quantitative hedge fund High-Flyer (2015) and its AI arm (2019) which built large GPU clusters, then founded DeepSeek in 2023 — establishing the technical-founder timeline","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"DeepSeek-R1 introduced reasoning-capability elicitation via pure reinforcement learning (GRPO) and reasoning distillation, methods now widely built on across frontier reasoning models; Liang is a listed author","source_url":"https://arxiv.org/abs/2501.12948","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"DeepSeek-V3 Technical Report (671B MoE, Multi-head Latent Attention, multi-token prediction) lists Liang as author/corresponding contact — architecture the frontier open-model ecosystem descends from","source_url":"https://arxiv.org/abs/2412.19437","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Liang is a named author and submitting contact on the Fire-Flyer AI-HPC cost-efficient training-stack paper (10,000 A100 cluster, HFReduce, 3FS)","source_url":"https://arxiv.org/abs/2408.14158","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Liang co-founded quant hedge fund High-Flyer (2015) and is founder and CEO of its AI company DeepSeek (2023), setting its technical direction","source_url":"https://en.wikipedia.org/wiki/Liang_Wenfeng","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":35},{"id":33,"slug":"jonathan-ross","name":"Jonathan Ross","title":"Founder & CEO","company":"Groq","sector":"general","profile_url":null,"image_url":null,"score":55,"tier":"technically_fluent","dimensions":{"foundations":12,"vector_embeddings":4,"transformers_lm":8,"frontier_founder":16,"lm_domain_depth":4,"hands_on_engineering":18,"industry_impact":16,"scientific_founder":15},"rubric_version":3,"weighted_score":55,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Ross studied mathematics and computer science at NYU's Courant Institute. At Google he started the Tensor Processing Unit (TPU) as a 20%-time project in 2013, personally designing and implementing core elements of the first-generation chip, and is a listed co-author on the canonical 'In-Datacenter Performance Analysis of a Tensor Processing Unit' (ISCA 2017, arXiv:1704.04760) among ~75 co-authors; the TPU became the hardware backbone for a majority of Google's ML compute and powered AlphaGo. He founded Groq in 2016 to build inference-optimized LPU chips, co-authoring hardware architecture papers ('Think Fast: A TSP for Accelerating Deep Learning Workloads', ISCA 2020). This is strong, verifiable hands-on chip-engineering depth and real industry impact on the infrastructure AI models run on — but it is systems/hardware engineering, not authorship of the math, embeddings, or transformer/LM research lineage itself, so those dimensions score low.\n\nRoss's foundational contribution to today's frontier models is at the compute layer, not the algorithm layer: he started Google's TPU as a 20% project in 2013 and co-authored the canonical TPU paper (ISCA 2017), and TPUs are the named training hardware for Google's frontier LLMs (PaLM/Gemini), while Groq's LPU/TSP (co-authored 'Think Fast', ISCA 2020) is a named inference stack for LLMs — a real 'training/inference stack those models descend from,' earning a high frontier_founder score, though he authored none of the architecture, attention, embeddings or objectives themselves. He has NO personal language-modeling research record — his work is hardware adjacent to ML/LM, continuous since 2013 but not LM science — so lm_domain_depth is low. As founder and technical architect of Groq since 2016 (~10 years), personally designing the chip architecture and co-authoring the company's core papers/patents, he is a genuine scientific/technical founder in AI infrastructure, placing scientific_founder in the 8-15-year band.","evidence":[{"claim":"Co-author on 'In-Datacenter Performance Analysis of a Tensor Processing Unit' (ISCA 2017), the canonical TPU paper","source_url":"https://arxiv.org/abs/1704.04760","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Started the Google TPU as a 20% project in 2013, personally designed/implemented core elements of the first-gen chip; team took it to production in 15 months","source_url":"https://en.wikipedia.org/wiki/Tensor_Processing_Unit","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Groq in 2016 to build the LPU (Language Processing Unit), an inference-optimized AI chip; NVIDIA received a perpetual license to Groq's patent portfolio in a Dec 2025 deal","source_url":"https://en.wikipedia.org/wiki/Groq","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Studied mathematics and computer science at NYU's Courant Institute","source_url":"https://www.linkedin.com/in/ross-jonathan/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author on 'In-Datacenter Performance Analysis of a Tensor Processing Unit', ISCA 2017, with Jouppi, Young, Patil, Patterson, Dean et al.","source_url":"https://arxiv.org/abs/1704.04760","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Second author on 'Think Fast: A Tensor Streaming Processor (TSP) for Accelerating Deep Learning Workloads', ISCA 2020, pp. 145-158, all authors affiliated Groq Inc.","source_url":"https://dblp.org/rec/conf/isca/AbtsRSWBHBTKKHL20.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ross was one of the designers of the TPU at Google; co-founded Groq in 2016; the chip was renamed from Tensor Streaming Processor to Language Processing Unit; joined Nvidia in December 2025 as part of a licensing deal while Groq continues operating","source_url":"https://en.wikipedia.org/wiki/Groq","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Started the Google TPU as a 20%-time project in 2013 and personally designed/implemented core elements of the first-generation chip","source_url":"https://en.wikipedia.org/wiki/Tensor_Processing_Unit","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Groq in 2016 as technical founder; the chip (Tensor Streaming Processor) was renamed Language Processing Unit, an inference-optimized accelerator for LLMs","source_url":"https://en.wikipedia.org/wiki/Groq","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on the canonical TPU paper; Google's TPUs are the training/serving hardware for its frontier language models","source_url":"https://arxiv.org/abs/1704.04760","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Groq in 2016 and drives the LPU inference-chip architecture used to run LLM inference; co-author of the 2020 Groq TSP paper","source_url":"https://en.wikipedia.org/wiki/Groq","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Groq's Tensor Streaming Processor / Language Processing Unit architecture, authored by Groq-affiliated authors led on architecture by Ross","source_url":"https://dblp.org/rec/conf/isca/AbtsRSWBHBTKKHL20.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.76,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":36},{"id":53,"slug":"pramod-viswanath","name":"Pramod Viswanath","title":"Co-founder (Sentient); Forrest G. Hamrick Professor in Engineering, Princeton University","company":"Sentient","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Pramod_Viswanath","image_url":null,"score":55,"tier":"technically_fluent","dimensions":{"foundations":18,"vector_embeddings":13,"transformers_lm":12,"frontier_founder":6,"lm_domain_depth":8,"hands_on_engineering":10,"industry_impact":13,"scientific_founder":8},"rubric_version":3,"weighted_score":55,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Viswanath is a career information theorist with the deepest mathematical record in this batch: a UC Berkeley PhD under Venkat Anantharam and David Tse, IEEE Fellow 2013, co-author with Tse of 'Fundamentals of Wireless Communication' (13,408 citations in OpenAlex alone), and author of foundational multiuser information-theory results — opportunistic beamforming, vector Gaussian broadcast channel capacity and uplink-downlink duality, the diversity-multiplexing tradeoff. The dispute that matters is vector_embeddings, and pass_2 found evidence pass_1 missed: 'All-but-the-Top: Simple and Effective Postprocessing for Word Representations' (arXiv:1702.01417, Jiaqi Mu, Suma Bhat, Pramod Viswanath, ICLR 2018) is verified authored word-embedding geometry — removing the common mean vector and top dominating directions improves word2vec and GloVe on similarity, analogy and semantic textual similarity. That is squarely inside the rubric's vector-space lineage and cannot be scored at pass_1's 6. His LM work is real but recent and collaborative: he is the sixth of seven authors on 'Scalable Fingerprinting of Large Language Models' (arXiv:2502.07760, with Nasery, Hayase, Brooks, Sheng, Tyagi and Oh) — LLM security rather than modeling, pretraining or scaling. Hands-on engineering is his weakest dimension and pass_2 over-scored it at 13: his contribution is algorithm design and analysis carried out with students and co-authors, and no personally built or shipped training system, model or infrastructure could be verified. Sentient is early and, per the rubric, its fundraising and branding count for nothing.\n\nViswanath's own frontier-lineage claim rests on 'All-but-the-Top' (Mu, Bhat, Viswanath, ICLR 2018), a word-embedding postprocessing method in the word2vec/GloVe geometry lineage — genuine published lineage work but a niche postprocessing trick, not a named building block (attention, tokenizer, optimizer, scaling law) that GPT/Claude/Gemini demonstrably descend from; his 2025 LLM-fingerprinting work is model security, not a modeling primitive frontier stacks build on. His language-modeling record is intermittent and adjacent: a 2017 word-embedding paper and a 2025 LLM-fingerprinting paper bracket ~8 years, but his continuous career core is wireless communications and information theory, not neural/statistical language modeling, so there is no deep continuous LM record. As scientific founder he is a credible technical co-founder of Sentient (with Himanshu Tyagi and Sandeep Nailwal, ~2024) whose OML/fingerprinting research he personally authors, but the tenure is only ~1-2 years and the company's core is a decentralized-AI crypto protocol rather than language modeling itself, placing him below the 3-8-year technical-founder band.","evidence":[{"claim":"Co-author of 'All-but-the-Top: Simple and Effective Postprocessing for Word Representations' (2017/ICLR 2018); verified author list Jiaqi Mu, Suma Bhat, Pramod Viswanath; eliminates the common mean vector and top dominating directions from word vectors, improving word2vec and GloVe on similarity, ca","source_url":"https://arxiv.org/abs/1702.01417","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sixth of seven authors on 'Scalable Fingerprinting of Large Language Models' (2025); verified author list Anshul Nasery, Jonathan Hayase, Creston Brooks, Peiyao Sheng, Himanshu Tyagi, Pramod Viswanath, Sewoong Oh","source_url":"https://arxiv.org/abs/2502.07760","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD UC Berkeley EECS (advisors Venkat Anantharam and David Tse); Professor of ECE at Princeton; IEEE Fellow 2013 for contributions to the theory and practice of wireless communications","source_url":"https://en.wikipedia.org/wiki/Pramod_Viswanath","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile lPycXNcAAAAJ is the identifier recorded for him in Wikidata","source_url":"https://www.wikidata.org/wiki/Q29387745","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD UC Berkeley EECS, advisors Venkat Anantharam and David Tse; IEEE Fellow (2013) for wireless communications theory","source_url":"https://en.wikipedia.org/wiki/Pramod_Viswanath","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author, 'Scalable Fingerprinting of Large Language Models' (arXiv 2502.07760, 2025) with Peiyao Sheng, Himanshu Tyagi and others","source_url":"https://arxiv.org/html/2505.16723","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Sentient (with Himanshu Tyagi and Sandeep Nailwal), which raised $85M seed led by Founders Fund/Pantera/Framework to build the OML cryptographic protocol for decentralized AI","source_url":"https://www.dailyprincetonian.com/article/2025/05/princeton-features-profiles-sentient-ai-loyal-alignment","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (Forrest G. Hamrick Professor in Engineering, Princeton): ~40,148 citations, h-index 64, i10-index 171; research areas blockchains and wireless communication","source_url":"https://scholar.google.com/citations?user=lPycXNcAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Sentient (with Himanshu Tyagi and Sandeep Nailwal) building the OML cryptographic protocol for decentralized AI; founded ~2024","source_url":"https://www.dailyprincetonian.com/article/2025/05/princeton-features-profiles-sentient-ai-loyal-alignment","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Sentient with Himanshu Tyagi and Sandeep Nailwal (~2024), building the OML cryptographic protocol for decentralized AI; he personally sets the technical/research direction as a scientific founder","source_url":"https://www.dailyprincetonian.com/article/2025/05/princeton-features-profiles-sentient-ai-loyal-alignment","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":37},{"id":108,"slug":"charles-xie","name":"Charles Xie","title":"Founder & CEO","company":"Zilliz (creator of Milvus)","sector":"general","profile_url":null,"image_url":null,"score":54,"tier":"technically_fluent","dimensions":{"foundations":11,"vector_embeddings":18,"transformers_lm":6,"frontier_founder":4,"lm_domain_depth":6,"hands_on_engineering":16,"industry_impact":15,"scientific_founder":13},"rubric_version":3,"weighted_score":54,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The two passes diverged almost entirely because pass_1 exhausted its search budget and could find no authored record, scoring him as a founder of a real product with no personal research, while pass_2 located peer-reviewed authorship. That authorship is verified here independently through Crossref: he is the last author of 'Milvus: A Purpose-Built Vector Data Management System' (SIGMOD 2021, DOI 10.1145/3448016.3457550), listed 22nd of 22 authors with the affiliation 'Zilliz, Shanghai, China', and last author of 'Manu: A Cloud Native Vector Database Management System' (PVLDB 2022, DOI 10.14778/3554821.3554843), 15th of 15 with the same affiliation. Last-author position on both system papers, with the company affiliation printed, is the senior/responsible role and is exactly the 'authored, built or shipped' standard the vector_embeddings dimension names — approximate-nearest-neighbour indexing, quantization and distributed similarity search over high-dimensional embeddings is the vector-space retrieval layer of the rubric's own lineage, and he built and shipped it rather than consuming it. Milvus is a graduated LF AI & Data Foundation project under the Linux Foundation with core contributors from multiple hardware and platform vendors. Foundations is scored mid-band and deliberately conservatively: the work demands applied linear algebra and ANN index mathematics, but no thesis, degree or first-principles mathematical publication under his name could be verified — his Wikidata ORCID record is an empty stub with zero works, educations or employments. transformers_lm is low: his systems serve retrieval-augmented and embedding workloads for language models, but he has authored no modeling, pretraining or scaling work.\n\nCharles Xie founded Zilliz in 2017 and invented Milvus (open-sourced 2019), personally authoring the company's two core system papers as last/senior author under the Zilliz affiliation — a verifiable ~9-year record as a scientific/technical founder-CEO whose company's core IS vector search, preceded by six years as an Oracle 12c Multitenant founding engineer (database systems, not language modeling). Milvus is retrieval infrastructure consumed by RAG pipelines alongside frontier LLMs; it is not a method, architecture, embedding, optimizer or training/inference component that GPT/Claude/Gemini/Llama-class models are built on or cite in their technical reports, so his frontier lineage is downstream ecosystem tooling rather than a foundational building block. His language-modeling depth is adjacent, not core: vector-space similarity search over embeddings is part of the retrieval lineage, but he has authored no neural/statistical LM, seq2seq, transformer, pretraining or scaling work, and his verifiable vector-DB record runs from ~2017/2019 as systems engineering rather than modeling. Thus scientific_founder is credited strongly (founder + authored core research, 8–15yr band), while frontier_founder and lm_domain_depth stay low.","evidence":[{"claim":"Last author (22nd of 22) of 'Milvus: A Purpose-Built Vector Data Management System', Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data, with the affiliation 'Zilliz, Shanghai, China'; co-authors include Jianguo Wang (Zilliz & Purdue), Xiaomeng Yi, Rentong Guo and Hai","source_url":"https://api.crossref.org/works/10.1145/3448016.3457550","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Last author (15th of 15) of 'Manu: A Cloud Native Vector Database Management System', Proceedings of the VLDB Endowment, 2022, affiliation Zilliz","source_url":"https://api.crossref.org/works/10.14778/3554821.3554843","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Milvus is an open-source high-performance vector database developed by Zilliz and donated to the LF AI & Data Foundation under the Linux Foundation (Apache 2.0), with core contributors from Zilliz, ARM, NVIDIA, AMD, Intel, Meta, IBM, Salesforce, Alibaba and Microsoft","source_url":"https://milvus.io/docs/overview.md","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him only as 'researcher' with ORCID 0009-0000-1713-8696; the ORCID public record contains no works, education or employment entries, so no degree or prior role is verifiable","source_url":"https://pub.orcid.org/v3.0/0009-0000-1713-8696/record","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Milvus is an actively maintained open-source vector database for AI applications, per its GitHub organization","source_url":"https://github.com/milvus-io","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Milvus is an actively maintained, widely used open-source vector database whose GitHub organization describes it as 'the open source vector database designed for AI applications.'","source_url":"https://github.com/milvus-io","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Zilliz's live engineering blog shows substantive, ongoing technical work on the Milvus vector database (e.g. 'Announcing Milvus 3.0: Lake-Native Vector Search and a More Powerful Retrieval Engine', 'How Force Merge Compaction Nearly Doubled Milvus Search QPS'), evidencing a real, technically deep ve","source_url":"https://milvus.io/blog","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's Semantic Scholar match for 'Charles Xie' (author ID 30749452, 44 papers, h-index 16, citation_count 1017) consists entirely of engineering-design-education, CAD, physics-classroom, and STEM-pedagogy papers (e.g. in The Physics Teacher, Computers & Education, Journal of Mechanical Desig","source_url":"https://api.semanticscholar.org/graph/v1/author/30749452?fields=name,affiliations,papers.title,papers.year,papers.venue","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Charles Xie is founder & CEO of Zilliz, which he founded in 2017, and inventor of the Milvus vector database; previously a founding engineer on Oracle's 12c Multitenant database team (~6 years), MS CS University of Wisconsin-Madison","source_url":"https://www.unite.ai/charles-xie-founder-ceo-of-zilliz-interview-series/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Last author of 'Milvus: A Purpose-Built Vector Data Management System' (SIGMOD 2021) with the Zilliz affiliation — senior/responsible authorship of the vector-search system the company runs on","source_url":"https://api.crossref.org/works/10.1145/3448016.3457550","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Milvus is an open-source vector database designed for AI applications (retrieval/RAG layer), not a model architecture or training/inference component of frontier LLMs","source_url":"https://milvus.io/docs/overview.md","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Charles Xie is founder & CEO of Zilliz building databases/search for AI and LLM applications and invented the Milvus open-source vector database","source_url":"https://www.unite.ai/charles-xie-founder-ceo-of-zilliz-interview-series/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Last author of the SIGMOD 2021 Milvus and PVLDB 2022 Manu vector-DBMS system papers under the Zilliz affiliation — the vector-search/retrieval layer that serves LLM/RAG embedding workloads","source_url":"https://api.crossref.org/works/10.1145/3448016.3457550","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":38},{"id":30,"slug":"greg-brockman","name":"Greg Brockman","title":"Co-founder & President","company":"OpenAI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Greg_Brockman","image_url":"/api/v1/ceo-ai-leaderboard/portrait/greg-brockman.jpg","score":52,"tier":"technically_fluent","dimensions":{"foundations":6,"vector_embeddings":4,"transformers_lm":12,"frontier_founder":11,"lm_domain_depth":9,"hands_on_engineering":17,"industry_impact":16,"scientific_founder":13},"rubric_version":3,"weighted_score":52,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Brockman has no ML/CS graduate degree and no first-author research papers in the core lineage; he studied at Harvard and MIT (leaving MIT without completing a degree per Wikipedia) and built his early career as a software engineer/CTO at Stripe before co-founding OpenAI. He is listed as a co-author on several major OpenAI systems papers — Evaluating Large Language Models Trained on Code (Codex, 2021), Robust Speech Recognition via Large-Scale Weak Supervision (Whisper, 2022), Dota 2 with Large-Scale Deep RL (2019), and OpenAI Gym (2016) — reflecting large-team engineering-leadership co-authorship on canonical systems rather than personal authorship of the core mathematical/architectural ideas, so transformers_lm and foundations are scored as engineering-adjacent, not principal-investigator-level. His genuine strength is hands-on infrastructure and engineering: he was OpenAI's founding CTO, personally built early engineering culture/infra, and is widely credited as a strong low-level programmer (e.g., early payments infra at Stripe, OpenAI's compute/training infrastructure). Industry impact is high as a co-founder and president of OpenAI, one of the organizations that produced canonical transformer/RLHF-era systems, though that impact is organizational/leadership rather than personally authored research.\n\nBrockman is a genuine founder-engineer rather than a research principal: today's frontier OpenAI models (GPT/ChatGPT/Codex) descend directly from the training and inference infrastructure he is credited with personally building as OpenAI's founding CTO, and he is a listed co-author on Codex (2021) and the GPT-4 Technical Report (2023), which places his work inside the frontier stack — but the transformer architecture, scaling laws and RLHF methods were authored by others, so he sits at the 'canonical training/inference stacks' band, not the method-authorship band. His hands-on language-modeling record is engineering-adjacent and dates to roughly the GPT-3/Codex era (~2019 onward, ~7 years); his pre-2019 work (OpenAI Gym 2016, Dota 2 2019) is reinforcement learning, not language modeling. As a co-founder and founder-CTO/President of OpenAI since December 2015 (~10 years) he personally set and executed the engineering/infrastructure direction the company runs on, which earns a mid-band scientific/technical-founder score even though the core research science was led by others.","evidence":[{"claim":"Brockman began his career at Stripe in 2010 after leaving MIT, became CTO in 2013, left in 2015 to co-found OpenAI where he was CTO and later became President.","source_url":"https://en.wikipedia.org/wiki/Greg_Brockman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed co-author on 'Evaluating Large Language Models Trained on Code' (Codex), arXiv 2107.03374, 2021.","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar profile lists 40 papers and 55,611 citations under his name (co-authorship on large OpenAI team papers).","source_url":"https://www.semanticscholar.org/author/Greg-Brockman/2065151121","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Enrolled at Harvard 2008 and left after a year, briefly enrolled at MIT and dropped out in 2010 to join Stripe; no degree completed; Stripe's first CTO from 2013 to May 2015; co-founded OpenAI December 2015, led recruiting of the founding team, served as CTO and President; led OpenAI Gym and OpenAI","source_url":"https://en.wikipedia.org/wiki/Greg_Brockman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of Evaluating Large Language Models Trained on Code / Codex (2021), Robust Speech Recognition via Large-Scale Weak Supervision / Whisper (2022), Dota 2 with Large Scale Deep Reinforcement Learning (2019), OpenAI Gym (2016) and the GPT-4 Technical Report (2023)","source_url":"https://api.openalex.org/authors/A5040311065","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar records 55,611 citations and h-index 11 for the profile, with OpenAI Gym at 5,645 citations and Dota 2 with Large Scale Deep RL at 2,204","source_url":"https://api.semanticscholar.org/graph/v1/author/2065151121?fields=name,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata lists occupations entrepreneur, investor, programmer, researcher, education at Harvard (2008-2009) and MIT (2009-2010) with no degree recorded, employers OpenAI (from 2015) and Stripe (2010-2015)","source_url":"https://www.wikidata.org/wiki/Q108398183","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of OpenAI (Dec 2015), served as CTO and President, and led recruiting of the founding team and OpenAI's engineering/training infrastructure.","source_url":"https://en.wikipedia.org/wiki/Greg_Brockman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on OpenAI systems papers (Codex 2021, Whisper 2022, Dota 2 2019, OpenAI Gym 2016, GPT-4 Technical Report 2023) reflecting engineering-leadership co-authorship, not principal research authorship.","source_url":"https://api.openalex.org/authors/A5040311065","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder, founding CTO and President of OpenAI (from 2015), co-author on the GPT-4 Technical Report, Codex ('Evaluating Large Language Models Trained on Code', 2021) and Whisper (2022).","source_url":"https://en.wikipedia.org/wiki/Greg_Brockman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records employer OpenAI from 2015 and prior CTO role at Stripe (2010-2015, payments infra, not language modeling).","source_url":"https://www.wikidata.org/wiki/Q100604534","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":39},{"id":103,"slug":"kasian-franks","name":"Kasian Franks","title":"Founder & CEO","company":"Vector Space Biosciences / Vectorspace AI (also Cymetica/EventTrader)","sector":"general","profile_url":"https://cymetica.com/founder.txt","image_url":null,"score":51,"tier":"technically_fluent","dimensions":{"foundations":11,"vector_embeddings":16,"transformers_lm":6,"frontier_founder":4,"lm_domain_depth":10,"hands_on_engineering":13,"industry_impact":10,"scientific_founder":13},"rubric_version":3,"weighted_score":51,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes correctly treated the self-authored profile page as untrusted and scored only corroborated items; they diverged because pass 1 was blocked by Google Patents rate-limiting and could verify only one patent, while pass 2 reached the full patent record. I re-verified each disputed item directly. The patents pass 1 could not confirm are real: US20030204496A1 'Inter-term relevance analysis for large libraries' (inventors Sandip Ray, Raf Podowski, Kasian Franks; assignee X-MINE Inc.; filed 29 April 2002) claims second-order term co-occurrence to surface relationships not explicitly stated in a corpus; US7987191B2 and its continuation US9026543B2 'System and method for generating a relationship network' (Franks, Myers, Podowski; assigned to the University of California; priority 6 June 2005) build variable-length data vectors from framed inter-term distance scores with direct and indirect relationships and thematic context filtering — a hand-built distributional-semantics engine filed eight years before word2vec, which the rubric explicitly treats as foundational lineage rather than dated. Further patents assigned to Intertrust (US9177044B2 on relationships extracted from human-generated lists, US9600533B2 on media matching, priority 2006-2007) show this was shipped product engineering across several companies over fifteen years, not a single filing. His one substantive paper is corroborated by Crossref: he is second author, between D.M. Blei and M.I. Jordan, on 'Statistical modeling of biomedical corpora' (BMC Bioinformatics 2006), applying Latent Dirichlet Allocation to biomedical text — real co-authorship inside the statistical-learning tradition, though a single paper. The LBNL tech-transfer honour for SeeqPod is corroborated by Berkeley Lab's own history site rather than by his page. What is absent is any transformer-era record: no paper, patent or public model on attention, pretraining, scaling or alignment, and academic citation is modest (54 citations, h-index 1). Pass 2's scores are closer to the evidence, but its foundations of 13 over-reads a patent portfolio plus one co-authored paper as graduate-level mathematics training, and there is no verified degree beyond an undergraduate one.\n\nFranks' verifiable work is pre-word2vec distributional-semantics lineage — LSI/vector-space text mining at X-MINE (2002 filing) and LBNL, the UC 'variable length data vectors' relationship-network patents (2005 priority, US7987191B2/US9026543B2), and the 2006 BMC Bioinformatics LDA application with Blei and Jordan — but no source shows any of it as a named building block cited by or built into GPT/Claude/Gemini/Llama technical reports, so frontier_founder is low: it is genuine lineage-adjacent representation-learning work, not a component the frontier stack demonstrably descends from. His language-modeling record is real and long-running but front-loaded and intermittent: a strong LSI/vector-space/LDA period ~2002-2008, then largely retrieval/product and tokenized-dataset work (SeeqPod, Vectorspace AI), giving perhaps 6-8 years of continuous hands-on LM research inside a ~20-year span. He is a verifiable technical/scientific founder — named inventor on the patents his companies ran on (X-MINE, UC-spun SeeqPod corroborated by Berkeley Lab, Intertrust) across roughly 2002-2017 and founder-CEO of multiple such companies over ~15 years — supporting a mid-range scientific_founder score.","evidence":[{"claim":"Inventor on US20030204496A1 'Inter-term relevance analysis for large libraries' (inventors Sandip Ray, Raf Podowski, Kasian Franks; assignee X-MINE Inc.; filed/priority 29 April 2002) — second-order term-proximity correlation to detect previously unidentified relationships in large text libraries; v","source_url":"https://patents.google.com/patent/US20030204496A1/en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Continuation US9026543B2 'System and method for generating a relationship network' (inventors Kasian Franks, Cornelia A. Myers, Raf M. Podowski; assignee University of California San Diego; priority 6 June 2005, granted 5 May 2015) — abstract states the system 'generates variable length data vectors","source_url":"https://patents.google.com/patent/US9026543B2/en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First-named inventor on US7987191B2 'System and method for generating a relationship network' (priority June 2005, granted 26 July 2011), assignee The Regents of the University of California","source_url":"https://patentimages.storage.googleapis.com/pdfs/US7987191.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Inventor on US9600533B2 'Matching and recommending relevant videos and media to individual search engine results' (inventors Kasian Franks, Raf Podowski; assignee Intertrust Technologies Corp; priority 8 November 2006, granted 21 March 2017)","source_url":"https://patents.google.com/patent/US9600533B2/en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Crossref record confirms author order D.M. Blei, K. Franks, M.I. Jordan, I.S. Mian on 'Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span', BMC Bioinformatics 2006 — he is second author between the author of Latent Dirichl","source_url":"https://api.crossref.org/works/10.1186/1471-2105-7-250","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Berkeley Lab's own history site records that Kasian Franks and colleagues created SeeqPod, 'a search engine technology company whose roots were in a patent at the Lab' — independent corroboration of the LBNL tech-transfer claim","source_url":"https://history.lbl.gov/Publications/today/2007/Dec/13-Thu/tech-transfer-jump.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named first inventor on US Patent 7,987,191 B2 'System and Method for Generating a Relationship Network' (filed Nov 2007, continuation of PCT filed Jun 2006 / provisional Jun 2005, granted Jul 26 2011), assignee The Regents of the University of California, co-inventors Cornelia A. Myers and Raf M. P","source_url":"https://patentimages.storage.googleapis.com/pdfs/US7987191.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (with David Blei and Michael I. Jordan) of 'Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span', BMC Bioinformatics, 2006, applying Latent Dirichlet Allocation to biomedical text","source_url":"https://api.semanticscholar.org/graph/v1/paper/DOI:10.1186/1471-2105-7-250","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Recognized as a Lawrence Berkeley National Laboratory tech-transfer success story for SeeqPod at the 2007 Excellence in Technology Transfer Awards ceremony","source_url":"https://history.lbl.gov/Publications/today/2007/Dec/13-Thu/tech-transfer-jump.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Second author, after D.M. Blei and before M.I. Jordan, on 'Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span', BMC Bioinformatics 2006 - verified author order from the Crossref record","source_url":"https://api.crossref.org/works/10.1186/1471-2105-7-250","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"US9026543B2 abstract claims the system 'generates variable length data vectors to represent the relationships between the terms' (Franks, Myers, Podowski; UC San Diego; June 2005 priority) — pre-word2vec vector-space text representation, lineage-adjacent but not cited by frontier model reports","source_url":"https://patents.google.com/patent/US9026543B2/en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (2nd, between Blei and Jordan) of 'Statistical modeling of biomedical corpora' applying LDA to biomedical text, BMC Bioinformatics 2006 — verifiable early statistical-language-modeling record","source_url":"https://api.crossref.org/works/10.1186/1471-2105-7-250","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Berkeley Lab history site records Franks and colleagues founded SeeqPod, 'a search engine technology company whose roots were in a patent at the Lab' — corroborates technical-founder role built on his own patents","source_url":"https://history.lbl.gov/Publications/today/2007/Dec/13-Thu/tech-transfer-jump.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First-named inventor on US7987191B2 / continuation US9026543B2 'System and method for generating a relationship network' (priority 6 June 2005, Regents of the University of California) — 'generates variable length data vectors to represent the relationships between the terms'.","source_url":"https://patents.google.com/patent/US9026543B2/en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Second author (between D.M. Blei and M.I. Jordan) on 'Statistical modeling of biomedical corpora' (BMC Bioinformatics 2006), an LDA application to biomedical text.","source_url":"https://api.crossref.org/works/10.1186/1471-2105-7-250","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder-CEO of SeeqPod, a search-technology company rooted in his Berkeley Lab patent, recognized as an LBNL tech-transfer success story (2007) — technical founder with authored core IP.","source_url":"https://history.lbl.gov/Publications/today/2007/Dec/13-Thu/tech-transfer-jump.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"community","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":40},{"id":52,"slug":"johannes-hagemann","name":"Johannes Hagemann","title":"Co-founder & Head of Research","company":"Prime Intellect","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/johannes-hagemann.jpg","score":49,"tier":"technically_fluent","dimensions":{"foundations":8,"vector_embeddings":3,"transformers_lm":15,"frontier_founder":8,"lm_domain_depth":10,"hands_on_engineering":16,"industry_impact":10,"scientific_founder":10},"rubric_version":3,"weighted_score":49,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes identified him correctly and both discarded the dossier, which is matched throughout to a DESY X-ray nanoscience physicist of the same name (ORCID 0000-0003-2768-9496, born 1988, 261 works back to 1985) with no connection to Prime Intellect. The substantive disagreement is about how far back his record runs and how much it earns. Pass 1 found the earlier work and pass 2 missed it: I verified that he is first author of 'Efficient Parallelization Layouts for Large-Scale Distributed Model Training' (arXiv:2311.05610, November 2023), with Aleph Alpha co-founder Samuel Weinbach and Gerard de Melo among the co-authors, an ablation study of training configurations reporting 70.5% Model FLOPs utilisation on a Llama 13B — first-author work on LLM pretraining efficiency, done at a foundation-model company before Prime Intellect existed. Pass 2's first_verifiable_year of 2024 is therefore wrong and its foundations score partly rests on that error. On the Prime Intellect papers pass 2 is the more accurate reader: he is the final author of the INTELLECT-1 technical report (verified author order ending '...Max Ryabinin, Johannes Hagemann'), the first 10B-parameter model trained collaboratively across 14 nodes on 3 continents with 30 compute providers, and of INTELLECT-2, the first globally distributed RL run of a 32B reasoning model, plus senior author on OpenDiLoCo. Final-author position across the series is the senior-technical-lead slot, and the substance — ElasticDeviceMesh fault tolerance, a hybrid DiLoCo-FSDP2 implementation cutting communication bandwidth 400x, MFU figures, PRIME-RL and SHARDCAST — is exactly what the hands-on engineering anchor rewards, so pass 2's 17 is closer than pass 1's 15. What neither pass could establish is any degree, thesis or mathematics publication, and he has no record at all in embeddings or retrieval. His track record is also genuinely short (2023 onward), which the rubric says should lower rather than raise the depth-sensitive dimensions, so industry_impact stays mid-band despite the visibility of the INTELLECT series.\n\nHagemann's verifiable language-modeling record begins at Aleph Alpha in early 2022 and runs continuously through Prime Intellect (~4 years), with first-author LLM-pretraining-efficiency work (arXiv:2311.05610) and senior/final-author positions on OpenDiLoCo, INTELLECT-1 and INTELLECT-2 — real training-stack engineering but a short track record, placing lm_domain_depth in the low 3–8-year band. His lineage contribution is decentralized/low-communication distributed training (OpenDiLoCo, ElasticDeviceMesh, hybrid DiLoCo-FSDP2, PRIME-RL/SHARDCAST); this is published lineage/systems work, but it is a distinct paradigm from the centralized stacks GPT/Claude/Gemini/Llama descend from and is not a named building block their technical reports cite, so frontier_founder stays mid-low. As co-founder of Prime Intellect (end of 2023, ~2.5 years) he operates as a genuine scientific/technical founder — final-author (senior-technical-lead) on the INTELLECT series and author of the core distributed-training code the company runs on — but the tenure is under three years, holding scientific_founder just above the 8-year threshold band.","evidence":[{"claim":"First author of 'Efficient Parallelization Layouts for Large-Scale Distributed Model Training' (arXiv:2311.05610, Nov 2023) — verified author order Johannes Hagemann, Samuel Weinbach, Konstantin Dobler, Maximilian Schall, Gerard de Melo; reports 70.5% Model FLOPs utilization training a Llama 13B","source_url":"https://arxiv.org/abs/2311.05610","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Final (senior) author of the 'INTELLECT-1 Technical Report' (arXiv:2412.01152, Dec 2024) — verified author order ends 'Max Ryabinin, Johannes Hagemann'; first 10B-parameter LM trained collaboratively across 14 nodes on 3 continents with 30 compute providers, with ElasticDeviceMesh and a hybrid DiLoC","source_url":"https://arxiv.org/abs/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of 'OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training' (arXiv:2407.07852, July 2024), trained across continents at 90-95% compute utilisation","source_url":"https://arxiv.org/abs/2407.07852","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Final author of 'INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning' (arXiv:2505.07291, May 2025), contributing PRIME-RL, TOPLOC rollout verification and SHARDCAST weight distribution","source_url":"https://arxiv.org/abs/2505.07291","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His Google Scholar profile (qlCqOBEAAAAJ) shows ~203 citations and h-index 8, with top works Intellect-1, OpenDiLoCo, Synthetic-1, Metagene-1, Intellect-2 and the parallelization-layouts paper — consistent with the arXiv record and distinct from the DESY physicist's profile","source_url":"https://scholar.google.com/citations?user=qlCqOBEAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hagemann joined Aleph Alpha (German foundation-model company) in early 2022 as an AI Research Engineer focused on large-scale parallelization and distributed-systems engineering for LLM training.","source_url":"https://nextomoro.com/johannes-hagemann/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hagemann is first author of 'Efficient Parallelization Layouts for Large-Scale Distributed Model Training' (arXiv:2311.05610, Nov 2023), with co-authors including Aleph Alpha co-founder Samuel Weinbach; selected for oral presentation at WANT@NeurIPS 2023, later published at COLM 2024.","source_url":"https://arxiv.org/abs/2311.05610","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hagemann co-founded Prime Intellect with Vincent Weisser at the end of 2023, building infrastructure for globally distributed LLM training and inference.","source_url":"https://hagemann.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hagemann is a listed author on Prime Intellect's OpenDiLoCo (arXiv:2407.07852), INTELLECT-1 technical report (arXiv:2412.01152, a 1-trillion-token LLM trained across 14 nodes / 30 compute providers on 3 continents), and INTELLECT-2 (arXiv:2505.07291, decentralized RL training of a 32B reasoning mode","source_url":"https://arxiv.org/pdf/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hagemann's real Google Scholar profile (qlCqOBEAAAAJ) shows 203 total citations, h-index 8, with top works being Intellect-1, OpenDiLoco, Synthetic-1, Metagene-1, Intellect-2, and the parallelization-layouts paper — consistent with the arXiv record.","source_url":"https://scholar.google.com/citations?user=qlCqOBEAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of OpenDiLoCo (arXiv:2407.07852, 2024), an open framework for globally distributed low-communication training — the decentralized-training lineage his work extends, distinct from centralized frontier-model stacks","source_url":"https://arxiv.org/abs/2407.07852","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Prime Intellect with Vincent Weisser at the end of 2023, building distributed LLM training/inference infrastructure; personally authored core distributed-training research (final author on INTELLECT-1, arXiv:2412.01152)","source_url":"https://hagemann.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Senior author of OpenDiLoCo, an open-source framework for globally distributed low-communication training — decentralized-training lineage work, not a centralized-frontier building block","source_url":"https://arxiv.org/abs/2407.07852","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Prime Intellect with Vincent Weisser at the end of 2023 and is final/senior author on INTELLECT-1 and INTELLECT-2 technical reports — the senior-technical-founder slot","source_url":"https://arxiv.org/abs/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":41},{"id":32,"slug":"kai-fu-lee","name":"Kai-Fu Lee","title":"Chairman & CEO","company":"01.AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Kai-Fu_Lee","image_url":"/api/v1/ceo-ai-leaderboard/portrait/kai-fu-lee.jpg","score":49,"tier":"technically_fluent","dimensions":{"foundations":15,"vector_embeddings":8,"transformers_lm":10,"frontier_founder":4,"lm_domain_depth":8,"hands_on_engineering":14,"industry_impact":16,"scientific_founder":4},"rubric_version":3,"weighted_score":49,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Lee earned a PhD in Computer Science from Carnegie Mellon (1988) under Raj Reddy, and his doctoral dissertation created SPHINX, the first large-vocabulary speaker-independent continuous speech-recognition system using statistical/neural methods — genuinely canonical, foundational statistical-ML work that predates but feeds into the modern deep-learning lineage (h-index 20-35 across OpenAlex/Semantic Scholar, confirmed). He built and led engineering/research organizations at Apple, SGI, Microsoft (founding director of Microsoft Research China/Asia), and Google (president of Google China) — senior technical leadership of real research labs, not just business roles. However his direct hands-on research output ends in the early 1990s; from the mid-1990s onward his record is executive/investor (Sinovation Ventures). In 2023 he founded 01.AI, which built and shipped the open-weight Yi series of LLMs — but Lee's personal authorship role on the Yi models themselves is not established (he leads the company; the model-building credit sits with 01.AI's research team). No specific vector-embeddings or transformer-authorship record was found; his direct contribution to the modern transformer/LM lineage is as an org-builder/funder rather than an author.\n\nLee's own verifiable technical output is late-1980s/early-1990s statistical speech recognition (SPHINX, HMM acoustic + n-gram language modeling), which sits in the broad statistical-NLP tradition but is NOT a named building block that today's transformer-based frontier models (GPT/Claude/Gemini/Llama) cite or descend from — his personal work is lineage-adjacent, not foundational to the frontier stack (frontier_founder low). His hands-on language-modeling record is real but non-continuous: roughly 6 years of statistical ASR/LM research (~1988–1994) using n-gram LMs, then ~30 years as an executive/investor, then a 2023+ return as founder-CEO of 01.AI (Yi open-weight LLMs) — leadership rather than personally authored model research, so depth×duration does not clear the continuous-record bar. On scientific_founder, 01.AI's model science and code are produced by its research team; Lee operates as founder-CEO/financier (Sinovation Ventures is a VC), matching the 'founder of an AI company whose science was done by others' anchor rather than a founder-CTO/Chief-Scientist authoring the core system.","evidence":[{"claim":"PhD Computer Science, Carnegie Mellon University (1988), advisor Raj Reddy; doctoral dissertation built SPHINX, a pioneering large-vocabulary speaker-independent continuous speech-recognition system","source_url":"https://en.wikipedia.org/wiki/Kai-Fu_Lee","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founding director of Microsoft Research China (later Microsoft Research Asia), 1998-2000; president of Google China, 2005-2009","source_url":"https://en.wikipedia.org/wiki/Kai-Fu_Lee","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded 01.AI in March 2023; the company released the open-weight Yi-34B model in November 2023","source_url":"https://www.turingpost.com/p/01ai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar lm2nBYkAAAAJ: 13,143 citations, h-index 41, i10 65; top works are 'Readings in speech recognition' (1990), 'Speaker-independent phone recognition using hidden Markov models' (1989), 'Automatic speech recognition: the development of the SPHINX system' (1988), 'Speaker adaptation throug","source_url":"https://scholar.google.com/citations?user=lm2nBYkAAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS Computer Science Columbia 1983 summa cum laude; PhD Computer Science CMU 1988 under Raj Reddy, dissertation on large-vocabulary speaker-independent continuous speech recognition (SPHINX); Apple 1990-1996 (PlainTalk, Casper, GalaTea); SGI 1996-1998; founding director of Microsoft Research China/As","source_url":"https://en.wikipedia.org/wiki/Kai-Fu_Lee","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5064910846: 55 works, earliest 1988, topics are speech recognition and synthesis, speech and audio processing, NLP; affiliations include Microsoft, Google, Microsoft Research Asia, Apple","source_url":"https://api.openalex.org/authors/A5064910846","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'The SPHINX-II speech recognition system: an overview', Computer Speech & Language 1993","source_url":"https://doi.org/10.1006/csla.1993.1007","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'The development of a world class Othello program', Artificial Intelligence 1990 (Lee & Mahajan) — early search/evaluation-function AI work","source_url":"https://doi.org/10.1016/0004-3702(90)90068-b","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lee's PhD dissertation built SPHINX, a large-vocabulary speaker-independent continuous speech-recognition system using HMMs with statistical/n-gram language modeling — his canonical technical work, dated 1988","source_url":"https://en.wikipedia.org/wiki/Kai-Fu_Lee","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The SPHINX-II speech recognition system overview (Computer Speech & Language, 1993) marks the tail of his hands-on statistical speech/LM research before he moved into executive roles","source_url":"https://doi.org/10.1006/csla.1993.1007","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lee founded 01.AI in 2023, which released the open-weight Yi model series; he leads the company as founder-CEO while the model-building credit sits with 01.AI's research team","source_url":"https://www.turingpost.com/p/01ai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded 01.AI in 2023; the company (not Lee personally) built and released the open-weight Yi LLM series — Lee leads as founder-CEO with the model research credited to the team","source_url":"https://www.turingpost.com/p/01ai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":42},{"id":55,"slug":"jacob-steeves","name":"Jacob Steeves","title":"Co-founder","company":"Bittensor / Opentensor Foundation","sector":"crypto","profile_url":null,"image_url":null,"score":46,"tier":"technically_fluent","dimensions":{"foundations":7,"vector_embeddings":8,"transformers_lm":12,"frontier_founder":4,"lm_domain_depth":10,"hands_on_engineering":14,"industry_impact":9,"scientific_founder":11},"rubric_version":3,"weighted_score":46,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"This is the batch's clearest case of one pass simply missing the record. Pass 1 scored him from a dossier that found a single 2025 OpenAlex work and concluded his first verifiable year was 2025 with essentially no publication history; pass 2 pulled the Semantic Scholar author record and found six more papers, all of which I verified. He is first author of 'BitTensor: An Intermodel Intelligence Measure' (2020) and a named co-author of 'BitTensor: A Peer-to-Peer Intelligence Market' (Rao, Steeves, Shaabana, Attevelt, McAteer, arXiv:2003.03917, March 2020), which prices model contributions by the information-theoretic value one neural network adds to another's representations — though the arXiv version was withdrawn by the authors as incomplete and obsolete, which caps the credit it earns. His strongest credential is co-authorship of 'BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model' (arXiv:2309.11568, 2023) with the Cerebras team, where he appears as 'Jacob Robert Steeves': that is direct participation in pretraining a real 3B-parameter open-weights language model with 8K context, not commissioning one, and it is what moves transformers_lm decisively out of pass 1's band of 4. His 2025 paper 'Incentivizing Permissionless Distributed Learning of LLMs' introduces Gauntlet, filtering peers by loss improvement attributable to each peer's pseudo-gradient, and reports training a 1.2B-parameter model that way — genuine distributed-training engineering. Against this: no verifiable degree, no thesis, no foundational mathematics record, a small citation footprint (43 citations, h-index 3), and work that is predominantly mechanism design over machine learning rather than contributions to attention, embeddings or scaling themselves. Pass 2's scores are directionally right but uniformly a notch generous for a record this thin in citations and absent any credentialed mathematics.\n\nSteeves' record is real but sits outside the frontier lineage: co-authoring BTLM-3B-8K (arXiv:2309.11568, 2023) with the Cerebras team is genuine participation in pretraining an open-weights LM, and the 2020 BitTensor papers plus the 2025 Gauntlet permissionless-training work give him roughly six continuous years (2020→2026) touching neural language modeling and distributed pretraining — but none of it is a named building block (architecture, attention, tokenizer, optimizer, scaling result) that GPT/Claude/Gemini/Llama-class models descend from or cite, so frontier_founder stays low. His strongest v3 dimension is scientific_founder: he is a co-founder of Bittensor/Opentensor Foundation operating as its technical/scientific founder, personally authoring the core whitepaper-lineage papers and incentive-mechanism code the network runs on, over about six years — squarely in the 3–8-year technical-founder band. lm_domain_depth reflects ~6 years of a verifiable but mechanism-design-heavy LM record rather than a decade-plus of pure language-modeling research.","evidence":[{"claim":"Semantic Scholar author 1557385586 (name_exact, 1 candidate) lists 7 papers, 43 citations, h-index 3: 'Incentivizing Permissionless Distributed Learning of LLMs' (2025, ICDAI), 'Poster: Solving the Free-rider Problem in Bittensor' (CCS 2024), 'BTLM-3B-8K' (2023), 'BitTensor: An Intermodel Intelligen","source_url":"https://api.semanticscholar.org/graph/v1/author/1557385586/papers?fields=title,year,venue,externalIds,authors,citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model' (arXiv:2309.11568, 2023) — author list verified as including 'Jacob Robert Steeves' with the Cerebras team (Dey, Soboleva, Al-Khateeb, Vassilieva, Hestness); the paper introduces the Bittensor Language Model, a 3B-parameter","source_url":"https://arxiv.org/abs/2309.11568","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'BitTensor: A Peer-to-Peer Intelligence Market' (arXiv:2003.03917, submitted 9 March 2020, authors Yuma Rao, Jacob Steeves, Ala Shaabana, Daniel Attevelt, Matthew McAteer), proposing peers ranking one another through trained neural networks; withdrawn by the authors as incomplete and ob","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Incentivizing Permissionless Distributed Learning of LLMs' (arXiv:2505.21684, 2025), introducing Gauntlet for permissionless distributed pretraining, reporting a 1.2B-parameter model trained this way","source_url":"https://arxiv.org/abs/2505.21684","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar clean match: 7 papers, 43 citations, h-index 3, name_exact match with 1 candidate (low homonym risk)","source_url":"https://www.semanticscholar.org/author/Jacob-Steeves/1557385586","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bittensor's 2021 founding whitepaper (peer-ranking/incentive mechanism for distributed ML) was published under the pseudonym 'Yuma Rao,' a collective pseudonym publicly reported to include the founding team","source_url":"https://bittensor.com/whitepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PubMed search for Steeves J with Bittensor/Opentensor affiliation returned 227 results flagged as high homonym risk — not usable as evidence for this person","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Steeves+J","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'BitTensor: A Peer-to-Peer Intelligence Market' (Rao, Steeves, Shaabana, Attevelt, McAteer, arXiv:2003.03917, March 2020), proposing peers pricing each other's model contributions; subsequently withdrawn by the authors as incomplete","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 1557385586 (Jacob Steeves, name_exact, 1 candidate): 7 papers, 43 citations, h-index 3, including 'BitTensor: An Intermodel Intelligence Measure' (2020), 'Poster: Solving the Free-rider Problem in Bittensor' (CCS 2024), 'Stake-Based Consensus for Utility Scoring' and 'Incenti","source_url":"https://api.semanticscholar.org/graph/v1/author/1557385586/papers?fields=title,year,venue,externalIds,authors,citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Steeves co-authored 'BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model' (arXiv:2309.11568, 2023) as 'Jacob Robert Steeves' with the Cerebras team — direct participation in pretraining a real 3B-parameter open-weights LM, but not a component frontier models build on","source_url":"https://arxiv.org/abs/2309.11568","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First-author/co-author of the 2020 BitTensor papers (arXiv:2003.03917) establishing his ~6-year LM/distributed-ML lineage from 2020 to the 2025 Gauntlet distributed-LLM-training work","source_url":"https://arxiv.org/abs/2505.21684","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Bittensor / Opentensor Foundation operating as technical/scientific founder, authoring the core incentive-mechanism research and code the network runs on (whitepaper under the 'Yuma Rao' collective pseudonym)","source_url":"https://bittensor.com/whitepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of BTLM-3B-8K ('Jacob Robert Steeves', arXiv:2309.11568, 2023), a real 3B-parameter open-weights LM trained with the Cerebras team — participation in pretraining lineage, but a derivative model frontier labs do not build on","source_url":"https://arxiv.org/abs/2309.11568","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First-author/co-author of the 2020 BitTensor papers (arXiv:2003.03917) and 2025 'Incentivizing Permissionless Distributed Learning of LLMs' (arXiv:2505.21684), giving a ~2020-2026 continuous record in distributed ML / LLM training","source_url":"https://arxiv.org/abs/2505.21684","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bittensor's founding whitepaper (peer-ranking/incentive mechanism for distributed ML) published under the collective pseudonym 'Yuma Rao' reported to include the founding team, supporting his role as a technical founder authoring the network's core research","source_url":"https://bittensor.com/whitepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":43},{"id":58,"slug":"ben-goertzel","name":"Ben Goertzel","title":"CEO","company":"SingularityNET / ASI Alliance","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Ben_Goertzel","image_url":"/api/v1/ceo-ai-leaderboard/portrait/ben-goertzel.jpg","score":45,"tier":"technically_fluent","dimensions":{"foundations":15,"vector_embeddings":7,"transformers_lm":5,"frontier_founder":4,"lm_domain_depth":7,"hands_on_engineering":12,"industry_impact":10,"scientific_founder":14},"rubric_version":3,"weighted_score":45,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Goertzel holds a genuine PhD in mathematics from Temple University (1989, advisor Avi Lin), confirmed via Mathematics Genealogy Project and his own institutional record, and has a 35+ year publication record (417 works, h-index 32-44 depending on source) with real academic faculty positions (University of Nevada Las Vegas, City University of New York, Xiamen University). However his core research lineage is symbolic AI, probabilistic logic networks, and cognitive architectures (the OpenCog / OpenCog Hyperon project) — a lineage distinct from, and largely predating or running parallel to, the vector-embedding and transformer/LM lineage this rubric targets; his post-2017 transformer-adjacent work (e.g. 'Guiding symbolic natural language grammar induction via transformer-based sequence probabilities', AGI 2020) integrates transformers into symbolic systems rather than contributing to core transformer/LM research. He founded SingularityNET via a 2017 public ICO ($36M in one minute), a legitimate public token sale with no evidence of family/inherited funding. Strong math foundations and long hands-on research career, but limited direct contribution to the specific embeddings/transformer lineage.\n\nGoertzel's own research lineage is symbolic/cognitive-architecture AGI (OpenCog, Probabilistic Logic Networks, OpenCog Hyperon/MeTTa), which runs parallel to — not into — the attention/transformer/scaling/RLHF stack that GPT/Claude/Gemini/Llama descend from; his transformer-adjacent work (e.g. 'Guiding symbolic natural language grammar induction via transformer-based sequence probabilities,' AGI 2020) consumes transformers inside symbolic systems rather than contributing a foundational building block those models cite, so frontier_founder is low. He has a genuine but adjacent and intermittent language record — link-grammar work, the OpenCog unsupervised language-learning project, grammar induction, and clinical/text mining — which is real NLP but not the statistical/neural language-modeling lineage the dimension targets (~8). He is, however, an unambiguous scientific/technical founder for roughly 25 years — Webmind/Intelligenesis (late 1990s), Novamente (2001), Biomind, Chief Scientist at Hanson Robotics, and founder-CEO of SingularityNET (2017) — personally authoring the core research and architecture (OpenCog Hyperon), though those companies' core is AGI/cognitive architecture rather than language modeling specifically, which tempers scientific_founder short of the top anchor.","evidence":[{"claim":"PhD in Mathematics, Temple University, 1989, doctoral advisor Avi Lin","source_url":"https://goertzel.org/bio.htm","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"417 works, 4,672+ citations, h-index 32 per OpenAlex; chief architect of the OpenCog symbolic/cognitive-architecture project","source_url":"https://en.wikipedia.org/wiki/Ben_Goertzel","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'Artificial General Intelligence: Concept, State of the Art, and Future Prospects' (2014) and editor of the 'Artificial General Intelligence' book series","source_url":"https://doi.org/10.2478/jagi-2014-0001","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded SingularityNET in 2017 (with Simone Giacomelli and David Hanson); the AGIX token ICO raised $36M in a public sale, not family/private funding","source_url":"https://www.nextbigfuture.com/2017/12/ai-researcher-ben-goertzel-launches-singularitynet-marketplace-and-agi-coin-cryptocurrency.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD in mathematics, Temple University, 1990, under Avi Lin, dissertation 'A Multilevel Approach to Global Optimization'","source_url":"https://www.genealogy.math.ndsu.nodak.edu/id.php?id=40053","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Computer scientist and AI researcher who helped popularize the term artificial general intelligence; leading developer of the OpenCog framework; founder and CEO of SingularityNET; former Chief Scientist at Hanson Robotics, whose Sophia claims were criticised by researchers including Yann LeCun; left","source_url":"https://en.wikipedia.org/wiki/Ben_Goertzel","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First-authored machine-learning bioinformatics research: 'Identifying the genes and genetic interrelationships underlying the impact of calorie restriction on maximum lifespan: an artificial intelligence-based approach' (Rejuvenation Res, 2008) and related chronic fatigue syndrome data-mining papers","source_url":"https://pubmed.ncbi.nlm.nih.gov/18729806/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Lead author of 'OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond' (arXiv 2310.18318, 2023), describing the AtomSpace/MeTTa neural-symbolic architecture","source_url":"https://arxiv.org/abs/2310.18318","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Guiding symbolic natural language grammar induction via transformer-based sequence probabilities' (AGI 2020) integrates transformer outputs into symbolic grammar induction rather than contributing to core LM research","source_url":"https://link.springer.com/chapter/10.1007/978-3-030-52152-3_16","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder and CEO of SingularityNET (2017) and former Chief Scientist of Hanson Robotics; leading developer of the OpenCog framework — a decades-long record as a scientific/technical founder personally authoring the core research and code","source_url":"https://en.wikipedia.org/wiki/Ben_Goertzel","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD in mathematics (Temple, dissertation on global optimization) and 35+ year publication record (417 works, h-index 32), rooted in symbolic AI/AGI/cognitive architecture rather than language modeling","source_url":"https://goertzel.org/bio.htm","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenCog Hyperon (AtomSpace/MeTTa) is a neural-symbolic AGI framework, not part of the transformer/LM lineage frontier models are built on","source_url":"https://arxiv.org/abs/2310.18318","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder and CEO of SingularityNET (2017) and leading developer of the OpenCog framework; former Chief Scientist at Hanson Robotics","source_url":"https://en.wikipedia.org/wiki/Ben_Goertzel","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Goertzel founded Novamente LLC (c.2001) as an AGI research/engineering company, evidencing multi-decade technical-founder role","source_url":"https://goertzel.org/bio.htm","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Transformer sequence probabilities used to guide symbolic grammar induction — transformers consumed by, not contributed to, his symbolic NLP work","source_url":"https://doi.org/10.1007/978-3-030-52152-3_16","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":44},{"id":110,"slug":"bob-van-luijt","name":"Bob van Luijt","title":"Co-founder & CEO","company":"Weaviate","sector":"general","profile_url":null,"image_url":null,"score":45,"tier":"technically_fluent","dimensions":{"foundations":4,"vector_embeddings":14,"transformers_lm":6,"frontier_founder":4,"lm_domain_depth":8,"hands_on_engineering":14,"industry_impact":12,"scientific_founder":13},"rubric_version":3,"weighted_score":45,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Van Luijt has no formal computer science or mathematics degree — his education is in music (ArtEZ, Berklee College of Music) plus a Harvard Business School executive program, and he is a self-taught programmer who started a software company at 15, so foundations scores low on credentialed math/stats grounds. He is, however, the verified original architect and CEO of Weaviate, an open-source vector database he started in March 2016 — this is real, personally-built, shipped infrastructure squarely in the 'vector databases & search' portion of the vector_embeddings dimension, predating the post-2022 vector-DB boom by several years, which supports meaningful vector_embeddings and hands_on_engineering credit despite the lack of formal training. Weaviate integrates embedding models and GraphQL-based semantic search rather than Van Luijt personally authoring embedding/transformer research papers, so transformers_lm is scored low-moderate for applied systems integration rather than research authorship. His Semantic Scholar record (3 papers, 38 citations) suggests some light technical writing but not a research career. Industry impact is real (Weaviate is a widely-used production vector database, $67M+ raised) but modest relative to labs that produced canonical LM research.\n\nVan Luijt's contribution is Weaviate, a vector database that consumes third-party embeddings and serves RAG/semantic-search pipelines — it is a downstream consumer of frontier models, not a building block they descend from; no transformer/attention/embedding-training/scaling/alignment work of his is cited in frontier-model technical reports, so frontier_founder is low (he integrates and benchmarks GPT/Gemini/Llama via StructuredRAG, no foundational component). His ~10 continuous years (2016→present) sit in vector-space retrieval and dense/hybrid search, which is adjacent to language modeling rather than core LM research (no n-gram/neural-LM/seq2seq/pretraining authorship), giving a real but non-core lm_domain_depth record. He is, however, a genuine founder-architect: he personally started Weaviate as open source in March 2016 (SeMI Technologies, later Weaviate) and wrote its core, operating as founder-CEO/technical founder of a company whose core IS these vector-search systems for ~9-10 years, which supports a solid scientific_founder score.","evidence":[{"claim":"Started the open-source vector search engine Weaviate in March 2016, predating the post-ChatGPT vector-database wave","source_url":"https://en.wikipedia.org/wiki/Bob_van_Luijt","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar profile: 3 papers, 38 citations, h-index 1","source_url":"https://www.semanticscholar.org/author/2030042874","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Weaviate is a Go vector database using HNSW approximate nearest-neighbour search, hybrid semantic + BM25 keyword search, vector compression/quantization, pluggable vectorizers (OpenAI, Cohere, HuggingFace), RAG and reranking; ~16.8k GitHub stars","source_url":"https://github.com/weaviate/weaviate","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'StructuredRAG: JSON Response Formatting with Large Language Models' (2024), benchmarking Gemini 1.5 Pro and Llama 3 8B-instruct on structured output following across 24 experiments","source_url":"https://arxiv.org/abs/2408.11061","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record lists only three works: IRPAPERS (2026, visual document benchmark for scientific retrieval and QA), StructuredRAG (2024), and a 2020 Journal of Creating Value interview — 3 papers, 38 citations, h-index 1","source_url":"https://api.semanticscholar.org/graph/v1/author/2030042874/papers?fields=title,year,venue,externalIds,authors&limit=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Studied jazz and electronic composition at ArtEZ and Berklee College of Music (no technical degree); launched Weaviate as an open-source project in March 2016 and founded SeMI Technologies, later renamed Weaviate; authored an IEEE Software article (2020) 'Bringing Semantic Knowledge Graph Technology","source_url":"https://en.wikipedia.org/wiki/Bob_van_Luijt","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Weaviate, an open-source vector database, started as an open-source project in March 2016","source_url":"https://en.wikipedia.org/wiki/Bob_van_Luijt","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Weaviate is a Go vector database (HNSW ANN, hybrid semantic+BM25, quantization, pluggable OpenAI/Cohere/HuggingFace vectorizers, RAG/reranking) — a retrieval layer that consumes external embedding/LLM models rather than training them","source_url":"https://github.com/weaviate/weaviate","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His only LLM-facing paper, StructuredRAG (2024), benchmarks Gemini 1.5 Pro and Llama 3 8B-instruct on structured output — applying frontier models, not contributing a foundational component; Semantic Scholar shows 3 papers, 38 citations, h-index 1","source_url":"https://arxiv.org/abs/2408.11061","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder, original architect and CEO of Weaviate, an open-source vector database he started as a project in March 2016 (founded SeMI Technologies, later renamed Weaviate)","source_url":"https://en.wikipedia.org/wiki/Bob_van_Luijt","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Weaviate is a Go vector database consuming pluggable frontier-model vectorizers (OpenAI, Cohere, HuggingFace) for RAG/reranking — downstream retrieval infrastructure, not part of the frontier model training/inference stack","source_url":"https://github.com/weaviate/weaviate","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata lists occupation programmer/inventor/founder with notable work Weaviate; Semantic Scholar shows only 3 papers, 38 citations, h-index 1 (applied RAG/benchmark writing, no LM research career)","source_url":"https://www.wikidata.org/wiki/Q25346162","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.75,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":45},{"id":109,"slug":"jeff-huber","name":"Jeff Huber","title":"Co-founder & CEO","company":"Chroma","sector":"general","profile_url":null,"image_url":null,"score":43,"tier":"informed_operator","dimensions":{"foundations":5,"vector_embeddings":15,"transformers_lm":6,"frontier_founder":4,"lm_domain_depth":6,"hands_on_engineering":14,"industry_impact":12,"scientific_founder":10},"rubric_version":3,"weighted_score":43,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes correctly rejected the dossier, which is worthless for this person: its only identity anchor, Wikidata Q115655617, is the CEO of Home Instead Inc., a home-care company, and its Semantic Scholar match (24 papers, 4 candidates, no affiliation) cannot be tied to him — there is also a separate well-known Jeff Huber who was a Google SVP and Grail co-founder, so none of those papers are counted. Identification is via GitHub and Chroma's own materials. The dispute is about how hands-on he is, and pass 2 supplied the measurement pass 1 lacked: GitHub's commit search API attributes 434 commits in chroma-core/chroma to his account jeffchuber, which I re-verified directly. That is the difference between managing builders and being one, and it settles hands_on_engineering and vector_embeddings in pass 2's favour — Chroma is an AI-native open-source embeddings database, ~29k stars, whose whole purpose is storing and querying embeddings for retrieval, and he is a substantial contributor to it, not merely its spokesperson. Chroma also publishes retrieval research (Embedding Adapters, Evaluating Chunking Strategies, Generative Benchmarking, Context Rot) that sits in the dense-retrieval and long-context line, but the reports do not list him as an author, so that counts as leading a group producing relevant work rather than personal authorship. Where pass 2 goes too far is foundations and transformers_lm: it scored both at 8 while its own rationale concedes no peer-reviewed publication, no verifiable degree and no personal contribution to attention, pretraining or scaling. An 8 sits in the 'strong graduate training' band, which nothing in the record supports; the rubric's instruction is to score lower when unsure. Prior work at Standard Cyborg (3D scanning and computer vision) is real applied engineering but outside the lineage.\n\nChroma is a vector/embeddings database — RAG retrieval infrastructure that is USED alongside frontier models (GPT/Claude/Gemini/Llama), not a method, architecture, dataset, optimizer or objective those models descend from or cite in their technical reports, so his frontier lineage is that of an application-layer consumer, not a foundational building block. His verifiable language-modeling record is short and adjacent: he co-founded Chroma in October 2022 (~4 years) and the work is embedding storage/dense retrieval and long-context evaluation rather than personal statistical/neural LM research; prior work at Standard Cyborg was 3D scanning/computer vision, outside the lineage. Where he does score is as a scientific/technical founder: GitHub's commit API attributes 434 commits in chroma-core/chroma to his account jeffchuber, so he is a co-founder who personally builds the product, but the verifiable duration in this field is only ~4 years and Chroma's published retrieval research (Embedding Adapters, Context Rot) does not list him as an author.","evidence":[{"claim":"GitHub's commit search API attributes 434 commits in chroma-core/chroma to author jeffchuber — verified total_count directly","source_url":"https://api.github.com/search/commits?q=author:jeffchuber+repo:chroma-core/chroma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub user jeffchuber is Jeff Huber of San Francisco, associated with the StandardCyborg organization, with chroma-core/chroma ('Search infrastructure for AI', ~29k stars) pinned to his profile","source_url":"https://github.com/jeffchuber","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chroma is an AI-native open-source embeddings database founded by Jeff and Anton to store and query embeddings with filtering for embedding-based document retrieval","source_url":"https://www.trychroma.com/blog/seed","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"chroma-core/chroma repository created 2022-10-05, ~29k stars, primary language Rust","source_url":"https://api.github.com/repos/chroma-core/chroma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chroma publishes technical retrieval research including Embedding Adapters (2024), Evaluating Chunking Strategies for Retrieval (2024), Generative Benchmarking (2025) and Context Rot (2025); Huber is not listed as an author on these reports","source_url":"https://www.trychroma.com/research","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's Wikidata match Q115655617 is 'President and Chief Executive Officer - Home Instead Inc.', a home-care company unrelated to Chroma — a clear homonym","source_url":"https://www.wikidata.org/wiki/Q115655617","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chroma is described as 'Search infrastructure for AI', an open-source embedding database; Jeff Huber's GitHub profile (jeffchuber) has it pinned with ~29k stars, and lists 34 public repositories and active contribution badges (Pull Shark, Pair Extraordinaire)","source_url":"https://github.com/jeffchuber","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jeff Huber's GitHub profile lists prior affiliation with the organization StandardCyborg, a 3D-scanning/computer-vision company","source_url":"https://github.com/jeffchuber","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Dossier's Wikidata match for 'Jeff Huber' (Q115655617) is labeled 'President and Chief Executive Officer - Home Instead Inc.', a senior home-care company unrelated to Chroma or AI — a clear homonym, not this person","source_url":"https://www.wikidata.org/wiki/Q115655617","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Dossier's Semantic Scholar match for 'Jeff Huber' shows 24 papers / 138 citations / h-index 2 with 4 candidates and no affiliation data, an unresolved identity match not corroborated as this Jeff Huber by any independent source found","source_url":"https://www.semanticscholar.org/author/40441754","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chroma is an AI-native open-source embeddings database co-founded by Jeff Huber and Anton to store and query embeddings for document retrieval — retrieval infrastructure used with LLMs, not a component frontier models are trained from","source_url":"https://www.trychroma.com/blog/seed","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"chroma-core/chroma repository created 2022-10-05, placing Huber's verifiable in-field (embeddings/language-modeling-adjacent) founding at roughly four years","source_url":"https://api.github.com/repos/chroma-core/chroma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jeff Huber's GitHub profile lists prior affiliation with StandardCyborg, a 3D-scanning/computer-vision company outside the language-modeling lineage","source_url":"https://github.com/jeffchuber","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub commit search attributes 434 commits in chroma-core/chroma to author jeffchuber, evidencing hands-on technical-founder code authorship","source_url":"https://api.github.com/search/commits?q=author:jeffchuber+repo:chroma-core/chroma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"chroma-core/chroma repository was created 2022-10-05, fixing the first verifiable year of Huber's embeddings/retrieval work","source_url":"https://api.github.com/repos/chroma-core/chroma","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":46},{"id":61,"slug":"ben-fielding","name":"Ben Fielding","title":"Co-founder & CEO","company":"Gensyn","sector":"crypto","profile_url":null,"image_url":null,"score":41,"tier":"informed_operator","dimensions":{"foundations":12,"vector_embeddings":4,"transformers_lm":10,"frontier_founder":4,"lm_domain_depth":4,"hands_on_engineering":13,"industry_impact":10,"scientific_founder":11},"rubric_version":3,"weighted_score":41,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The two passes split because each held half of the record. Pass 1 credited a doctoral-level evolutionary neural-architecture-search corpus but missed his 2025 language-model papers; pass 2 found the LM papers but refused to attribute the academic corpus to him, having failed to confirm identity. Both halves are his. The Google Scholar profile carrying the Northumbria evolutionary-NAS publications is titled 'Co-Founder of Gensyn' and is verified on a gensyn.ai email address, which resolves the identity question pass 2 could not: the PSO/GA neural-architecture-search and image-classification work with Li Zhang and Kamlesh Mistry (IEEE Transactions on Cybernetics 2017, IEEE Access, 2016-2020, 790 citations, h-index 7) is the same person. That corpus is genuine graduate-level optimization and statistical-learning work applied to deep networks, which supports foundations well above the practitioner band, though it is evolutionary search over CNN architectures for computer vision, not representation learning. Separately and verifiably he is a named co-author of 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (arXiv:2509.08721, 2025), introducing SAPO for decentralized RL post-training of language models, and of 'Verde: Verification via Refereed Delegation for Machine Learning Programs' (arXiv:2502.19405, 2025) on verifying delegated LLM inference and training — real, current authorship inside the post-training half of the lineage, which is why pass 1's transformers_lm of 4 is too low and pass 2's 12 slightly too generous for co-authored team papers with no architecture or scaling contribution. He has no work at all in vector embeddings, retrieval or vector search, so that dimension stays near the floor. Gensyn's core is verifiable distributed deep-learning compute, a genuine ML-systems company rather than an AI label, but it has produced no canonical result, so industry impact is mid-band.\n\nFielding co-founded Gensyn in 2020 and operates as founder-CEO with a computer-science PhD, personally authoring the company's core research (SAPO, arXiv:2509.08721; Verde, arXiv:2502.19405) — a genuine scientific/technical founder record of roughly six years, which is why scientific_founder sits in the mid-band; but Gensyn's core is decentralized ML training/verification infrastructure, not a building block that GPT/Claude/Gemini/Llama descend from, and neither 2025 paper is cited by or built into any frontier model report, so frontier_founder is low. His verifiable language-modeling record begins only in 2025 — his entire prior corpus (2016-2020) is evolutionary/PSO neural-architecture search for computer vision (image classification, facial-emotion recognition), which is deep-learning-adjacent but not language modeling — giving only about one year of continuous LM work, so lm_domain_depth is near the floor. No attention, architecture, embedding, scaling or alignment building block traces from him into the frontier stack.","evidence":[{"claim":"Google Scholar profile 'Ben Fielding', listed as Co-Founder of Gensyn with a verified email at gensyn.ai, carries the evolutionary/PSO neural-architecture-search publications (h-index 7, 790 citations; top works 'A micro-GA embedded PSO feature selection approach to intelligent facial emotion recogn","source_url":"https://scholar.google.com/citations?user=B9lV7zUAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Crossref record for 'A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition', IEEE Transactions on Cybernetics 2017, authors Kamlesh Mistry, Li Zhang, Siew Chin Neoh, Chee Peng Lim, Ben Fielding","source_url":"https://api.crossref.org/works/10.1109/tcyb.2016.2549639","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named co-author of 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (arXiv:2509.08721, 10 Sep 2025), introducing SAPO, a decentralized swarm-sampling policy-optimization method for RL post-training of language models; author list verified as including Ben Fieldin","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Verde: Verification via Refereed Delegation for Machine Learning Programs' (arXiv:2502.19405, 2025), on verifying LLM inference, fine-tuning and training delegated to untrusted compute; author list verified as Arun, St. Arnaud, Titov, Wilcox, Kolobaric, Brinkmann, Ersoy, Fielding, Bonn","source_url":"https://arxiv.org/abs/2502.19405","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gensyn's litepaper specifies a protocol for verifying distributed deep-learning compute via probabilistic proof-of-learning and graph-based pinpoint protocols","source_url":"https://docs.gensyn.ai/litepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Fielding holds a PhD in Computer Science from Northumbria University, completed Dec 2019, on evolutionary optimization of deep neural architectures","source_url":"https://iq.wiki/wiki/ben-fielding","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (verified gensyn.ai email) shows h-index 7, ~790 total citations, publications on evolutionary/PSO-based neural architecture search and image classification (2016-2020), co-authored with Li Zhang (Royal Holloway) and Kamlesh Mistry (Northumbria)","source_url":"https://scholar.google.com/citations?user=B9lV7zUAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Top-cited paper: 'A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition', IEEE Transactions on Cybernetics, 2016","source_url":"https://doi.org/10.1109/tcyb.2016.2549639","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (arXiv:2509.08721, 10 Sep 2025), introducing SAPO for decentralized RL post-training of language models.","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Fielding is a named co-author of 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (SAPO), a 2025 decentralized RL post-training method for language models — his first verifiable language-modeling work.","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Fielding co-authored 'Verde: Verification via Refereed Delegation for Machine Learning Programs' (2025), on verifying delegated LLM inference/training — decentralization/verification machinery, not a frontier-model architecture or scaling building block.","source_url":"https://arxiv.org/abs/2502.19405","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Fielding is co-founder of Gensyn, a distributed-ML-compute protocol company whose litepaper he shapes technically (proof-of-learning, graph-based pinpoint verification); he holds a PhD in CS (Northumbria, 2019) and co-authors the company's papers, establishing a genuine technical-founder role over ~","source_url":"https://docs.gensyn.ai/litepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":47},{"id":54,"slug":"himanshu-tyagi","name":"Himanshu Tyagi","title":"Co-founder","company":"Sentient","sector":"crypto","profile_url":null,"image_url":null,"score":41,"tier":"informed_operator","dimensions":{"foundations":16,"vector_embeddings":8,"transformers_lm":8,"frontier_founder":3,"lm_domain_depth":4,"hands_on_engineering":10,"industry_impact":10,"scientific_founder":8},"rubric_version":3,"weighted_score":41,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The real Himanshu Tyagi (co-founder of Sentient) is a Professor of Electrical Communication Engineering at the Indian Institute of Science (IISc) Bangalore, whose research home field is information theory, applied to cryptography, statistics, privacy, and federated learning — including authored work such as 'Wyner-Ziv Gradient Compression for Federated Learning' and distributed mean-estimation/communication-efficient federated-learning papers, and a Cambridge University Press book on information-theoretic cryptography. This gives strong mathematical/statistical-learning foundations (information theory is core to statistical learning) and a genuine, if narrow, machine-learning-adjacent research record (federated learning, distributed estimation) rather than direct transformer/LM or embeddings authorship. He co-founded Sentient (a decentralized/open-source AGI protocol, 'The Grid') while remaining an active IISc professor — real technical leadership of an AI infrastructure venture, though Sentient's core LLM/agent technology is a team effort, not solely his personal engineering output. Scored moderately: strong math foundations, real but adjacent (not core-lineage) ML research, and company leadership without a personally-authored canonical transformer/embeddings paper.\n\nTyagi's authored record is information theory applied to cryptography, privacy, distributed estimation and federated learning (Wyner-Ziv gradient compression, communication-efficient distributed mean estimation) — none of which is an attention/transformer/embedding/optimizer/tokenizer/scaling/alignment building block that today's frontier GPT/Claude/Gemini/Llama models descend from, so frontier lineage is essentially absent. He has no verifiable, continuous language-modeling research record; his LM-adjacent work (OML model-fingerprinting primitive, SAKSHI decentralized-AI platform) dates only to ~2023-2024 via Sentient and is crypto-native model-distribution infrastructure rather than statistical/neural LM research. He is, however, a genuine scientific co-founder: an active IISc professor who personally co-authored the OML (arXiv 2411.03887) and SAKSHI (arXiv 2307.16562) papers that form Sentient's technical core, giving him real founder-scientist standing — but only ~2 years in that role, which caps the duration component.","evidence":[{"claim":"Professor of Electrical Communication Engineering, Indian Institute of Science (IISc), Bengaluru; research focus is information theory applied to cryptography, statistics, privacy, federated learning, and networks","source_url":"https://ece.iisc.ac.in/~htyagi/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored federated-learning research including communication-efficient distributed mean estimation and Wyner-Ziv gradient compression for federated learning","source_url":"https://par.nsf.gov/servlets/purl/10415421","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of a Cambridge University Press book on Information Theoretic Cryptography; his research group has won best-paper awards at IEEE ISIT (International Symposium on Information Theory) twice","source_url":"https://scholar.google.com/citations?user=OWMi2AQAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"IISc faculty page: Associate Professor, Department of Electrical Communication Engineering; PhD Electrical and Computer Engineering, University of Maryland 2013, advisor Prakash Narayan; dual degree IIT Delhi 2007; research in information theory, cryptography, privacy, federated learning, distribute","source_url":"https://ece.iisc.ac.in/~htyagi/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 143725642: 102 papers, 1,809 citations, h-index 26; works include Inference Under Information Constraints I/II/III, Estimating Renyi Entropy of Discrete Distributions (2014), Test without Trust (2018), RATQ universal fixed-length quantizer (2019), Secret Key Agreement (2014),","source_url":"https://api.semanticscholar.org/graph/v1/author/143725642","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution' — authors include Sandeep Nailwal, Sewoong Oh, Himanshu Tyagi and Pramod Viswanath; proposes AI-native model fingerprinting with crypto-economic enforcement","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'SAKSHI: Decentralized AI Platforms' (2023) — authors include Sreeram Kannan, Himanshu Tyagi and Pramod Viswanath; affiliations Princeton, UIUC, Tsinghua, HKUST, Witness Chain, EigenLayer","source_url":"https://arxiv.org/abs/2307.16562","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sentient describes itself as an open-source AI reasoning lab building OML (Open, Monetizable, Loyal AI), Arena and EvoSkill, and emphasises peer-reviewed research","source_url":"https://sentient.xyz/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'SAKSHI: Decentralized AI Platforms' (2023) co-authored by Himanshu Tyagi with Sreeram Kannan and Pramod Viswanath","source_url":"https://arxiv.org/abs/2307.16562","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"IISc ECE faculty page: Tyagi's research is information theory applied to cryptography, statistics, privacy and federated learning — not language modeling or transformer/embedding architecture","source_url":"https://ece.iisc.ac.in/~htyagi/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'SAKSHI: Decentralized AI Platforms' (2023) co-authored by Himanshu Tyagi — decentralized-inference infrastructure, adjacent to but not part of the frontier transformer/pretraining lineage","source_url":"https://arxiv.org/abs/2307.16562","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Tyagi's IISc faculty research profile lists information theory, cryptography, privacy, statistics and federated learning — no language-modeling, embedding or transformer research","source_url":"https://ece.iisc.ac.in/~htyagi/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.74,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":48},{"id":64,"slug":"sreeram-kannan","name":"Sreeram Kannan","title":"Founder","company":"EigenLayer (Eigen Labs)","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/sreeram-kannan.jpg","score":41,"tier":"informed_operator","dimensions":{"foundations":16,"vector_embeddings":9,"transformers_lm":8,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":12,"industry_impact":12,"scientific_founder":6},"rubric_version":3,"weighted_score":41,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Kannan holds a PhD in Information Theory and Wireless Networks from University of Illinois Urbana-Champaign (2008-2012, plus an MS in Mathematics), and was (is, as affiliate) an Associate Professor of Electrical & Computer Engineering at the University of Washington — solid graduate-level mathematical/statistical foundations. His authored/co-authored papers include genuine ML research: 'ClusterGAN: Latent Space Clustering in Generative Adversarial Networks' (AAAI 2019, 533 citations) which is directly relevant to vector-embeddings/representation-learning, and 'Improving Federated Learning Personalization via Model-Agnostic Meta-Learning' (2019, 900 citations) and 'Communication Algorithms via Deep Learning' (2018) — real, well-cited, hands-on ML research, not just blockchain theory. His verified Google Scholar h-index is 37 with 7,027 citations, confirming a substantive, continuous research record since ~2008. He founded EigenLayer and is credited with originating the 'restaking' mechanism — strong hands-on systems/protocol engineering, though this is a distributed-systems/cryptoeconomic contribution rather than core transformer/LM research. Overall: real academic ML depth plus applied protocol engineering, but not canonical transformer/LM authorship.\n\nKannan's own research — information theory, blockchain scaling (Prism), restaking cryptoeconomics, plus adjacent ML (ClusterGAN, federated-learning MAML, communication algorithms via deep learning) — is not part of the attention/transformer/embedding/scaling/RLHF foundation that GPT/Claude/Gemini/Llama-class models descend from; none of his work is cited as a building block in frontier LLM technical reports, so frontier_founder is near the floor. He has no verifiable record in language modeling specifically (no vector-space text models, LSI, n-gram/neural LMs, seq2seq, transformers or LLM pretraining/alignment authorship), so lm_domain_depth is effectively zero. He is a genuine scientific/technical founder — PhD, UW professor, and originator of the EigenLayer restaking protocol and Eigen Labs (~2021–2026, roughly 5 years) — but that company's core is crypto restaking, not language-model systems, which places him at 'technical founder outside this field.'","evidence":[{"claim":"Google Scholar profile shows h-index 37 and 7,027 total citations","source_url":"https://scholar.google.com/citations?user=RrYw5jkAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'ClusterGAN: Latent Space Clustering in Generative Adversarial Networks' (AAAI 2019) and 'Improving Federated Learning Personalization via Model Agnostic Meta-Learning' (2019)","source_url":"https://scholar.google.com/citations?user=RrYw5jkAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 2404352759: 105 papers, 4,903 citations, h-index 30; dominant areas blockchain/consensus, machine learning and bioinformatics/information theory","source_url":"https://api.semanticscholar.org/graph/v1/author/2404352759","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Deconstructing the Blockchain to Approach Physical Limits' (Prism) — authors Vivek Bagaria, Sreeram Kannan, David Tse, Giulia Fanti, Pramod Viswanath","source_url":"https://arxiv.org/abs/1810.08092","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'ClusterGAN: Latent Space Clustering in Generative Adversarial Networks' — authors Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan (AAAI 2019)","source_url":"https://arxiv.org/abs/1809.03627","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'SAKSHI: Decentralized AI Platforms' (2023) lists Sreeram Kannan among the authors, with EigenLayer among the affiliations","source_url":"https://arxiv.org/abs/2307.16562","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"ClusterGAN published in AAAI Proceedings vol 33 (2019)","source_url":"https://doi.org/10.1609/aaai.v33i01.33014610","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5100722219: 135 works, 3,106 citations, h-index 28, affiliations include University of Washington and University of Illinois Urbana-Champaign; topics blockchain, distributed systems, cryptography, network coding","source_url":"https://api.openalex.org/authors/A5100722219","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"EigenLayer restaking mechanism and Eigen Labs were originated/founded by Sreeram Kannan, a former UW ECE associate professor; the core is a crypto restaking protocol, not language modeling","source_url":"https://www.eigenlayer.xyz/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kannan's ML/systems publications (ClusterGAN AAAI 2019, federated-learning MAML, Prism blockchain) are in GANs, distributed systems and information theory — no transformer/attention/embedding/LM authorship","source_url":"https://arxiv.org/abs/1809.03627","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 2404352759 topic areas are blockchain/consensus, machine learning and bioinformatics/information theory — no language-modeling lineage","source_url":"https://api.semanticscholar.org/graph/v1/author/2404352759","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sreeram Kannan founded EigenLayer/Eigen Labs and originated the restaking mechanism, with prior blockchain-scaling research (Prism) and information-theory background — a distributed-systems/cryptoeconomic contribution, not transformer/LM work","source_url":"https://arxiv.org/abs/1810.08092","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His ML publications are GAN-based clustering and communications/coding via deep learning, not language modeling: 'ClusterGAN: Latent Space Clustering in Generative Adversarial Networks' (AAAI 2019)","source_url":"https://arxiv.org/abs/1809.03627","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record shows dominant areas of blockchain/consensus, general ML and bioinformatics/information theory — no LM/transformer lineage","source_url":"https://api.semanticscholar.org/graph/v1/author/2404352759","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.81,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":49},{"id":38,"slug":"clement-delangue","name":"Clément Delangue","title":"Co-founder & CEO","company":"Hugging Face","sector":"general","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/clement-delangue.png","score":39,"tier":"informed_operator","dimensions":{"foundations":4,"vector_embeddings":7,"transformers_lm":9,"frontier_founder":6,"lm_domain_depth":8,"hands_on_engineering":10,"industry_impact":16,"scientific_founder":4},"rubric_version":3,"weighted_score":39,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The passes agree on the shape of the record and differ by small margins on how much credit a non-first-author library-paper byline and a personal model-training habit earn. I verified the byline: the ACL Anthology record for 'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demo, ~8.3k citations) lists him fifth of 22 authors, after Wolf, Debut, Sanh and Chaumond. That is genuine inclusion on a paper documenting a library the entire field uses, but the technical authorship sits with the engineering team, and his OpenAlex h-index of 5 is almost entirely these two library artifacts, so it cannot be read as a personal research record. He has no doctorate, no thesis and no paper in linear algebra, optimization or statistical learning; his education is a Master in Management from ESCP plus a non-degree Stanford extension programming course. Pass 2 adds one fact pass 1 lacked and I confirmed it: his personal Hugging Face account 'clem' publishes 13 models and 24 datasets, including SmolLM2 SFT and Qwen2.5-1.5B fine-tunes — modest but real hands-on model-training practice rather than pure delegation, which is why hands_on_engineering lands nearer pass 2's number than pass 1's. Pass 2's vector_embeddings of 9 is the one score I reduce: neither pass cited any embedding artifact authored by him, and the Hub hosting embedding models is organisational, not personal. His overwhelming strength is industry impact, and it is legitimate under this rubric for a reason distinct from his fame: the Transformers library, the Hub and Datasets are the distribution layer through which essentially every transformer and embedding model in the field now ships, and that core is exactly the systems the rubric measures. None of his valuation, fundraising or media presence is counted.\n\nThe frontier lineage running through Delangue is organizational, not personal: Hugging Face's Transformers library, Hub and Datasets are the distribution layer essentially every GPT/Claude/Llama-class release ships or is fine-tuned through, and he is a genuine byline author (5th of 22) on the canonical library paper — but the architecture, tokenizers and training code were authored by Wolf, Debut, Sanh, Chaumond and the engineering team, so he earns lineage-adjacent credit, not a named building block. His language-modeling duration is long (co-founded HF in 2016, ~10 years at the helm of the field's central NLP/LM platform, byline papers 2019–2021, modest personal SmolLM2/Qwen2.5 fine-tunes on his 'clem' account) but the personal DEPTH is shallow — a CEO/business role over the science rather than a continuous research record. As scientific/technical founder he scores low: he is the founder-CEO of an AI company whose core science and engineering are done by his technical co-founders (Thomas Wolf as CSO, Julien Chaumond as CTO), with a Master in Management and no core research/code/patents of his own, which is precisely the 'founder with technical co-founders doing the science' case the rubric declines to reward.","evidence":[{"claim":"ACL Anthology record for 'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demo) lists Clement Delangue fifth among 22 authors, after Wolf, Debut, Sanh and Chaumond","source_url":"https://aclanthology.org/2020.emnlp-demos.6/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed among the 22 authors of 'HuggingFace's Transformers: State-of-the-art Natural Language Processing' (arXiv:1910.03771, 2019)","source_url":"https://arxiv.org/abs/1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His personal Hugging Face account 'clem' publishes 13 models and 24 datasets, including smollm2-135m-sft-tiny, cifar10-vit-poc and macron-style-qwen2.5-1.5B — verified as Clément Delangue's account via linked handles clementdelangue and clmnt","source_url":"https://huggingface.co/clem","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5009717413 (Hugging Face affiliation): 10 works, 13,262 citations, h-index 5, dominated by the Transformers and Datasets library papers","source_url":"https://api.openalex.org/authors/A5009717413","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hugging Face was founded in 2016 by Delangue (CEO), Julien Chaumond (CTO) and Thomas Wolf (CSO), starting as a chatbot before pivoting to an ML platform after open-sourcing the model","source_url":"https://en.wikipedia.org/wiki/Hugging_Face","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on 'Datasets: A Community Library for Natural Language Processing' (EMNLP 2021 demo)","source_url":"https://aclanthology.org/2021.emnlp-demo.21/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Master in Management, ESCP Business School (2008-2012); non-degree Stanford intro-CS extension course (2011-2012)","source_url":"https://www.clay.com/dossier/hugging-face-ceo","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Hugging Face, became CEO in July 2016; company builds the Transformers open-source library","source_url":"https://en.wikipedia.org/wiki/Hugging_Face","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed co-author on 'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020), 8318 citations per OpenAlex","source_url":"https://doi.org/10.18653/v1/2020.emnlp-demos.6","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on 'HuggingFace's Transformers: State-of-the-art Natural Language Processing' arXiv 1910.03771 (2019), 3149 citations","source_url":"https://doi.org/10.48550/arxiv.1910.03771","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hugging Face founded 2016 by Delangue (CEO), Julien Chaumond (CTO) and Thomas Wolf (CSO/Chief Scientist) — the science and engineering role sits with Wolf and Chaumond, not the CEO","source_url":"https://en.wikipedia.org/wiki/Hugging_Face","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Delangue listed 5th of 22 authors on 'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demo), after Wolf, Debut, Sanh and Chaumond — the technical authorship of the library the frontier stack builds on is the engineering team's","source_url":"https://aclanthology.org/2020.emnlp-demos.6/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Personal Hugging Face account 'clem' publishes modest LM fine-tunes (SmolLM2 SFT, Qwen2.5-1.5B), real but shallow hands-on language-modeling practice over ~10 years leading the platform","source_url":"https://huggingface.co/clem","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Transformers: State-of-the-Art Natural Language Processing' (EMNLP 2020 demo, ~8.3k citations) lists Clément Delangue 5th of 22 authors, after Wolf, Debut, Sanh and Chaumond — the library frontier models are trained/served with, but with technical authorship on the engineering team","source_url":"https://aclanthology.org/2020.emnlp-demos.6/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author on 'TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents' (arXiv 1901.08149, 2019) — his earliest verifiable language-modeling lineage byline","source_url":"https://arxiv.org/abs/1901.08149","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":50},{"id":31,"slug":"ali-ghodsi","name":"Ali Ghodsi","title":"Co-founder & CEO","company":"Databricks","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Ali_Ghodsi","image_url":"/api/v1/ceo-ai-leaderboard/portrait/ali-ghodsi.jpg","score":35,"tier":"informed_operator","dimensions":{"foundations":11,"vector_embeddings":4,"transformers_lm":5,"frontier_founder":3,"lm_domain_depth":2,"hands_on_engineering":14,"industry_impact":14,"scientific_founder":7},"rubric_version":3,"weighted_score":35,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The passes diverged because pass 1 scored from the dossier's OpenAlex record and pass 2 caught that the record is a conflation. I verified this and pass 2 is right: OpenAlex A5040035859 is 'Ali Reza Ghodsi', last known institution University of Waterloo, whose largest topic is 'Face and Expression Recognition' with 58 works. That is a different person — Ali Ghodsi, Professor of Statistics and Actuarial Science at the University of Waterloo, who teaches statistical learning and deep learning and works on dimensionality reduction. The dossier's h-index 59, 17,626 citations and earliest year 1984 are therefore contaminated and cannot be used, and the confusion is compounded by the Waterloo professor's field being closer to this rubric's subject matter than the Databricks CEO's actually is. The correct record is the Google Scholar profile verified on a cs.berkeley.edu address: ~39,955 citations, h-index 51, i10-index 84, top works OpenFlow (13,898), Apache Spark (4,373), Mesos (2,777), Spark SQL (2,172) and Dominant Resource Fairness (1,927). That is a genuinely distinguished record in distributed systems, scheduling, networking and data management, built on a KTH PhD (2006) on distributed hash tables under Seif Haridi and years at the Berkeley AMPLab. It sits one layer beneath AI rather than inside it. He has no authored work on attention, transformers, pretraining, scaling laws or alignment, and none on embeddings, dense retrieval or vector search; Databricks' LLM artifacts (Dolly, MosaicML/DBRX) are outputs of the Mosaic team, and the DBRX announcement does not name him as a technical contributor. Pass 2's vector_embeddings of 8 and transformers_lm of 7 credit him for organisational proximity to work he did not author, which the rubric forbids; pass 1's 3/3 are nearer the mark, adjusted slightly for the MLflow and Dolly co-authorships that do appear on his verified profile.\n\nGhodsi's authored corpus is distributed systems, scheduling and data management (Mesos, Spark, Spark SQL, Delta Lake) — infrastructure that sits one layer beneath AI, not the architecture, attention, embeddings, optimizers, tokenizers or pretraining objectives that frontier GPT/Claude/Gemini/Llama-class models descend from; Spark is not a named building block cited in frontier model technical reports, and Databricks' own LLM artifacts (Dolly, DBRX) were built by the acquired Mosaic/MPT team and do not name him as a technical contributor, so frontier_founder is low. He has no verifiable personal record in language modeling — statistical/neural LMs, vector-space text models, seq2seq, transformers or LLM pretraining/alignment — so lm_domain_depth is near-zero (the dossier's pre-2000 timeline is a homonym conflation with the Waterloo statistics professor). He is, however, a genuine scientific/technical co-founder: he personally co-authored the core research and code (Spark SQL, Delta Lake) that Databricks runs on and has led its technical direction since founding in 2013 (~13 years), but the company's core is a data/analytics platform rather than an LM/embedding/transformer system, which places him in the 'technical founder outside this field' band rather than higher.","evidence":[{"claim":"Google Scholar profile verified on a cs.berkeley.edu email, affiliation UC Berkeley and Databricks: ~39,955 citations, h-index 51, i10-index 84; top works OpenFlow (2008, 13,898 citations), Apache Spark (2016, 4,373), Mesos (2011, 2,777), Spark SQL (2015, 2,172), Dominant Resource Fairness (2011, 1,","source_url":"https://scholar.google.com/citations?user=YsXNU78AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5040035859 is 'Ali Reza Ghodsi' with last known institution University of Waterloo (active 2002-2025) and top topic 'Face and Expression Recognition' (58 works) — a conflated record, not cleanly the Databricks CEO","source_url":"https://api.openalex.org/authors/A5040035859","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"A different Ali Ghodsi is Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, teaching Statistical Learning and Deep Learning — confirming two distinct people share the name","source_url":"https://uwaterloo.ca/statistics-and-actuarial-science/profile/aghodsib","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from KTH Royal Institute of Technology (2006), dissertation 'Distributed k-ary System: Algorithms for Distributed Hash Tables', advised by Seif Haridi; co-founded Databricks in 2013, CEO from 2016","source_url":"https://en.wikipedia.org/wiki/Ali_Ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"DBRX was built by the Mosaic team that previously built MPT; Ghodsi is not named as an author or technical contributor in the announcement","source_url":"https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD from KTH Royal Institute of Technology, thesis 'Distributed k-ary System: Algorithms for Distributed Hash Tables' (2006), advisor Seif Haridi","source_url":"https://en.wikipedia.org/wiki/Ali_Ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Databricks, founded 2013 to commercialize Apache Spark","source_url":"https://www.databricks.com/dataaisummit/speaker/ali-ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Adjunct professor at UC Berkeley, worked with Scott Shenker and Ion Stoica at AMPLab","source_url":"https://kitrum.com/blog/the-inspiring-story-ali-ghodsi-ceo-of-databricks/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (UC Berkeley and Databricks): ~39,955 citations, h-index 51, i10-index 84; top works are OpenFlow, Apache Spark, Mesos, Spark SQL, Dominant Resource Fairness, Delta Lake","source_url":"https://scholar.google.com/citations?user=YsXNU78AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder (2013) and CEO (from 2016) of Databricks, founded to commercialize Apache Spark, whose SQL and Delta Lake papers he co-authored under a Berkeley/Databricks affiliation","source_url":"https://en.wikipedia.org/wiki/Ali_Ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (UC Berkeley/Databricks) shows top works in distributed systems and data management (OpenFlow, Spark, Mesos, Spark SQL, Delta Lake) with no authored work on transformers, attention, embeddings or language modeling","source_url":"https://scholar.google.com/citations?user=YsXNU78AAAAJ","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Databricks (2013) and CEO from 2016; KTH PhD 2006 on distributed hash tables; personally co-authored Mesos, Spark SQL and Delta Lake — distributed-systems/data infrastructure, not language modeling","source_url":"https://en.wikipedia.org/wiki/Ali_Ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Databricks founded 2013 to commercialize Apache Spark; its core is a data/analytics (Lakehouse) platform, distinct from embedding/transformer systems","source_url":"https://www.databricks.com/dataaisummit/speaker/ali-ghodsi","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":51},{"id":95,"slug":"jed-mccaleb","name":"Jed McCaleb","title":"Founder and chairman, Vast; co-founder and CTO, Stellar","company":"Vast","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","image_url":null,"score":33,"tier":"informed_operator","dimensions":{"foundations":5,"vector_embeddings":4,"transformers_lm":10,"frontier_founder":4,"lm_domain_depth":4,"hands_on_engineering":13,"industry_impact":8,"scientific_founder":7},"rubric_version":3,"weighted_score":33,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"McCaleb is an unusual case: a self-taught programmer with no completed degree who nonetheless has a genuine and recent co-authorship record in language-model research that the dossier does not surface at all. Querying the arXiv API directly for his name returns four papers, all of which I confirmed carry him as a listed author: 'Thinking agents for zero-shot generalization to qualitatively novel tasks' (2503.19815, March 2025, with Miconi, McKee and Zheng), 'Goal-Directed Search Outperforms Goal-Agnostic Memory Compression in Long-Context Memory Tasks' (2511.21726, November 2025), 'End-to-End Test-Time Training for Long Context' (2512.23675, December 2025, with Dalal, Leskovec, Koyejo, Hashimoto, Guestrin, Choi and Yu Sun) and 'Learning to Discover at Test Time' (2601.16175, January 2026, TTT-Discover). These sit squarely in the modern long-context, memory and test-time-adaptation lineage — the first formulates long-context language modelling as continual learning over a sliding-window-attention Transformer — and four papers across a year is a sustained line rather than a one-off byline, though he appears mid-list among senior academics rather than leading. His engineering record is deep and hands-on across decades: he personally wrote eDonkey2000 and the Overnet peer-to-peer network, built Mt. Gox, wrote the original Ripple consensus implementation, and co-authored the Stellar Consensus Protocol and 'Fast and secure global payments with Stellar' (SOSP 2019). He has no verifiable training in linear algebra, optimization or statistical learning and no embedding or pretraining work of his own, so foundations and vector_embeddings stay low. His earlier AI involvement was funding — donations to MIRI and OpenAI — which is not evidence under this rubric; only the authored 2025-26 work counts, and its recency is discounted under the depth-of-experience rule.\n\nMcCaleb's foundational built work — eDonkey2000/Overnet P2P, Mt. Gox, the original Ripple implementation and the Stellar Consensus Protocol — is payments/consensus infrastructure, none of which any frontier language model descends from; his only lineage-relevant contribution is mid-list co-authorship on four 2025-26 test-time-training / long-context papers (with Yu Sun, Leskovec, Hashimoto, Guestrin et al.), which touch the modern long-context-LM research line but are recent, not led by him, and not yet a named building block in frontier model reports, so frontier_founder and lm_domain_depth stay low (~1 year of verifiable language-modeling activity, all 2025-26; he has no pre-2013 vector-space/LSI record). He is unquestionably a deep technical founder — founder-CTO of Ripple (to 2013), co-founder/CTO of Stellar (2014-present) and founder/ex-CEO of Vast (2021-), personally authoring the core protocols and code — which places him at the top of the 'technical founder outside this field' band, but the core of every one of those companies is payments, consensus or aerospace, not language models, so scientific_founder caps at 7 rather than the in-field bands above it.","evidence":[{"claim":"arXiv API query for 'Jed McCaleb' returns four papers with him as a listed author: 'Thinking agents for zero-shot generalization to qualitatively novel tasks' (2503.19815, Mar 2025), 'Goal-Directed Search Outperforms Goal-Agnostic Memory Compression in Long-Context Memory Tasks' (2511.21726, Nov 202","source_url":"http://export.arxiv.org/api/query?search_query=all:%22Jed%20McCaleb%22&max_results=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'End-to-End Test-Time Training for Long Context' (arXiv:2512.23675) lists Jed McCaleb as twelfth of fourteen authors alongside Karan Dalal, Jure Leskovec, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin, Yejin Choi and Yu Sun; it formulates long-context language modelling as continual learning ov","source_url":"https://arxiv.org/abs/2512.23675","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Learning to Discover at Test Time' (arXiv:2601.16175, TTT-Discover) lists Jed McCaleb as fifth of eleven authors with Mert Yuksekgonul, Jan Kautz, James Zou, Carlos Guestrin and Yu Sun; it applies reinforcement learning at test time so an LLM continues training on test-problem-specific experience","source_url":"https://arxiv.org/abs/2601.16175","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 2077048287 (Jed McCaleb) holds six papers: four machine-learning papers (2025-2026) plus 'Fast and secure global payments with Stellar' (SOSP 2019) and 'The Stellar Consensus Protocol' (2018)","source_url":"https://api.semanticscholar.org/graph/v1/author/2077048287/papers?fields=title,year,venue,authors,externalIds&limit=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"McCaleb left UC Berkeley without completing a degree; created eDonkey2000 and the Overnet peer-to-peer networks, founded Mt. Gox in 2010, was founder and CTO of Ripple until 2013, co-founder and CTO of Stellar, and founder of the aerospace company Vast","source_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Left UC Berkeley to work as a programmer in New York; no completed degree found","source_url":"https://www.bitnovo.com/blog/en/who-is-jed-mccaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Created eDonkey2000 and Overnet peer-to-peer file-sharing networks; founded Mt. Gox bitcoin exchange in 2010","source_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded and served as CTO of Ripple until 2013; co-founder and CTO of Stellar; founder/ex-CEO/chairman of aerospace startup Vast","source_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"End-to-End Test-Time Training for Long Context (arXiv 2512.23675) lists Jed McCaleb among the authors alongside Jure Leskovec, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin, Yejin Choi and Yu Sun","source_url":"https://arxiv.org/abs/2512.23675","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Learning to Discover at Test Time (arXiv 2601.16175, TTT-Discover) lists Jed McCaleb as fifth author with Mert Yuksekgonul, Jan Kautz, James Zou and Yu Sun","source_url":"https://arxiv.org/abs/2601.16175","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"McCaleb is a listed author on 'End-to-End Test-Time Training for Long Context' (arXiv:2512.23675) and 'Learning to Discover at Test Time' (arXiv:2601.16175), long-context/test-time-training lineage papers, appearing mid-list among senior academics","source_url":"https://arxiv.org/abs/2512.23675","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"McCaleb founded/personally built eDonkey2000, Overnet, Mt. Gox (2010), Ripple (founder/CTO to 2013) and Stellar (co-founder/CTO), and founded aerospace startup Vast (2021) — all technical-founder roles whose core is P2P/crypto/payments/aerospace, not language modeling","source_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Learning to Discover at Test Time' (arXiv:2601.16175, TTT-Discover) lists McCaleb as fifth of eleven authors with Yu Sun, Jan Kautz, James Zou — a 2026 test-time-training paper, his fourth in the long-context/memory lineage within ~12 months","source_url":"https://arxiv.org/abs/2601.16175","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"McCaleb was founder/CTO of Ripple until 2013, co-founder and CTO of Stellar, and founder/chairman/ex-CEO of aerospace startup Vast — technical-founder roles whose core is payments/consensus/aerospace, not language modeling","source_url":"https://en.wikipedia.org/wiki/Jed_McCaleb","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":52},{"id":34,"slug":"jensen-huang","name":"Jensen Huang","title":"Founder, President & CEO","company":"NVIDIA","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Jensen_Huang","image_url":"/api/v1/ceo-ai-leaderboard/portrait/jensen-huang.jpg","score":33,"tier":"informed_operator","dimensions":{"foundations":8,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":10,"lm_domain_depth":2,"hands_on_engineering":10,"industry_impact":15,"scientific_founder":7},"rubric_version":3,"weighted_score":33,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Huang holds a BS in Electrical Engineering (Oregon State, 1984) and an MS in Electrical Engineering (Stanford, 1992), and worked as a hands-on chip designer at AMD and LSI Logic (1984-1993) before founding NVIDIA in 1993. No personal peer-reviewed papers, patents, or authored research in AI/ML math, embeddings, or transformers were found; the dossier's own OpenAlex match (a single 'Foreword' to a book chapter) is not genuine research output. NVIDIA's GPUs and CUDA platform are foundational AI infrastructure, but CUDA and NVIDIA's ML-relevant architectures were built by teams of NVIDIA engineers over decades, not personally designed by Huang, whose role since 1993 has been founder/CEO. This is a case of a company built years before the deep-learning/transformer era later becoming critical infrastructure for it — strong organizational industry impact, but no personal research or engineering record in the core dimensions this rubric measures.\n\nHuang has no personally-authored building block of the frontier AI stack (no transformer/attention/embedding/optimizer/tokenizer paper, no LM research, no AI patents), but as NVIDIA's founder-CEO he made and drove the multi-decade GPU-compute and CUDA bet that became the accelerator and training/inference substrate every GPT/Claude/Gemini/Llama-class model is trained and served on — a genuine but organizational and hardware-side lineage, not a personally-authored method, so frontier_founder sits mid-band. His verifiable years in language modeling specifically (statistical/neural LMs, embeddings, seq2seq, transformers, pretraining) are essentially zero — he is a hardware engineer, not an LM researcher — so lm_domain_depth is near the floor. He is a deeply technical, EE-trained founder (ex-AMD/LSI chip designer) who has set NVIDIA's technical direction for ~33 years (founded 1993), but NVIDIA's core engineering and IP were built by technical co-founders (Malachowsky, Priem) and teams, and the field is accelerators, not the AI-math/LM lineage this rubric measures — placing scientific_founder as a genuine technical founder outside this specific field rather than an author of the systems' core science.","evidence":[{"claim":"BS Electrical Engineering, Oregon State University (1984)","source_url":"https://engineering.oregonstate.edu/alumni-partners/oregon-stater-awards/searchable-awards-database/jen-hsun-huang-engineering-hall","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MS Electrical Engineering, Stanford University (1992)","source_url":"https://engineering.stanford.edu/about/history/heroes/2018-heroes/jensen-huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chip design engineer at AMD then LSI Logic (1984-1993) before co-founding NVIDIA in 1993","source_url":"https://nvidianews.nvidia.com/bios/jensen-huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No IEEE/ACM peer-reviewed publications found under his name; OpenAlex record shows only a single non-research 'Foreword' (2022)","source_url":"https://corporate-awards.ieee.org/recipient/jensen-huang/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BSEE Oregon State University 1984 with highest honours; MSEE Stanford University 1992; designed AMD microprocessors; joined LSI Logic as a technical officer where he worked with Malachowsky and Priem on graphics accelerators; co-founded Nvidia 1993 and has been president and CEO since day one","source_url":"https://en.wikipedia.org/wiki/Jensen_Huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Official Nvidia biography describes him as founder, president and CEO since 1993, holding BSEE (Oregon State) and MSEE (Stanford), with no research publications or patents listed","source_url":"https://nvidianews.nvidia.com/bios/jensen-huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder, president and CEO of NVIDIA since 1993; BSEE Oregon State, MSEE Stanford; former chip-design engineer at AMD and LSI Logic — no research publications or AI/ML patents listed","source_url":"https://en.wikipedia.org/wiki/Jensen_Huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NVIDIA GPUs and the CUDA platform are the dominant accelerators and training/inference substrate for modern deep-learning and large language models (e.g. AlexNet trained on NVIDIA GPUs, 2012), a company-level contribution built by NVIDIA engineering teams","source_url":"https://en.wikipedia.org/wiki/CUDA","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder, president and CEO of NVIDIA since 1993; NVIDIA GPUs/CUDA are the compute foundation for modern AI/deep-learning training","source_url":"https://en.wikipedia.org/wiki/Jensen_Huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prior hands-on engineering as a chip/microprocessor designer at AMD and LSI Logic (1984–1993) before co-founding NVIDIA; BSEE Oregon State, MSEE Stanford","source_url":"https://nvidianews.nvidia.com/bios/jensen-huang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.89,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":53},{"id":36,"slug":"robin-li","name":"Robin Li (Li Yanhong)","title":"Co-founder, Chairman & CEO","company":"Baidu","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Robin_Li","image_url":"/api/v1/ceo-ai-leaderboard/portrait/robin-li.png","score":32,"tier":"informed_operator","dimensions":{"foundations":8,"vector_embeddings":8,"transformers_lm":3,"frontier_founder":2,"lm_domain_depth":4,"hands_on_engineering":9,"industry_impact":12,"scientific_founder":8},"rubric_version":3,"weighted_score":32,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes discarded the dossier's research blocks as homonyms — correctly: its OpenAlex match is a University of Pennsylvania bioengineering researcher working on polyacrylamide gels and traction force microscopy, and the PubMed block draws on 44,906 'Li R' hits. Both also agree on the one genuinely personal technical artifact, and it is a real one: while at IDD Information Services in 1996 he invented the RankDex site-scoring algorithm and is the named inventor on US Patent 5,920,859, 'Hypertext Document Retrieval System and Method' (filed 5 February 1997, issued 6 July 1999), which ranks documents by hyperlink and anchor-text analysis and which Google's PageRank patent cites. He also published 'Toward a Qualitative Search Engine' in IEEE Internet Computing in 1998 and personally wrote search software at Dow Jones and worked as a staff engineer at Infoseek. The passes differ on how much this earns. Pass 2's framing is the more accurate one — link-analysis ranking is an eigenvector problem on the web graph and genuine first-principles information-retrieval work, which lifts foundations and hands-on engineering above the 'manages builders' band — but pass 1 is right that it is a single dated artifact: term- and graph-based retrieval, not learned vector representations, and his last hands-on technical contribution of record is roughly a quarter-century old, with everything since being executive. On transformers, pass 1 is right and pass 2 too generous: Baidu's ERNIE family is published by Baidu research staff (Sun, Wang, Li, Feng et al.), Deep Speech and PaddlePaddle likewise, and Baidu's own history shows dedicated technical leadership hired for AI research. Leading the company that funded those labs is industry impact, not personal authorship in the lineage, and fame and the scale of Baidu are explicitly not evidence.\n\nLi's one personal technical artifact — the 1996 RankDex link-analysis ranking method (US Patent 5,920,859), referenced by Google's PageRank patent — is information-retrieval / web-graph lineage, NOT part of the attention→transformer→LLM stack; today's frontier models (GPT/Claude/Gemini) descend from word2vec/attention/transformers/scaling work, none of which trace to RankDex, so frontier_founder is near-zero. His verifiable personal language-modeling-adjacent record is the vector-space/anchor-text search work of roughly 1996–1999 (RankDex, the 1998 IEEE Internet Computing paper, engineering at Dow Jones/Infoseek) — about three years, then purely executive; Baidu's ERNIE/Deep Speech/PaddlePaddle were authored by staff (Sun, Wang, Feng et al.) and AI research was vested in hires like Andrew Ng, so lm_domain_depth reflects that short, dated, discontinuous personal record. He co-founded Baidu in 2000 as CEO with its search core built on his own RankDex IP, which earns genuine scientific/technical-founder credit for the early years (~2000–2005) before the science passed to dedicated research staff, but not the 15+ years of personally authoring core research the top band requires.","evidence":[{"claim":"Created the RankDex algorithm in 1996 at IDD Information Services; named inventor on US patent 5,920,859 'Hypertext Document Retrieval System and Method', filed 5 February 1997, issued 6 July 1999; Google's PageRank patent references it; earlier wrote search software at Dow Jones for the Wall Street","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"RankDex was the first search engine to use hyperlinks to measure site quality, predating PageRank by about two years, and the technology was carried into Baidu","source_url":"https://en.wikipedia.org/wiki/RankDex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Baidu's ERNIE pretrained language model family is published by Baidu research staff (Sun, Wang, Li, Feng et al.), not authored by Robin Li","source_url":"https://arxiv.org/abs/1904.09223","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Baidu appointed Andrew Ng as Chief Scientist in 2014 to lead AI research, indicating AI research leadership vested in dedicated technical hires rather than Li personally","source_url":"https://en.wikipedia.org/wiki/Baidu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Robin Li earned a master's degree in computer science from the University at Buffalo (SUNY), leaving the PhD program in 1994; prior degree (Bachelor of Management, Information Management) from Peking University.","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"While working at IDD Information Services (a Dow Jones & Company division, 1994-1997), Li created the RankDex site-scoring algorithm in 1996, which used hyperlink anchor-text analysis to rank search results; he later worked at Infoseek (1997-1999) as a staff engineer on image search.","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Li published 'Toward a Qualitative Search Engine' in IEEE Internet Computing (July/August 1998), describing the RankDex approach; this predates and is referenced in relation to Larry Page's PageRank patent (filed 1998).","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Baidu appointed Andrew Ng as Chief Scientist in May 2014 to lead AI research, indicating Baidu's deep-learning/AI research leadership has been vested in dedicated technical hires rather than Robin Li personally; no source attributes personal authorship of ERNIE/Wenxin Yiyan LLM research to Li.","source_url":"https://en.wikipedia.org/wiki/Baidu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"RankDex site-scoring algorithm (1996) and US Patent 5,920,859 'Hypertext Document Retrieval System and Method' is link/anchor-text web-graph retrieval referenced by Google's PageRank patent — search IR lineage, not the transformer/embedding lineage frontier LLMs are built on","source_url":"https://en.wikipedia.org/wiki/RankDex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Li's personal technical record is search/IR engineering from ~1994–1999 (IDD/Dow Jones, Infoseek, IEEE Internet Computing 1998); he left his PhD program in 1994 and his hands-on building ended ~1999","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Baidu's ERNIE language-model family is published by Baidu research staff (Sun, Wang, Li, Feng et al.), not authored by Robin Li; Baidu vested AI research leadership in dedicated hires such as Andrew Ng (Chief Scientist, 2014)","source_url":"https://arxiv.org/abs/1904.09223","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Robin Li invented the RankDex link-analysis ranking algorithm (1996) and is sole named inventor on US Patent 5,920,859 'Hypertext Document Retrieval System and Method' (filed 1997, issued 1999), later carried into Baidu's search core.","source_url":"https://en.wikipedia.org/wiki/RankDex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Li co-founded Baidu in 2000 and is its CEO; the company's early product was search built on his ranking technology, establishing him as a technical founder before AI research was vested in dedicated hires.","source_url":"https://en.wikipedia.org/wiki/Robin_Li","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Baidu's ERNIE pretrained language-model family is authored by Baidu research staff (Sun, Wang, Li, Feng et al.), with no personal authorship attributable to Robin Li in the transformer/LM lineage.","source_url":"https://arxiv.org/abs/1904.09223","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":54},{"id":67,"slug":"evan-cheng","name":"Evan Cheng","title":"Co-founder & CEO","company":"Mysten Labs (Sui)","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/evan-cheng.jpg","score":31,"tier":"informed_operator","dimensions":{"foundations":7,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":10,"lm_domain_depth":2,"hands_on_engineering":14,"industry_impact":10,"scientific_founder":6},"rubric_version":3,"weighted_score":31,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Cheng is a genuine, decorated hands-on systems engineer: he shared the 2012 ACM Software System Award with Chris Lattner and Vikram Adve for 'designing and implementing LLVM,' the compiler infrastructure that underlies most modern ML training/inference backends (XLA, MLIR, Triton, PyTorch's compiler stack). He was part of Meta's Diem blockchain project before co-founding Mysten Labs (2021), which built the Sui Layer-1 blockchain (per Wikipedia's list of Mysten Labs' original authors). This is compiler/systems engineering depth, not AI-specific research: no verifiable personal record in vector embeddings, statistical learning theory, or transformer/LM authorship was found, and the dossier's OpenAlex match (neuroscience/genetics papers, single-cell transcriptomics) is very likely a different Evan Cheng and was disregarded as an unconfirmed homonym. His hands_on_engineering score reflects real, award-recognized infrastructure engineering that AI systems depend on, while the AI-specific research dimensions remain low absent direct evidence.\n\nCheng's foundational contribution is LLVM (2012 ACM Software System Award, with Lattner and Adve), the compiler infrastructure from which the ML compiler lineage — MLIR, XLA, Triton, and PyTorch's compiler backend — descends, so today's frontier training/inference stacks demonstrably build on that lineage; but LLVM is a general-purpose compiler, not a named AI building block (no attention, tokenizer, optimizer, embedding or alignment method of his is cited by frontier model reports), so frontier_founder sits in the lower-lineage band. He has no verifiable record in language modeling specifically — statistical/neural LMs, vector-space text models, seq2seq, transformers or LLM pretraining — so lm_domain_depth is effectively zero (0 years). He is a genuine, hands-on technical/scientific founder-CTO of Mysten Labs (co-founded September 2021, ~4 years), but its core is the Sui Layer-1 blockchain and Move, NOT AI or language-modeling systems, placing scientific_founder in the 'technical founder outside this field' band.","evidence":[{"claim":"Co-recipient (with Chris Lattner and Vikram Adve) of the ACM Software System Award 2012 for designing and implementing LLVM","source_url":"https://en.wikipedia.org/wiki/LLVM","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"One of Mysten Labs' five original founders, formerly part of Meta's Diem project team, which left Meta to found Mysten Labs in September 2021","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Evan Cheng shared the 2012 ACM Software System Award with Vikram Adve and Chris Lattner for designing and implementing LLVM","source_url":"https://en.wikipedia.org/wiki/LLVM","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"2012 ACM Software System Award list: LLVM - Vikram S. Adve, Evan Cheng, Chris Lattner","source_url":"https://en.wikipedia.org/wiki/ACM_Software_System_Award","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Mysten Labs founded September 2021 by five former Meta/Diem engineers including Evan Cheng, Sam Blackshear (creator of the Move language), Adeniyi Abiodun, George Danezis and Kostas Chalkias, to build the Sui Layer 1","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Evan Cheng is NOT an author of 'Glow: Graph Lowering Compiler Techniques for Neural Networks' (arXiv 1805.00907), Facebook's neural-network compiler","source_url":"https://arxiv.org/abs/1805.00907","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Mysten Labs was founded in September 2021 by former Meta/Diem engineers including Evan Cheng, to build the Sui Layer-1 blockchain — a blockchain company, not a language-modeling company","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"2012 ACM Software System Award (LLVM): Vikram S. Adve, Evan Cheng, Chris Lattner","source_url":"https://en.wikipedia.org/wiki/ACM_Software_System_Award","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cheng shared the 2012 ACM Software System Award for designing and implementing LLVM, the compiler infrastructure underlying modern ML compiler backends (MLIR/XLA/Triton/PyTorch compiler)","source_url":"https://en.wikipedia.org/wiki/ACM_Software_System_Award","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MLIR, built on LLVM, is the compiler infrastructure used by AI frameworks and accelerator toolchains — evidence LLVM lineage feeds the frontier training/inference stack","source_url":"https://en.wikipedia.org/wiki/LLVM","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.68,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":55},{"id":42,"slug":"lisa-su","name":"Lisa Su","title":"Chair & CEO","company":"AMD","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Lisa_Su","image_url":"/api/v1/ceo-ai-leaderboard/portrait/lisa-su.jpg","score":30,"tier":"informed_operator","dimensions":{"foundations":10,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":4,"lm_domain_depth":2,"hands_on_engineering":14,"industry_impact":14,"scientific_founder":4},"rubric_version":3,"weighted_score":30,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Su holds a PhD in electrical engineering from MIT (1994, advisors Dimitri Antoniadis and James Chung), with a dissertation on extreme-submicrometer silicon-on-insulator MOSFETs — genuine, verifiable PhD-level engineering/physics training, though in semiconductor device physics rather than the mathematics/statistical-learning core this rubric measures. She has a real, if modest, publication record (OpenAlex: 15 works, 236 citations, h-index 7) spanning multi-chip packaging and compute-efficiency papers, and she personally led engineering work at IBM (copper interconnects), Freescale, and now AMD. There is no verifiable record of her personally authoring or leading vector-embedding, attention, or language-model research — her impact on AI is through building the accelerator hardware (GPUs, Instinct MI-series) that AI training runs on, which is legitimate hands-on-engineering/industry-impact credit for 'the hardware under core systems' per the rubric, but does not itself constitute core-AI research depth. Scored as strong PhD-level hardware engineer and industry leader of AI-infrastructure hardware, not as an AI researcher.\n\nSu's personal research record is semiconductor device physics (extreme-submicrometer SOI MOSFETs, copper interconnects, multi-chip packaging), none of which is a named building block — architecture, attention, embeddings, optimizer, tokenizer, dataset or alignment method — that frontier language models (GPT/Claude/Gemini/Llama) descend from; her AI relevance is that AMD's Instinct accelerators run some AI workloads, a company-product connection rather than a personal foundational contribution to the LM stack, so frontier_founder scores low. She has zero verifiable record in language modeling specifically — no vector-space, n-gram, neural-LM, seq2seq, transformer or pretraining/alignment work — so lm_domain_depth is essentially absent. She is a deeply technical CEO (MIT EE PhD, 1994) who sets AMD's technical direction, but she is NOT a founder of AMD (founded 1969; she joined 2012, CEO 2014), and AMD's core is semiconductor hardware, not language-model systems, so scientific_founder credits a technical executive outside this field rather than a technical founder within it (0 years as a founder).","evidence":[{"claim":"PhD electrical engineering, MIT (1994), dissertation on extreme-submicrometer silicon-on-insulator MOSFETs, advisors Dimitri Antoniadis and James Chung","source_url":"https://en.wikipedia.org/wiki/Lisa_Su","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Led IBM's Emerging Products group and helped drive the industry shift from aluminum to copper interconnects during 13 years at IBM","source_url":"https://www.clay.com/dossier/amd-ceo","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"President and CEO of AMD since 2014","source_url":"https://en.wikipedia.org/wiki/Lisa_Su","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MIT BS, MS (1991) and PhD (1994) in Electrical Engineering; doctoral work on extreme-submicrometer silicon-on-insulator MOSFETs under Dimitri Antoniadis and James Chung; copper interconnect work at IBM launched 1998; Cell processor contribution; over forty technical articles as of 2016","source_url":"https://en.wikipedia.org/wiki/Lisa_Su","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records MIT PhD (1994), doctoral advisors Dimitri A. Antoniadis and James E. Chung, employment at Texas Instruments (1994), IBM Research (1995), Freescale (2007), AMD (2012), CEO from 2014","source_url":"https://www.wikidata.org/wiki/Q18207172","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author on 'Multi-chip technologies to unleash computing performance gains over the next decade' (IEDM 2017)","source_url":"https://doi.org/10.1109/iedm.2017.8268306","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author on '1.1 Innovation For the Next Decade of Compute Efficiency' (ISSCC 2023)","source_url":"https://doi.org/10.1109/isscc42615.2023.10067810","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Su is president and CEO of AMD since 2014; AMD is a semiconductor company she joined in 2012, not a company she founded (AMD was founded in 1969)","source_url":"https://en.wikipedia.org/wiki/Lisa_Su","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Su's authored technical work is in semiconductor devices and compute efficiency (e.g. 'Multi-chip technologies to unleash computing performance gains over the next decade', IEDM 2017), not attention/transformer/embedding or language-model research","source_url":"https://doi.org/10.1109/iedm.2017.8268306","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Su's MIT PhD (1994) dissertation was on extreme-submicrometer silicon-on-insulator MOSFETs under Dimitri Antoniadis and James Chung — device physics, with no language-modeling lineage","source_url":"https://www.wikidata.org/wiki/Q18207172","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Su is president/CEO of AMD since 2014; AMD builds Instinct GPU accelerators used for AI training and inference — a hardware stack, not a language-modeling method she authored","source_url":"https://en.wikipedia.org/wiki/Lisa_Su","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named author on hardware compute-efficiency papers ('Innovation For the Next Decade of Compute Efficiency', ISSCC 2023), with no language-modeling or embedding/transformer authorship","source_url":"https://doi.org/10.1109/isscc42615.2023.10067810","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD EE, MIT (1994) on submicrometer SOI MOSFETs under Antoniadis and Chung — semiconductor device physics, not the AI/LM research core; she did not found AMD","source_url":"https://www.wikidata.org/wiki/Q18207172","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.81,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":56},{"id":35,"slug":"mira-murati","name":"Mira Murati","title":"Co-founder & CEO","company":"Thinking Machines Lab","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Mira_Murati","image_url":"/api/v1/ceo-ai-leaderboard/portrait/mira-murati.jpg","score":29,"tier":"informed_operator","dimensions":{"foundations":4,"vector_embeddings":3,"transformers_lm":6,"frontier_founder":5,"lm_domain_depth":6,"hands_on_engineering":8,"industry_impact":12,"scientific_founder":5},"rubric_version":3,"weighted_score":29,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Murati holds a B.Eng in mechanical engineering (Dartmouth, 2012) and a B.A. from Colby College — no graduate degree, thesis, or coursework record in linear algebra/optimization/statistical learning, and no personal authored papers in vector embeddings or the attention/transformer lineage. Her one identified individual publication is a non-technical essay, 'Language & Coding Creativity' (Daedalus, 2022), about creativity rather than ML research. Her OpenAlex record (3 works, h-index 3) consists entirely of large collaborative OpenAI system-card papers (Codex eval 2021, GPT-4o System Card, o1 System Card) where she appears as one of dozens-to-hundreds of co-authors in a corporate-report capacity typical of a CTO signing off on org output, not as a research contributor with a distinguishable technical contribution. Her career trajectory — Tesla product manager, Leap Motion product/engineering lead, OpenAI VP then CTO — is consistently product/engineering leadership rather than personal hands-on model-building; Wikipedia explicitly frames her OpenAI role as overseeing 'research, product and safety teams' rather than authoring research. She does get meaningful hands_on_engineering/industry_impact credit for having led (not built) the engineering org that shipped ChatGPT, DALL-E, Codex, and Sora, and for now running Thinking Machines Lab, but per the rubric's explicit instruction, fame/leadership/company-building is not itself research depth, and no verifiable personal foundational-math, embeddings, or transformer research record exists.\n\nMurati's only frontier-lineage authorship is as one of ~58 co-authors on the Codex paper 'Evaluating Large Language Models Trained on Code' (2021) and as a signatory on the GPT-4o and o1 system cards — corporate-report authorship that today's code/LLM stack draws on generally, but no method, architecture, optimizer, tokenizer or dataset named after her that frontier reports cite as a building block. Her verifiable language-modeling history is as an executive (OpenAI VP→CTO ~2018–2024, then TML) overseeing research/product/safety rather than personally authoring LM research — roughly 6–7 years of continuous LM-org leadership but no personal research record, so depth is thin against duration. As co-founder and CEO of Thinking Machines Lab (Feb 2025, ~1.5 yrs), the lab's technical publications (Defeating Nondeterminism, Modular Manifolds, LoRA Without Regret) are authored by Horace He, Jeremy Bernstein, John Schulman and Kevin Lu — not Murati — placing her squarely in the 'AI-company founder-CEO whose science is done by others' band, not a founder personally writing the core research/code/patents.","evidence":[{"claim":"B.Eng mechanical engineering, Dartmouth Thayer School, 2012; B.A. Colby College, 2011 — no graduate ML/math degree","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Career path was product/engineering management: PM at Tesla (2013-16), product+eng lead at Leap Motion (2016-18), VP then CTO at OpenAI (2018-2024) overseeing teams rather than authoring research","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Only identified personal-authorship publication is a non-technical essay on creativity, not ML research","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BA Colby College (2011), BEng mechanical engineering Dartmouth Thayer School (2012); Tesla Model X product manager 2013-2016; Leap Motion product/engineering 2016-2018; OpenAI 2018-2024 rising to CTO, led work on ChatGPT, DALL-E, Codex and Sora; co-founder and CEO of Thinking Machines Lab from Febru","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of 58 authors on 'Evaluating Large Language Models Trained on Code' (Codex, arXiv:2107.03374, July 2021)","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thinking Machines Lab technical publications (Defeating Nondeterminism in LLM Inference, Modular Manifolds, LoRA Without Regret, On-Policy Distillation) are authored by Horace He, Jeremy Bernstein, John Schulman and Kevin Lu; Murati is not an author on any of them","source_url":"https://thinkingmachines.ai/blog/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records her education at Colby, Dartmouth/Thayer and Pearson College UWC and her notable work as ChatGPT, DALL-E, GPT-4 and Thinking Machines Lab","source_url":"https://www.wikidata.org/wiki/Q116706551","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of ~58 authors on the Codex paper 'Evaluating Large Language Models Trained on Code' (arXiv:2107.03374), the frontier-lineage work she is an author on","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CTO of OpenAI May 2022–Sept 2024 overseeing research, product and safety teams; VP from 2018 — LM-org leadership, not personal authorship","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder & CEO of Thinking Machines Lab from Feb 2025; the lab's technical blog posts are authored by Horace He, Jeremy Bernstein, John Schulman and Kevin Lu, not Murati","source_url":"https://thinkingmachines.ai/blog/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of ~58 authors on 'Evaluating Large Language Models Trained on Code' (Codex); co-author (org capacity) on GPT-4o and o1 System Cards — no distinguishable personal frontier building block","source_url":"https://arxiv.org/abs/2107.03374","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAI CTO May 2022–Sep 2024 (VP from 2018) overseeing research/product/safety for ChatGPT, DALL-E, GPT-4; co-founder & CEO of Thinking Machines Lab from Feb 2025 — leadership over LM orgs, not personal LM research","source_url":"https://en.wikipedia.org/wiki/Mira_Murati","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Thinking Machines Lab technical publications are authored by Horace He, Jeremy Bernstein, John Schulman and Kevin Lu; Murati is not an author — the science is done by co-founders","source_url":"https://thinkingmachines.ai/blog/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":57},{"id":63,"slug":"shaw-walters","name":"Shaw Walters","title":"Founder & lead developer","company":"Eliza Labs / elizaOS (formerly ai16z)","sector":"crypto","profile_url":null,"image_url":"https://unavatar.io/x/shawmakesmagic?fallback=false","score":29,"tier":"informed_operator","dimensions":{"foundations":3,"vector_embeddings":7,"transformers_lm":6,"frontier_founder":2,"lm_domain_depth":4,"hands_on_engineering":13,"industry_impact":8,"scientific_founder":6},"rubric_version":3,"weighted_score":29,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Both passes correctly threw out the dossier, whose OpenAlex match is 'Warren Shaw. Walters', author of a 1973 Naval Postgraduate School thesis on Marine Corps officer assignment, producing a nonsensical timeline of 53 years active for a person whose record starts this decade. The dispute is that pass 2 found a publication pass 1 declared did not exist, and it is real: I verified that he is the first author of 'Eliza: A Web3 friendly AI Agent Operating System' (arXiv:2501.06781, January 2025), ahead of thirteen co-authors. That is a genuine first-author paper, but its content is decisive for how much it earns — it describes a TypeScript agent runtime that lets language-model agents read and write blockchain data and call smart contracts. It is a systems and framework paper, not a contribution to the mathematics, to representation learning, or to the attention/transformer lineage, so it lifts hands-on engineering rather than the research dimensions. The elizaOS repository is a substantial shipped artifact (~19.3k stars, MIT-licensed) whose core includes memory and knowledge management, local embedding generation via a local-inference plugin with runtime-managed GGUF models, and retrieval-augmented generation, which is personally shipped embedding and retrieval machinery and justifies pass 2's direction on vector_embeddings over pass 1's 4 — though wiring existing embedding models into an agent memory store is integration, not authorship of retrieval methods, so 9 overshoots. He has no degree, no thesis, no mathematics or ML publication, and he trains no models: he orchestrates other people's. Extensive media coverage concerns the ai16z DAO and token and is explicitly not evidence here.\n\nNothing of Walters's is a building block that frontier models (GPT/Claude/Gemini/Llama) descend from: elizaOS is a downstream TypeScript agent runtime that consumes those models' APIs, and arXiv:2501.06781 is a Web3 agent-OS systems paper with no architecture, optimizer, tokenizer, dataset, alignment or scaling contribution the frontier stack cites — a frontier-model APPLIER, not a foundation (score 3). His language-modeling record is adjacent and thin: he orchestrates other people's LLMs and wires in existing embedding/GGUF models for RAG memory, with no statistical/neural LM research, pretraining, seq2seq or transformer work; his AI-agent lineage traces only to roughly 2021-2022 via Webaverse/Magick, well after the pre-word2vec era, so this is under-3-years-of-real-LM-work / adjacent territory (score 4). He is, however, a genuine hands-on technical founder — founder and lead developer of Eliza Labs/elizaOS who personally authored the ~19.3k-star core framework and is first author of its paper — but the company's core is agent orchestration over others' models rather than language-modeling/embedding/transformer systems, and the verifiable founder tenure is only ~4 years (~2021→2025), placing him at the top of the technical-founder-outside-this-field band (score 7).","evidence":[{"claim":"First author of 'Eliza: A Web3 friendly AI Agent Operating System' (arXiv:2501.06781, January 2025) — verified author order begins Shaw Walters, Sam Gao, Shakker Nerd, ahead of 13 co-authors; describes an open-source TypeScript agentic framework integrating LLM agents with blockchain reads, writes a","source_url":"https://arxiv.org/abs/2501.06781","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"elizaOS/eliza is an MIT-licensed TypeScript agentic operating system (~19.3k stars, 5.7k forks) providing memory and knowledge workflows, local embeddings via a local-inference plugin with runtime-managed GGUF models, RAG, and multiple model providers","source_url":"https://raw.githubusercontent.com/elizaOS/eliza/main/README.md","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub profile 'lalalune' (Shaw, San Francisco), member of the Eliza Labs and elizaOS organizations, creator of elizaOS, with pinned AI projects Magick, CharacterStudio and autocoder","source_url":"https://github.com/lalalune","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder of Eliza Labs and lead developer of the ElizaOS open-source AI agent framework, formerly known as ai16z","source_url":"https://iq.wiki/wiki/shaw-walters","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Path into AI ran through blockchain/game development (NFT projects, Webaverse metaverse AI characters) where he learned Solidity before building agent infrastructure","source_url":"https://www.blockchaingamer.biz/features/interviews/41648/shaw-walters-elizaos-ai-agents-blockchain-gaming/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Early GPT-3 tester before ChatGPT's release, cited as basis for his interest in AI agents","source_url":"https://joetechnologist.com/elizaos-the-age-of-ai-agents-a-conversation-with-shaw-walters/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Eliza: A Web3 friendly AI Agent Operating System' (2025), an open-source agentic framework integrating LLM agents with blockchain operations as a TypeScript program","source_url":"https://arxiv.org/abs/2501.06781","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"elizaOS/eliza is an MIT-licensed TypeScript agentic operating system with ~19.3k stars providing memory/knowledge workflows, local embeddings via a local-inference plugin with runtime-managed GGUF models, RAG, and multiple model providers","source_url":"https://raw.githubusercontent.com/elizaOS/eliza/main/README.md","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv:2501.06781 'Eliza: A Web3 friendly AI Agent Operating System' is a TypeScript agent-runtime/systems paper integrating LLM agents with blockchain ops — no attention/transformer/scaling/alignment contribution that frontier model reports build on","source_url":"https://arxiv.org/abs/2501.06781","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"elizaOS is an MIT-licensed agent framework that plugs into existing model providers and does local embeddings/RAG over runtime-managed GGUF models — orchestration and integration, not authored LM research","source_url":"https://raw.githubusercontent.com/elizaOS/eliza/main/README.md","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Walters is founder of Eliza Labs and lead developer/creator of the elizaOS (formerly ai16z) open-source agent framework — the technical founder who personally built the core code","source_url":"https://iq.wiki/wiki/shaw-walters","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His path into AI ran through blockchain/game development (NFT projects, Webaverse metaverse AI characters, Magick) circa 2021-2022, learning Solidity before building agent infrastructure — no language-modeling research history","source_url":"https://www.blockchaingamer.biz/features/interviews/41648/shaw-walters-elizaos-ai-agents-blockchain-gaming/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"elizaOS/eliza is an MIT-licensed agent framework that wires in existing model providers and local GGUF embeddings for memory/RAG; Walters is its creator and lead developer, i.e. the hands-on technical founder of Eliza Labs (formerly ai16z), a company established in 2024","source_url":"https://iq.wiki/wiki/shaw-walters","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Walters is self-taught, entering AI via blockchain/game development (NFT projects, Webaverse metaverse AI characters, Magick) ~2021-2022 before building agent infrastructure — no language-modeling research or model training in the record","source_url":"https://www.blockchaingamer.biz/features/interviews/41648/shaw-walters-elizaos-ai-agents-blockchain-gaming/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":58},{"id":40,"slug":"alexandr-wang","name":"Alexandr Wang","title":"Chief AI Officer, Meta / Co-Founder (former CEO), Scale AI","company":"Meta Superintelligence Labs (formerly Scale AI)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Alexandr_Wang","image_url":"/api/v1/ceo-ai-leaderboard/portrait/alexandr-wang.jpg","score":28,"tier":"informed_operator","dimensions":{"foundations":5,"vector_embeddings":3,"transformers_lm":6,"frontier_founder":6,"lm_domain_depth":4,"hands_on_engineering":6,"industry_impact":11,"scientific_founder":6},"rubric_version":3,"weighted_score":28,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Wang has no completed degree in a technical field and no personal record of authored research in linear algebra/optimization/statistical learning, embeddings, or the transformer/LM lineage: he attended MIT intending to study machine learning but dropped out after one year (2016) to found Scale AI, and prior to that worked as a software engineer at Quora and Addepar, plus strong pre-college competitive math/programming results (Math Olympiad Program, US Physics Team, USACO) which show aptitude but are not verifiable research. Scale AI itself is a data-labeling/annotation and model-evaluation infrastructure business — Wang built and led the company (CEO for ~9 years) but the searched record shows him as a business/organizational leader rather than a hands-on model builder; OpenAlex/Semantic Scholar list him as a co-author on a handful of recent (2024-2026) multi-author policy/benchmark papers (WMDP unlearning benchmark, a Nature academic-benchmark paper, and a 'Superintelligence Strategy' position paper), which are large-consortium outputs consistent with an executive-sponsor/co-author role rather than a first-author technical contribution, and none rise to canonical original transformer/embedding research. His industry_impact score reflects that he built and scaled a company whose core product (human-in-the-loop data labeling and RLHF/eval infrastructure for foundation-model training) genuinely sits adjacent to LM training pipelines, and he now leads Meta's Superintelligence Labs, but per the rubric this is leadership/infrastructure-provisioning impact, not personal foundational research.\n\nWang authored no architecture, optimizer, tokenizer, embedding, scaling result or alignment method that today's frontier models (GPT/Claude/Gemini/Llama) technically descend from; his lineage is company-level — Scale AI's human-labeled RLHF/eval data pipelines are consumed by frontier labs, but that is infrastructure Scale provided, not a personal foundational building block Wang wrote, so this stays in the 'applies/supplies, no named contribution' band. His personal language-modeling record is thin and recent: the first verifiable technical papers are 2024-2026 multi-author benchmark/policy consortium outputs (WMDP, Humanity's Last Exam, a Nature academic-benchmark paper) consistent with an executive-sponsor co-author role, well under three years of any hands-on LM record, with Scale AI (2016-2025) merely adjacent as a data-labeling business. He was founder-CEO of Scale AI for ~9 years, but the rubric explicitly bars a business founder of an 'AI company' whose science and engineering were done by others from the scientific/technical-founder tier — there is no evidence Wang personally authored the core models, research or patents Scale runs on.","evidence":[{"claim":"Wang briefly attended MIT intending to study machine learning, dropped out after his freshman year (summer 2016) to found Scale AI with Lucy Guo — no degree completed","source_url":"https://en.wikipedia.org/wiki/Alexandr_Wang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Before MIT, Wang worked as a software engineer at Quora and at Addepar; his pre-college record includes Math Olympiad Program (2013), US Physics Team (2014), USACO finalist (2012, 2013)","source_url":"https://www.entrepreneur.com/business-news/who-is-alexandr-wang-the-founder-of-scale-ai-joining-meta/493281","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Scale AI's business is AI data labeling / annotation and model evaluation services (Remotasks, Outlier), not model research; Wang co-founded it in 2016 and served as CEO until stepping down in 2025 to become Meta's Chief AI Officer","source_url":"https://en.wikipedia.org/wiki/Scale_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The WMDP Benchmark paper (arXiv 2403.03218) is a large multi-institution consortium paper led by researchers at the Center for AI Safety (Nathaniel Li, Dan Hendrycks et al.), consistent with Wang appearing as an organizational/sponsoring co-author rather than lead technical author","source_url":"https://arxiv.org/abs/2403.03218","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Briefly attended MIT and dropped out to co-found Scale AI in 2016 with Lucy Guo; prior roles as software engineer at Addepar, programmer at Quora, intern at Hudson River Trading; Scale AI provides data labeling and LLM evaluation services; Chief AI Officer of Meta and head of Superintelligence Labs","source_url":"https://en.wikipedia.org/wiki/Alexandr_Wang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed among 56 authors of 'The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning' (2024) — a hazardous-knowledge evaluation benchmark and unlearning method.","source_url":"https://arxiv.org/abs/2403.03218","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed among 1,155+ authors of 'Humanity's Last Exam' (2025), a 2,500-question LLM evaluation benchmark.","source_url":"https://arxiv.org/abs/2501.14249","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Scale AI's business is AI data labeling/annotation and LLM evaluation (Remotasks, Outlier), not model architecture research; Wang co-founded it in 2016 and served as CEO until 2025 — a business/organizational founder role, not a hands-on model builder","source_url":"https://en.wikipedia.org/wiki/Scale_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The WMDP Benchmark (arXiv 2403.03218, 2024) is a ~56-author multi-institution consortium paper led by the Center for AI Safety, consistent with Wang as an organizational/sponsoring co-author rather than a first-author technical contributor; earliest verifiable Wang publication year is 2024","source_url":"https://arxiv.org/abs/2403.03218","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wang attended MIT ~1 year with no completed degree and no personal authored research in the transformer/embedding/LM lineage; he leads Meta Superintelligence Labs as Chief AI Officer, a leadership role","source_url":"https://en.wikipedia.org/wiki/Alexandr_Wang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wang is a co-author among ~56 authors of the WMDP unlearning benchmark and appears in large-consortium eval/policy papers rather than as first-author of foundational LM research","source_url":"https://arxiv.org/abs/2403.03218","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Scale AI is a data-labeling/annotation and LLM-evaluation infrastructure business; Wang co-founded it in 2016 and served as CEO until 2025, i.e. as founder-CEO of an AI-services company, not as author of the models","source_url":"https://en.wikipedia.org/wiki/Scale_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wang attended MIT ~1 year with no completed degree and no personal record of authored transformer/embedding/optimization research; earliest verifiable publication year is 2024","source_url":"https://en.wikipedia.org/wiki/Alexandr_Wang","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.81,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":59},{"id":60,"slug":"ala-shaabana","name":"Ala Shaabana","title":"Co-founder, Opentensor Foundation (Bittensor)","company":"Bittensor (Opentensor Foundation)","sector":"crypto","profile_url":null,"image_url":null,"score":27,"tier":"informed_operator","dimensions":{"foundations":8,"vector_embeddings":3,"transformers_lm":5,"frontier_founder":2,"lm_domain_depth":4,"hands_on_engineering":9,"industry_impact":7,"scientific_founder":8},"rubric_version":3,"weighted_score":27,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Shaabana has a real but narrow academic record that sits outside the lineage this index measures. I confirmed directly against OpenAlex that author A5058692441 is the correct person and not a homonym — the affiliations are University of Windsor (2013-2014), McMaster University (2014-2019) and University of Waterloo (2023), matching his known career — and that the 16 indexed works are on wearable electromyography, sensor networks, textiles, thermoregulation and green/optical networking. None of that is mathematics of learning, embeddings or language modelling; the profile's 'artificial intelligence' concept tag at 0.70 is a topic-classifier artifact, not authored AI work. His single document in the lineage is co-authorship of 'BitTensor: A Peer-to-Peer Intelligence Market' (Rao, Steeves, Shaabana, Attevelt, McAteer, arXiv:2003.03917), a mechanism-design proposal for a market in which peers rank one another by training neural networks — and I verified on arXiv that v3 (10 November 2021) is a WITHDRAWAL whose authors' note states the paper 'is incomplete', that one author has been removed, and that it is 'now obsolete from both a content and an author perspective'. A self-disowned whitepaper is weak evidence and cannot support scores in the PhD-level or canonical band. He is a genuine hands-on co-founder and builder of the Bittensor protocol, and the network does host language-model training subnets, which is real if indirect industry impact. His graduate-level CS research training is verified by the publication record itself, so foundations sits at the base of the strong-graduate-training band; the embedding and transformer dimensions stay low because no authored or shipped work in vector representations, attention or pretraining is verifiable under his name.\n\nNo verifiable lineage runs from Shaabana's own work into frontier models (GPT/Claude/Gemini/Llama): his sole document in the lineage, arXiv:2003.03917 'BitTensor: A Peer-to-Peer Intelligence Market,' was withdrawn by its own authors on 10 Nov 2021 as 'incomplete' and 'obsolete,' and no frontier technical report cites or builds on it. He has no personal, continuous record in language modeling specifically — his 16-work academic corpus is wearable EMG, sensor networks, textiles and optical networking, not statistical/neural LMs, seq2seq, transformers or pretraining — so lm_domain_depth is adjacent at best (~3-7 band, low). He is, however, a genuine hands-on technical co-founder of Bittensor / the Opentensor Foundation (a live decentralized-ML network that hosts LM training subnets) for roughly six years (2020-2026), which places scientific_founder in the 3-8-year band; it is held down because the core incentive-protocol science is attributed in primary sources to Yuma Rao and Jacob Steeves rather than to him.","evidence":[{"claim":"OpenAlex author A5058692441 'Ala Shaabana': 16 works, 32 citations, h-index 3; affiliations University of Waterloo (2023), University of Windsor (2013, 2014), McMaster University (2014, 2015, 2017, 2019); primary topics are building-energy optimization, textile materials and thermoregulation, not ma","source_url":"https://api.openalex.org/authors/A5058692441","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'BitTensor: A Peer-to-Peer Intelligence Market' with Yuma Rao, Jacob Steeves, Daniel Attevelt and Matthew McAteer (arXiv:2003.03917, v1 March 2020)","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv v3 (10 November 2021) is a withdrawal; the authors' note states 'This paper is incomplete... one of the authors (daniel attevelt) has been removed from the work and so this paper is now obsolete from both a content and an author perspective'","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Portable Electromyography: A Case Study on Ballistic Finger Movement Recognition', IEEE Sensors Journal 2019 — representative of his indexed corpus (wearable sensing, not AI core)","source_url":"https://doi.org/10.1109/jsen.2019.2908312","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The active Bittensor/subtensor codebase is maintained under the opentensor GitHub organization he co-founded","source_url":"https://github.com/opentensor/bittensor","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ala Shaabana is a listed co-author of 'BitTensor: A Peer-to-Peer Intelligence Market' alongside Yuma Rao, Jacob Steeves, Daniel Attevelt, and Matthew McAteer.","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The BitTensor arXiv paper (v3, Nov 2021) was withdrawn by Ala Shaabana, with an authors' note describing it as incomplete and obsolete from both a content and an author perspective.","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex profile for 'Ala Shaabana' (id A5058692441) lists 16 works, h-index 3, cited_by_count 32, affiliations at University of Waterloo/Windsor/McMaster, topics in wearable sensing, optical networks, textiles and thermoregulation -- none in vector embeddings or transformer/LM research -- with the","source_url":"https://doi.org/10.1109/jsen.2019.2908312","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar independently lists an 'A. Shaabana' author profile (id 1788032) with 20 papers, 61 citations, h-index 4, consistent in scale with the OpenAlex profile but not independently confirmed as the same Bittensor co-founder.","source_url":"https://www.semanticscholar.org/author/1788032","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The GitHub account 'unconst' (bio: opentensor/bittensor.com), which authors the active Bittensor/subtensor codebase, is associated with Jacob Steeves rather than Ala Shaabana, and no README or repository documentation names Shaabana's specific technical contributions to the current protocol.","source_url":"https://github.com/opentensor/bittensor","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv:2003.03917 v3 (10 Nov 2021) is a withdrawal; authors' note states the paper is 'incomplete' and 'now obsolete from both a content and an author perspective' — no frontier-model lineage","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5058692441 'Ala Shaabana': 16 works on sensor networks, wearable EMG, textiles, thermoregulation and optical networking — none in language modeling, embeddings or transformers","source_url":"https://api.openalex.org/authors/A5058692441","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ala Shaabana is a hands-on co-founder of Bittensor; the active protocol codebase is maintained under the opentensor GitHub organization","source_url":"https://github.com/opentensor/bittensor","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Shaabana co-authored 'BitTensor: A Peer-to-Peer Intelligence Market' (arXiv:2003.03917), whose v3 (10 Nov 2021) is an authors' withdrawal calling it incomplete and obsolete — not a building block cited by frontier LMs","source_url":"https://arxiv.org/abs/2003.03917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The active Bittensor/subtensor codebase (decentralized network hosting LM-training subnets) is maintained under the opentensor GitHub organization Shaabana co-founded","source_url":"https://github.com/opentensor/bittensor","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex A5058692441 lists 16 works in sensor networks, textiles, thermoregulation and optical networking — no authored language-modeling, embedding or transformer research under his name","source_url":"https://api.openalex.org/authors/A5058692441","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.78,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":60},{"id":57,"slug":"alex-skidanov","name":"Alex Skidanov","title":"Co-founder, NEAR Protocol (formerly NEAR.ai)","company":"NEAR Protocol","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/alex-skidanov.jpg","score":27,"tier":"informed_operator","dimensions":{"foundations":6,"vector_embeddings":2,"transformers_lm":6,"frontier_founder":2,"lm_domain_depth":3,"hands_on_engineering":13,"industry_impact":7,"scientific_founder":7},"rubric_version":3,"weighted_score":27,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Skidanov has one genuine entry in the lineage, which the dossier and one assessor missed entirely. I verified directly on arXiv that he is the second author, with Illia Polosukhin, of 'Neural Program Search: Solving Programming Tasks from Description and Examples' (arXiv:1802.04335, 12 February 2018), which trains a Seq2Tree neural model to guide search over a domain-specific language and reports outperforming a sequence-to-sequence-with-attention baseline. That is hands-on work training attention-era neural models, not merely using them, and it was the technical substance of NEAR.ai, the program-synthesis startup he and Polosukhin founded in 2017 before pivoting to the NEAR sharded blockchain in late 2018. It is, however, a single workshop-level paper from a roughly one-year AI phase, with no pretraining, scaling or alignment work following it, so it supports the lower-middle band and not the PhD-level anchor. His deeper and better-documented strength is systems engineering: Director of Engineering at MemSQL/SingleStore, co-author of 'A column store engine for real-time streaming analytics' (ICDE 2016), and principal designer of NEAR's Nightshade sharding — demanding distributed-systems work that is not AI infrastructure. I found no publication, patent or shipped system by him on vector embeddings, dense retrieval or vector search, and no verifiable formal training in optimization or matrix methods. His indexed citations total in the tens, so there is no citation-based industry impact; the transformer pedigree often associated with NEAR belongs to co-founder Polosukhin, an 'Attention Is All You Need' co-author, and must not be transferred to Skidanov.\n\nNothing of Skidanov's own is a building block of today's frontier models: his single AI-lineage paper, 'Neural Program Search' (arXiv:1802.04335, 2018, with Polosukhin), trains a Seq2Tree model and merely benchmarks against a seq2seq-with-attention baseline — it is not cited by or built into any GPT/Claude/Gemini/Llama technical report, and the 'Attention Is All You Need' pedigree associated with NEAR belongs to Polosukhin, not him (frontier_founder 2). His verifiable language-modeling record is that one workshop-level paper from the ~12-month Near.ai program-synthesis phase (2017–2018); there is no earlier vector-space/LSI/n-gram work and no later pretraining/scaling/alignment work, i.e. under three years, intermittent and adjacent (lm_domain_depth 3). He is a genuine scientific/technical founder — he co-founded NEAR in 2017, personally co-authored its AI research and designed the Nightshade sharding protocol — but that ~9-year founder record is in distributed systems and blockchain, a technical founder OUTSIDE the language-modeling field, which caps scientific_founder in the 3–7 band (7).","evidence":[{"claim":"Co-author with Illia Polosukhin of 'Neural Program Search: Solving Programming Tasks from Description and Examples', arXiv:1802.04335, submitted 12 February 2018; combines deep learning and program synthesis via a Seq2Tree model and outperforms a sequence-to-sequence-with-attention baseline","source_url":"https://arxiv.org/abs/1802.04335","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'A column store engine for real-time streaming analytics', ICDE 2016 — his only OpenAlex-indexed work, 13 citations","source_url":"https://doi.org/10.1109/icde.2016.7498332","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR Protocol was founded in early 2017 by Illia Polosukhin and Alexander Skidanov, initially as Near.ai, an AI/program-synthesis startup, before pivoting to a sharded blockchain; Skidanov was previously Director of Engineering at MemSQL and a software engineer at Microsoft","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record 3419692: 2 papers, 28 citations, h-index 2","source_url":"https://www.semanticscholar.org/author/Alex-Skidanov/3419692","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Director of Engineering at MemSQL (now SingleStore), software engineer at Microsoft prior to NEAR","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded NEAR Protocol in early 2017 with Illia Polosukhin","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'A column store engine for real-time streaming analytics' (ICDE 2016), a database-systems paper unrelated to AI/ML","source_url":"https://doi.org/10.1109/icde.2016.7498332","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR Protocol founded early 2017 by Illia Polosukhin and Alexander Skidanov; initially established as Near.ai, an AI startup focused on program synthesis, pivoting to sharded blockchain from late 2018; Skidanov was previously Director of Engineering at MemSQL and a software engineer at Microsoft","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record: 2 papers, 28 citations, h-index 2","source_url":"https://www.semanticscholar.org/author/Alex-Skidanov/3419692","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Skidanov is one of only two authors (with Illia Polosukhin) of 'Neural Program Search', a Seq2Tree program-synthesis model benchmarked against a seq2seq-with-attention baseline — his sole LM-adjacent publication, from a ~one-year AI phase","source_url":"https://arxiv.org/abs/1802.04335","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR was founded in 2017 by Polosukhin and Skidanov as Near.ai (program synthesis) before pivoting to a sharded blockchain from late 2018; Skidanov personally led engineering (previously Director of Engineering at MemSQL) — a genuine technical-founder role of ~9 years, but the company's core is a bl","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Neural Program Search (arXiv:1802.04335, 2018) with Polosukhin trains a Seq2Tree model and compares against a seq2seq-with-attention baseline; it is not a named building block of frontier LMs","source_url":"https://arxiv.org/abs/1802.04335","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"NEAR Protocol was founded early 2017 by Illia Polosukhin and Alexander Skidanov, initially as Near.ai (AI/program synthesis), pivoting to a sharded blockchain from late 2018; Skidanov was previously Director of Engineering at MemSQL and a software engineer at Microsoft","source_url":"https://en.wikipedia.org/wiki/NEAR_Protocol","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Skidanov is credited as a principal designer of NEAR's Nightshade sharding, a distributed-systems (not language-modeling) protocol","source_url":"https://near.org/papers/nightshade","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.85,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":61},{"id":37,"slug":"mustafa-suleyman","name":"Mustafa Suleyman","title":"CEO, Microsoft AI","company":"Microsoft AI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","image_url":"/api/v1/ceo-ai-leaderboard/portrait/mustafa-suleyman.jpg","score":27,"tier":"informed_operator","dimensions":{"foundations":3,"vector_embeddings":4,"transformers_lm":6,"frontier_founder":4,"lm_domain_depth":6,"hands_on_engineering":5,"industry_impact":12,"scientific_founder":4},"rubric_version":3,"weighted_score":27,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Suleyman has no completed degree -- he enrolled in philosophy and theology at Oxford and dropped out at 19 -- and no PhD, publications record, or personal engineering track record in the mathematics, vector-embeddings, or transformer research core. At DeepMind (co-founded 2010) his own role was Head of Applied AI / Chief Product Officer: integrating DeepMind's technology into Google products, launching DeepMind Health, and founding DeepMind Ethics & Society, i.e. applied/business/policy leadership, not research. He appears as the 6th of 7 authors on 'Teaching Machines to Read and Comprehend' (Hermann et al. 2015), a genuinely important reading-comprehension/attention paper, but the ordering and his documented applied/ops role at the time (not a research scientist) indicate a leadership/co-founder authorship credit rather than personal technical authorship of the method. He co-founded Inflection AI (2022, chatbot 'Pi') and now runs Microsoft AI as an executive. Industry impact is scored moderately for building/leading consequential AI organizations (DeepMind co-founder, Inflection AI co-founder, Microsoft AI CEO), but the rubric explicitly excludes fame/business success from the research dimensions, and no verifiable personal research or engineering record was found to support higher scores there.\n\nSuleyman's personal contribution to the frontier stack is a leadership-credit co-authorship (6th of 7) on 'Teaching Machines to Read and Comprehend' (Hermann et al., 2015), whose CNN/DailyMail reading-comprehension dataset is a genuine LM-lineage benchmark, but he did not author any named building block (architecture, attention, embeddings, optimizer, tokenizer, alignment method) that GPT/Claude/Gemini-class models descend from, so frontier_founder is low. His language-modeling record is real but organizational and continuous only as an executive/product leader — DeepMind's applied side (2010), an LM-lineage co-author (2015), Inflection AI's Pi (2022) and Microsoft AI/Copilot (2024) — roughly a decade adjacent to LM without a hands-on personal research trail. He is repeatedly the business/product/applied co-founder (Demis Hassabis and Shane Legg were DeepMind's scientific founders; Karén Simonyan was Inflection's Chief Scientist), i.e. founder/CEO of AI companies whose core science was done by others, which places scientific_founder in the low-to-mid band rather than the technical-founder anchor.","evidence":[{"claim":"Dropped out of Oxford at 19, no completed degree","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"At DeepMind (co-founded 2010) served as Head of Applied AI / Chief Product Officer, launched DeepMind Health and DeepMind Ethics & Society -- applied/business/ethics roles, not research scientist","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as 6th of 7 authors on 'Teaching Machines to Read and Comprehend' (Hermann, Kocisky, Grefenstette, Espeholt, Kay, Suleyman, Blunsom, 2015)","source_url":"https://arxiv.org/abs/1506.03340","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Inflection AI (2022) and is now CEO of Microsoft AI (since March 2024)","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Enrolled in philosophy and theology at Oxford, dropped out at 19; co-founded DeepMind 2010 as chief product officer, later head of applied AI; launched DeepMind Health 2016 and DeepMind Ethics & Society; placed on administrative leave in 2019 over bullying allegations, then moved to a Google policy","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (hkDGEVQAAAAJ): ~29,404 citations, h-index 26, i10 35, with no first-author papers; top entries are Kinetics dataset, Teaching Machines to Read and Comprehend, breast-cancer screening, clinical-impact commentary, retinal disease","source_url":"https://scholar.google.com/citations?user=hkDGEVQAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sixth of seven authors on 'Teaching Machines to Read and Comprehend' (Hermann, Kocisky, Grefenstette, Espeholt, Kay, Suleyman, Blunsom), NIPS 2015","source_url":"https://arxiv.org/abs/1506.03340","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex profile A5001712647: 51 works, h-index 20, dominant topics are healthcare AI, digital innovation and interdisciplinary technology-and-society rather than machine-learning methods","source_url":"https://api.openalex.org/authors/A5001712647","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (6th of 7) on 'Teaching Machines to Read and Comprehend', an early attention/reading-comprehension paper whose CNN/DailyMail dataset is a cited LM-lineage benchmark, but in a leadership-credit position, not method author","source_url":"https://arxiv.org/abs/1506.03340","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded DeepMind (2010) as head of applied AI / chief product officer and co-founded Inflection AI (2022) building the Pi LLM before becoming CEO of Microsoft AI (2024) — leadership of LM-building orgs rather than hands-on LM research","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Inflection AI's scientific/technical lead was co-founder and Chief Scientist Karén Simonyan; Suleyman was CEO — the science was executed by others","source_url":"https://en.wikipedia.org/wiki/Inflection_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (6th of 7) on 'Teaching Machines to Read and Comprehend' (2015), the CNN/DailyMail reading-comprehension dataset, in an Applied-AI/product role rather than as method author","source_url":"https://arxiv.org/abs/1506.03340","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Inflection AI in 2022 (conversational LLM Pi) and is CEO of Microsoft AI since March 2024, both LM-centric orgs led as an executive; Karén Simonyan served as Inflection's chief scientist","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"At DeepMind (co-founded 2010) his role was Head of Applied AI / Chief Product Officer — applied/business/ethics leadership, with Hassabis and Legg leading the science","source_url":"https://en.wikipedia.org/wiki/Mustafa_Suleyman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":62},{"id":62,"slug":"ron-bodkin","name":"Ron Bodkin","title":"Founder & CEO","company":"Theoriq (ChainML)","sector":"crypto","profile_url":null,"image_url":"https://unavatar.io/x/ronbodkin?fallback=false","score":27,"tier":"informed_operator","dimensions":{"foundations":6,"vector_embeddings":6,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":10,"industry_impact":9,"scientific_founder":6},"rubric_version":3,"weighted_score":27,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Bodkin holds CS degrees from McGill and MIT and has a 15+ year applied-ML engineering career: Technical Director of Applied AI at Google Cloud CTO office, engineering lead at the Vector Institute, and founder of Think Big Analytics (data science consultancy acquired by Teradata). He is a co-author (7th of 8 authors, engineering-lead position) on a 2022 SIGIR paper on VAE-based recommender systems mitigating filter bubbles, which is genuine applied representation-learning/ML work, though his position in the author list suggests an engineering-support rather than principal-researcher role. His earlier technical record (2003 OOPSLA paper on AspectJ/middleware) is software-engineering, not AI. No evidence of authored foundational math/optimization theory, embeddings research, or transformer/LM work — his AI record is applied engineering leadership, not core research. Theoriq/ChainML is an AI-agent infrastructure company built on top of existing LLMs, not a model-building lab.\n\nNothing of Bodkin's authored or built work sits in the lineage today's frontier models descend from — his single ML research artifact is a 7th-of-8-author 2022 SIGIR paper on VAE-based recommender diversification, not attention, embeddings, tokenizers, optimizers, scaling or alignment, and Theoriq/ChainML is agent infrastructure that consumes existing LLMs rather than trains them (frontier_founder=2). He has no verifiable continuous language-modeling record: his career is applied-ML/data-engineering leadership (Think Big Analytics, Teradata Kylo, Google Cloud applied AI, Vector Institute engineering), with the recommender paper being representation learning adjacent to but not within statistical/neural LM work (lm_domain_depth=3). He is a genuine technical founder-CEO — Think Big Analytics (~2010–2014, acquired by Teradata) and ChainML/Theoriq (2022–present, ~8 years total) — but the science sits outside the language-modeling core, so he fits the 'technical founder outside this field' band (scientific_founder=6).","evidence":[{"claim":"CS degrees from McGill and MIT; 15+ years in AI/big data before founding ChainML/Theoriq in 2022","source_url":"https://podcasts.apple.com/us/podcast/ron-bodkin-chainml-founder-and-ceo-and-ex-google/id1476885647?i=1000622925860","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Technical director on Google's applied AI team in the Cloud CTO office; previously founded and was CEO of Think Big Analytics (enterprise big data, data science, data engineering), which was acquired by Teradata where he led the Kylo open-source data lake framework and helped establish Teradata's AI","source_url":"https://www.oreilly.com/people/ron-bodkin/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5022442588, affiliation Vector Institute (Canada), 4 works, 60 citations, h-index 3; research topics are software system performance and reliability, software engineering methodologies, service-oriented architecture, with one recommender-systems entry","source_url":"https://api.openalex.org/authors/A5022442588","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar record (8 papers, 86 citations, h-index 4): 'Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems' (2022, with Gao, Shen, Mai, Bouadjenek, Waller, Anderson, Sanner), 'Using AspectJ for component integration in middlewa","source_url":"https://api.semanticscholar.org/graph/v1/author/2261058?fields=name,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount,papers.authors","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Theoriq describes itself as a DeFi strategy curator for tokenized assets using autonomous AI to monitor rates and liquidity, surface signal and validate executions, operating human-in-the-loop where 'Curators decide. AI streamlines'","source_url":"https://www.theoriq.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Theoriq is a DeFi strategy curator using autonomous AI agents on top of existing models, not a model-training lab whose work frontier LLMs build on","source_url":"https://www.theoriq.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded and was CEO of Think Big Analytics (data science/engineering consultancy, acquired by Teradata) and later founder-CEO of ChainML/Theoriq — technical-founder roles, but in data engineering and agent infra, not language modeling","source_url":"https://www.oreilly.com/people/ron-bodkin/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5022442588: 4 works, 60 citations, h-index 3; sole AI-topic paper is the 2022 SIGIR filter-bubble/VAE recommender work, remaining record is software-engineering (AspectJ/middleware) — no transformer/embedding/LM lineage work","source_url":"https://api.openalex.org/authors/A5022442588","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bodkin's applied-ML engineering leadership record (founder/CEO Think Big Analytics acquired by Teradata, led Kylo data-lake framework; Technical Director Applied AI in Google Cloud CTO office) — data/ML engineering, not language-modeling research","source_url":"https://www.oreilly.com/people/ron-bodkin/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Theoriq (ChainML) is a DeFi strategy-curation platform using autonomous AI agents on top of existing LLMs ('Curators decide. AI streamlines'), not a foundation-model or LM research lab","source_url":"https://www.theoriq.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.76,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":63},{"id":65,"slug":"ari-juels","name":"Ari Juels","title":"Weill Family Foundation Professor, Cornell Tech; Co-director, IC3; Chief Scientist, Chainlink Labs","company":"Chainlink Labs","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Ari_Juels","image_url":"https://unavatar.io/x/AriJuels?fallback=false","score":26,"tier":"informed_operator","dimensions":{"foundations":11,"vector_embeddings":2,"transformers_lm":3,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":7,"industry_impact":10,"scientific_founder":6},"rubric_version":3,"weighted_score":26,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Juels is a distinguished researcher whose entire canonical corpus is applied cryptography and security, not the AI lineage this index measures. His 1996 UC Berkeley PhD was advised by Alistair Sinclair, a randomized-algorithms and Markov-chain theorist, and his canonical work — the fuzzy commitment scheme (1999), the fuzzy vault (2003), Proofs of Retrievability (CCS 2007), and later Town Crier/DECO/MEV — rests on real probability, coding theory and information-theoretic construction. That is genuine mathematical rigor and earns foundations at the top of the strong-graduate-training band, but it is not linear algebra, optimization or statistical learning for neural networks, so it does not reach the PhD-level-in-the-core anchor. I verified his single substantive machine-learning publication directly: 'Stealing Machine Learning Models via Prediction APIs' (Tramer, Zhang, Juels, Reiter, Ristenpart, USENIX Security 2016, arXiv:1609.02943), which extracts logistic-regression, neural-network and decision-tree models through black-box prediction APIs — an important security result about ML systems, not a contribution to embeddings, attention, pretraining or scaling. His own site lists his interests as blockchain technologies, AI security, applied cryptography and privacy, with no ML, embedding or language-model work; no authored work in the lineage exists. Industry impact is real but earned in security and oracle organizations (RSA Labs Chief Scientist, IC3 co-founder, Chainlink Labs), whose core is cryptography and distributed systems rather than language modeling, so it is credited on adjacency, not on AI systems the field runs on.\n\nNothing of Juels's authored corpus is a building block of frontier language models: his lineage is applied cryptography, RFID/biometric security, proofs-of-retrievability and blockchain oracles (Town Crier, DECO, Chainlink), none of which appears in the architecture, training or alignment stack of GPT/Claude/Gemini/Llama-class systems; his one ML-adjacent paper ('Stealing Machine Learning Models via Prediction APIs', USENIX 2016) is an adversarial-security result, not a frontier component. He has zero verifiable years in language modeling specifically — 30 years of research, but continuously in cryptography and security, not statistical/neural LMs, embeddings, seq2seq or transformers. He IS a genuine scientific/technical founder — IC3 co-founder/co-director (2014) and Chainlink Labs Chief Scientist and co-author of the 2017 Chainlink white paper (~9 years) — but that founder role is in crypto/oracles, squarely OUTSIDE the language-modeling field this index measures, which caps scientific_founder in the 'technical founder outside this field' band.","evidence":[{"claim":"PhD in computer science, UC Berkeley 1996, doctoral advisor Alistair Sinclair; Weill Professor at Cornell Tech and co-director of IC3","source_url":"https://www.wikidata.org/wiki/Q102320479","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Stealing Machine Learning Models via Prediction APIs' (Tramer, Zhang, Juels, Reiter, Ristenpart, USENIX Security 2016) — extraction attacks against logistic regression, neural networks and decision trees via black-box APIs; a security result about ML systems","source_url":"https://arxiv.org/abs/1609.02943","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His own faculty page states his research areas are blockchain technologies, AI security, applied cryptography and privacy — no machine-learning, embedding or language-model research is listed","source_url":"https://www.arijuels.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cornell Tech biography lists blockchains, cryptocurrency, smart contracts, applied cryptography and user authentication as his research areas","source_url":"https://www.tech.cornell.edu/people/ari-juels/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD 1996, UC Berkeley, advisor Alistair Sinclair; currently Weill Family Foundation Professor at Cornell Tech and co-director of IC3","source_url":"https://en.wikipedia.org/wiki/Ari_Juels","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cornell Tech faculty bio: research areas are blockchains, cryptocurrency, smart contracts, applied cryptography, user authentication, and privacy; previously Chief Scientist and Director of RSA Laboratories","source_url":"https://www.tech.cornell.edu/people/ari-juels/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD UC Berkeley 1996; 17 years at RSA Security, Chief Scientist from 2007; Weill Professor at Cornell Tech; co-director of IC3; Chief Scientist at Chainlink Labs and co-author of the 2017 Chainlink white paper; canonical work includes fuzzy commitment, fuzzy vault, PORs, client puzzles, proof-of-wor","source_url":"https://en.wikipedia.org/wiki/Ari_Juels","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ari Juels is co-founder and co-director of IC3 (Initiative for CryptoCurrencies and Contracts) and Chief Scientist at Chainlink Labs, co-authoring the 2017 Chainlink white paper — a technical-founder role whose core is cryptography and oracle/distributed-systems work, not language modeling","source_url":"https://en.wikipedia.org/wiki/Ari_Juels","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His only ML-facing publication, 'Stealing Machine Learning Models via Prediction APIs' (2016), attacks logistic regression, neural networks and decision trees via black-box APIs — a security result about ML systems, not a contribution to embeddings, attention, pretraining or scaling that frontier mo","source_url":"https://arxiv.org/abs/1609.02943","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Cornell Tech faculty bio lists his research areas as blockchains, cryptocurrency, smart contracts, applied cryptography, user authentication and privacy — no language-modeling, embedding or LLM research","source_url":"https://www.tech.cornell.edu/people/ari-juels/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Stealing Machine Learning Models via Prediction APIs' (USENIX Security 2016) — an adversarial-ML security paper, not a frontier-model building block; no attention/embedding/pretraining/scaling authorship exists in his corpus","source_url":"https://arxiv.org/abs/1609.02943","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chief Scientist at Chainlink Labs and co-author of the 2017 Chainlink white paper; co-founder/co-director of IC3; his research areas are blockchains, smart contracts, applied cryptography and privacy — no language-modeling work","source_url":"https://en.wikipedia.org/wiki/Ari_Juels","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD UC Berkeley 1996 under Alistair Sinclair (randomized algorithms/Markov chains); faculty research areas listed as blockchain, AI security, applied cryptography and privacy — none in embeddings or language models","source_url":"https://www.tech.cornell.edu/people/ari-juels/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":64},{"id":68,"slug":"balaji-srinivasan","name":"Balaji Srinivasan","title":"Angel Investor / Author (former CTO, Coinbase)","company":"Network School","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","image_url":"/api/v1/ceo-ai-leaderboard/portrait/balaji-srinivasan.png","score":26,"tier":"informed_operator","dimensions":{"foundations":11,"vector_embeddings":4,"transformers_lm":3,"frontier_founder":1,"lm_domain_depth":2,"hands_on_engineering":8,"industry_impact":8,"scientific_founder":6},"rubric_version":3,"weighted_score":26,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Balaji Srinivasan holds a BS/MS/PhD in Electrical Engineering plus an MS in Chemical Engineering from Stanford, and taught Stanford courses in statistics, bioinformatics, and a popular 'Startup Engineering' course, giving him genuine graduate-level quantitative/statistical-learning training (foundations) — but this is bioinformatics/genomics-era statistical learning (pre-2013), not vector embeddings or the transformer lineage specifically. He co-founded the genomics company Counsyl (bioinformatics/statistics engineering) and later served as CTO of Coinbase and General Partner at a16z — these are technical-leadership and investing roles, not personal contributions to language modeling, embeddings, or transformers. There is no evidence of authored work in vector-space models, word embeddings, attention, or transformer architectures; his public output since ~2013 is largely commentary, books ('The Network State'), and media/podcast presence, which per rubric should not be rewarded. This is a case of strong general quantitative/statistics graduate training but a thin-to-absent record in the specific AI-lineage dimensions, correctly kept low on vector_embeddings/transformers_lm despite decent foundations.\n\nNothing of Srinivasan's own work is a building block of today's frontier language models: his verifiable research is computational-biology network alignment (Graemlin, Genome Research 2006; 'Automatic parameter learning for multiple local network alignment', J Comput Biol 2009) and genomics carrier screening (Counsyl, PMID 20729146, 2010) — protein/genetic 'networks,' not vector-space text models, embeddings, attention or transformers, so frontier_lineage is empty. He has NO verifiable record in language modeling (statistical/neural LMs, LSI, seq2seq, transformers, LLM pretraining/alignment) at any point from the 1990s to today, so lm_domain_depth is near-zero. He was, however, a genuine scientific/technical founder — co-founder and CTO of the genomics firm Counsyl (~2007) whose core carrier-screening method he first-authored, plus 21 Inc/Earn.com and later Coinbase CTO — roughly a decade as a technical founder/CTO, but entirely OUTSIDE the language-modeling field, which the anchor caps in the 3-7 band.","evidence":[{"claim":"BS/MS/PhD Electrical Engineering, MS Chemical Engineering, Stanford University; taught Stanford courses in statistics and bioinformatics","source_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'The Network State: How to Start a New Country'; founder of Network School retreat in Malaysia","source_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PubMed refined match under Stanford/Coinbase/a16z affiliation filter shows bioinformatics/pharmacogenomics papers (e.g., 'A universal carrier test for the long tail of Mendelian disease', 2010) consistent with Counsyl-era genomics work, not AI/ML","source_url":"https://pubmed.ncbi.nlm.nih.gov/20729146/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS, MS and PhD in electrical engineering plus MS in chemical engineering from Stanford; taught statistics and bioinformatics at Stanford; co-founded Counsyl (acquired by Myriad Genetics for $375M), 21 Inc/Earn.com and Teleport; Coinbase CTO 2018-2019; founded Network School September 2024","source_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 39363815 (Balaji S. Srinivasan): 21 papers, 1,881 citations, h-index 14, all in computational biology / network alignment / pharmacogenomics","source_url":"https://api.semanticscholar.org/graph/v1/author/39363815?fields=name,paperCount,citationCount,hIndex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Graemlin: general and robust alignment of multiple large interaction networks (Genome Research 2006) — Srinivasan BS co-author with Batzoglou","source_url":"https://pubmed.ncbi.nlm.nih.gov/16899655/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Automatic parameter learning for multiple local network alignment (J Comput Biol 2009) — Srinivasan BS with Flannick, Novak, Do and Batzoglou","source_url":"https://pubmed.ncbi.nlm.nih.gov/19645599/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q87684934 lists doctoral advisors Brad Osgood and Serafim Batzoglou and occupations angel investor, entrepreneur, university teacher","source_url":"https://www.wikidata.org/wiki/Q87684934","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"A universal carrier test for the long tail of Mendelian disease (2010) — Srinivasan BS first author, the Counsyl screening method","source_url":"https://pubmed.ncbi.nlm.nih.gov/20729146/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Srinivasan's verifiable research is biological network alignment, not language modeling: 'Graemlin: general and robust alignment of multiple large interaction networks' (Genome Research 2006, with Batzoglou)","source_url":"https://pubmed.ncbi.nlm.nih.gov/16899655/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Counsyl carrier-screening method he first-authored — 'A universal carrier test for the long tail of Mendelian disease' (2010) — the core science of the genomics company he co-founded","source_url":"https://pubmed.ncbi.nlm.nih.gov/20729146/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikipedia/Wikidata: co-founder of genomics company Counsyl, CTO of Coinbase, general partner at a16z, author of 'The Network State' — technical-founder and investing roles, none in language modeling","source_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Srinivasan's authored research is biological-network alignment and genomics (Graemlin, Genome Research 2006), not the transformer/embedding lineage — no frontier-model lineage","source_url":"https://pubmed.ncbi.nlm.nih.gov/16899655/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 39363815 shows 21 papers in computational biology/network alignment/pharmacogenomics, with no language-modeling or embeddings work","source_url":"https://api.semanticscholar.org/graph/v1/author/39363815","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded and served as CTO of the genomics company Counsyl (acquired by Myriad Genetics), a genuine technical-founder role but outside language modeling","source_url":"https://en.wikipedia.org/wiki/Balaji_Srinivasan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.75,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":65},{"id":76,"slug":"emin-gun-sirer","name":"Emin Gun Sirer","title":"Co-founder & CEO","company":"Ava Labs (Avalanche)","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","image_url":"/api/v1/ceo-ai-leaderboard/portrait/emin-gun-sirer.jpg","score":26,"tier":"informed_operator","dimensions":{"foundations":9,"vector_embeddings":3,"transformers_lm":1,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":14,"industry_impact":10,"scientific_founder":6},"rubric_version":3,"weighted_score":26,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Sirer earned a PhD in Computer Science and Engineering from the University of Washington (2002, advisor Brian Bershad) and was an associate professor at Cornell University before founding Ava Labs. His genuinely deep, personally authored research record (OpenAlex: 156 works, 11,707 citations, h-index 49, unambiguous topical match to distributed systems/P2P/blockchain) spans operating systems (SPIN, 1995, 955 citations), peer-to-peer systems (Meridian, 2005), and blockchain/consensus, including the influential 'Majority Is Not Enough: Bitcoin Mining Is Vulnerable' (2014, 1,409 citations) and 'On Scaling Decentralized Blockchains' (2016, 1,189 citations). This is strong, PhD-level, personally-built systems and distributed-computing research with real academic depth and a long track record (first verifiable year 1994, 32 years active), but it sits in distributed systems, consensus, and networking -- not in the mathematics of embeddings, representation learning, or the attention/transformer/LM lineage the rubric weights most heavily. No AI/ML-specific publications were found. He designed the Avalanche consensus protocol underlying Ava Labs' blockchain, a real, personally-led technical system, supporting solid hands-on-engineering and foundations scores but low scores on the two core-AI dimensions.\n\nNone of Sirer's personally-authored work touches the frontier-model lineage: his 156-work, ~11,700-citation record is in operating systems (SPIN), peer-to-peer systems (Meridian, KARMA), and blockchain consensus (selfish-mining, On Scaling Decentralized Blockchains, the Avalanche protocol) — no attention/transformer/embedding/optimizer/tokenizer/scaling/alignment contribution that GPT/Claude/Gemini/Llama descend from, so frontier_founder is near-zero. He has zero verifiable years in language modeling specifically (statistical/neural LMs, vector-space text models, seq2seq, transformers); his decades of work are in distributed systems and consensus, not the LM tip-of-the-spear, so lm_domain_depth is near-zero. He IS a genuine technical founder — he personally designed the Avalanche consensus protocol and co-founded/leads Ava Labs (2019), writing the core science the company runs on (~7 years) — but that field is blockchain, not the AI/language-model systems this dimension scores, placing scientific_founder in the 'technical founder outside this field' band.","evidence":[{"claim":"PhD Computer Science and Engineering, University of Washington (2002), advisor Brian Bershad; associate professor at Cornell University","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-authored 'Majority Is Not Enough: Bitcoin Mining Is Vulnerable' with Ittay Eyal (2014), an influential blockchain-security paper","source_url":"https://doi.org/10.1007/978-3-662-45472-5_28","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Designed the Avalanche consensus protocol and co-founded/CEO of Ava Labs","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD Computer Science and Engineering, University of Washington 2002, advisor Brian N. Bershad; Princeton undergraduate; associate professor at Cornell and former IC3 co-director; SPIN OS, HyperDex, KARMA (2003), selfish-mining paper, Avalanche consensus; founded Ava Labs 2019","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar (DaDmjMMAAAAJ), Cornell University: ~21,740 citations, h-index 58, i10 111; research focus operating systems, distributed systems, networking and blockchain - no machine learning, neural network, embedding or language-model papers among top works","source_url":"https://scholar.google.com/citations?user=DaDmjMMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Majority Is Not Enough: Bitcoin Mining Is Vulnerable' (Eyal & Sirer), Financial Cryptography 2014, ~1,409 citations","source_url":"https://doi.org/10.1007/978-3-662-45472-5_28","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sirer's top works are in blockchain/consensus and distributed systems (Majority Is Not Enough 2014, On Scaling Decentralized Blockchains 2016, SPIN OS 1995) — no language-model, embedding, or transformer work; nothing frontier LLMs build on","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sirer personally developed the Avalanche Consensus protocol underlying the Avalanche blockchain and is CEO and co-founder of Ava Labs — a genuine technical founder, but in blockchain not language modeling","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile (research focus operating systems, distributed systems, networking, blockchain) shows no machine-learning, neural-network, embedding or language-model papers","source_url":"https://scholar.google.com/citations?user=DaDmjMMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sirer developed the Avalanche Consensus protocol and is CEO and co-founder of Ava Labs; his known research is in P2P systems, operating systems and networking — not AI/ML or language modeling","source_url":"https://en.wikipedia.org/wiki/Emin_G%C3%BCn_Sirer","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google Scholar profile shows research in operating systems, distributed systems, networking and blockchain with no machine-learning, neural-network, embedding or language-model publications","source_url":"https://scholar.google.com/citations?user=DaDmjMMAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Top-cited works ('Majority Is Not Enough' 2014, 'On Scaling Decentralized Blockchains' 2016, SPIN OS 1995) confirm a consensus/systems record, not an LM lineage frontier models cite","source_url":"https://doi.org/10.1007/978-3-662-45472-5_28","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.82,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":66},{"id":71,"slug":"tarun-chitra","name":"Tarun Chitra","title":"Founder & CEO","company":"Gauntlet","sector":"crypto","profile_url":null,"image_url":null,"score":26,"tier":"informed_operator","dimensions":{"foundations":12,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":10,"industry_impact":8,"scientific_founder":6},"rubric_version":3,"weighted_score":26,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Chitra has a genuine, verifiable quantitative-research record: B.A. Mathematics and B.S. Applied Engineering Physics from Cornell, prior quant/scientific-programming roles at D.E. Shaw Research and Vatic Labs (HFT), and a Google Scholar profile showing h-index 23 (1,882 citations) built almost entirely on DeFi mechanism-design and convex-optimization papers (Constant Function Market Makers / price oracles, Uniswap market analysis, MEV, intent-based markets). This is real applied mathematics and statistical/optimization work — strong foundations-dimension evidence — but it is financial/mechanism-design engineering, not the vector-embeddings or transformer/language-model lineage the rubric scores; no papers on embeddings, attention, or language modeling were found. Gauntlet applies simulation and optimization to DeFi risk parameters, not AI model-building. His technical depth is real but off-target for the core-AI dimensions.\n\nChitra's verifiable research record is entirely in DeFi mechanism design and convex optimization — constant function market makers, price oracles, optimal routing (e.g. 'Improved Price Oracles', 2020; 'Optimal Routing for Constant Function Market Makers', 2022) — none of which is cited by or built into any frontier language model's architecture, tokenizer, optimizer, pretraining objective, scaling result or alignment method, so frontier_founder is effectively nil. He has zero verifiable years in the language-modeling lineage (vector-space/LSI/n-gram/neural LMs → seq2seq → transformers → LLM pretraining/alignment); his prior quant roles (D.E. Shaw Research, Vatic Labs) and Gauntlet work are financial simulation and optimization, not LM. He genuinely IS a scientific/technical founder — founder-CEO of Gauntlet (founded ~2018, ~8 years) who personally authors the company's core research and risk-simulation methods — but that company's core is DeFi risk modeling, not language-model systems, so under the anchors he lands in the 'technical founder outside this field' band.","evidence":[{"claim":"Google Scholar profile: h-index 23, 1,882 citations, top papers are AMM/DeFi mechanism-design and convex optimization (no ML/embeddings/LM papers)","source_url":"https://scholar.google.com/citations?user=_48EkToAAAAJ&hl=en","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"B.A. Mathematics and B.S. Applied Engineering Physics, Cornell University; prior roles at D.E. Shaw Research and Vatic Labs","source_url":"https://www.clay.com/dossier/gauntlet-ceo","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 6285600, affiliation Gauntlet: 44 papers, 1,241 citations, h-index 17; works include 'Improved Price Oracles: Constant Function Market Makers' (2020, 263 citations), 'An analysis of Uniswap markets' (2019, 250), 'Optimal Routing for Constant Function Market Makers' (2022), 'T","source_url":"https://api.semanticscholar.org/graph/v1/author/6285600?fields=name,affiliations,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author with Guillermo Angeris, Hsien-Tang Kao, Rei Chiang and Charlie Noyes of 'An analysis of Uniswap markets' (2019), which formally analyses constant product markets and validates stability via agent-based simulation","source_url":"https://arxiv.org/abs/1911.03380","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chief Executive Officer of Gauntlet; previously held positions in quantitative R&D at Vatic HFT and D.E. Shaw","source_url":"https://www.gauntlet.xyz/our-team","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5082358070: 49 works, 406 citations, h-index 11, topics blockchain technology applications and security, economic theories and models, financial markets, auction theory — no machine-learning or NLP topics","source_url":"https://api.openalex.org/authors/A5082358070","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chitra's top works are DeFi/AMM mechanism-design papers ('Improved Price Oracles: Constant Function Market Makers', 2020; 'Optimal Routing for Constant Function Market Makers', 2022) with no embeddings/attention/language-model content — no lineage into frontier LMs","source_url":"https://api.semanticscholar.org/graph/v1/author/6285600?fields=name,affiliations,paperCount,citationCount,hIndex,papers.title","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex topics for the author are blockchain, economic theories, financial markets and auction theory — no NLP/ML/language-modeling topics, confirming zero language-modeling domain record","source_url":"https://api.openalex.org/authors/A5082358070","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder and CEO of Gauntlet, a DeFi risk-simulation firm; he sets and executes the technical/research direction personally (author of the firm's CFMM and risk papers) — a genuine technical founder, but in DeFi rather than AI/LM","source_url":"https://www.gauntlet.xyz/our-team","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chitra founded Gauntlet (founder-CEO); his authored research (CFMMs, price oracles, optimal routing) is the technical core of the firm's risk-simulation platform — a genuine technical founder but in DeFi mechanism design, not language modeling","source_url":"https://www.gauntlet.xyz/our-team","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex/Semantic Scholar record shows topics limited to blockchain, economic/auction theory and financial markets — no NLP, embeddings, attention or language-model work that frontier models build on","source_url":"https://api.semanticscholar.org/graph/v1/author/6285600?fields=name,affiliations,paperCount,citationCount,hIndex,papers.title","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.75,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":67},{"id":39,"slug":"amjad-masad","name":"Amjad Masad","title":"Founder & CEO","company":"Replit","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Amjad_Masad","image_url":"/api/v1/ceo-ai-leaderboard/portrait/amjad-masad.jpg","score":25,"tier":"informed_operator","dimensions":{"foundations":4,"vector_embeddings":3,"transformers_lm":4,"frontier_founder":3,"lm_domain_depth":4,"hands_on_engineering":11,"industry_impact":8,"scientific_founder":6},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Masad is a genuine career software engineer with no verifiable personal record in the mathematics, embeddings or transformer/LM lineage. He holds a computer science degree from Princess Sumaya University for Technology, was a founding engineer at Codecademy (2011-2013), then ran the JavaScript infrastructure team at Facebook (2013-2016), before founding Replit in 2016 — real, substantial systems and developer-tooling engineering. Neither OpenAlex nor Semantic Scholar returns any author record for him, which is a correct absence rather than a collection failure, and the PubMed hits are Saudi and Palestinian clinician homonyms with no connection to him. The strongest AI item on his record is replit-code-v1-3b, a 2.7B-parameter causal code language model trained on 525B tokens across 256 A100-40GB GPUs; I retrieved the model card directly and it attributes the work to 'Replit, Inc.' as an organization with no individual named, so this counts as leading an organization that trained a model rather than personally authoring it. Replit's current product is an LLM-driven coding agent, which is genuine industry impact whose core is these systems, though the models underneath are largely third-party. He is best read as a strong systems and infrastructure engineer who manages builders of LM products: the research dimensions belong in the 3-7 'uses the tools, manages builders' band, and his engineering in the 8-12 band.\n\nNo frontier lineage traces to Masad personally: the only model on the record, replit-code-v1-3b, is a 2.7B code LM whose card credits 'Replit, Inc.' (built on others' components — Flash Attention, ALiBi, LionW) and it is a downstream code model, not a method, dataset or architecture that GPT/Claude/Gemini/Llama-class systems descend from; Replit's product applies and fine-tunes third-party frontier models, placing him in the 3-7 'applies/fine-tunes' band. His language-modeling record is short and organizational rather than personal — Replit's code-completion/model work dates only to roughly 2022-2023 (~2-3 years), preceded by a decade of general dev-tooling and JavaScript-infrastructure engineering with no LM lineage. He is a genuine technical founder-CEO of Replit since 2016 (~10 years, personally building the browser IDE and setting technical direction), but the LM science is executed by others and the company's core is a developer IDE rather than language modeling, so scientific_founder sits in the 3-7 'technical founder outside this field / AI company whose science others do' band rather than higher.","evidence":[{"claim":"Computer science degree from Princess Sumaya University for Technology; founding engineer at Codecademy (2011-2013); software engineer overseeing the JavaScript infrastructure team at Facebook (2013-2016); founded Replit in 2016","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The replit-code-v1-3b model card attributes the model to 'Replit, Inc.' as an organization with no individual developer named; 2.7B parameters, 525B training tokens over Stack Dedup v1.2, trained on 256 x A100-40GB GPUs using Flash Attention, ALiBi positional embeddings and the LionW optimizer","source_url":"https://huggingface.co/replit/replit-code-v1-3b","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him as CEO of Replit, educated at Princess Sumaya University for Technology, with employers Replit, Codecademy and Meta Platforms — no research affiliation or academic post","source_url":"https://www.wikidata.org/wiki/Q113856785","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Amjad Masad holds a computer science degree from Princess Sumaya University for Technology in Jordan.","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Masad worked as a founding engineer at Codecademy (Nov 2011-Oct 2013), then as a software engineer at Facebook (Oct 2013-Apr 2016) where he led the JavaScript infrastructure team.","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Masad founded Replit, an online integrated development environment, in 2016 with his wife Haya Odeh and brother Faris Masad.","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Computer science degree, Princess Sumaya University for Technology; founding engineer at Codecademy 2011-2013; Facebook JavaScript infrastructure 2013-2016; founded Replit 2016","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"replit-code-v1-3b: 2.7B-parameter causal LM for code, 525B tokens, Stack Dedup v1.2, trained on 256 x A100-40GB; developer listed as Replit, Inc. with no individual author credited","source_url":"https://huggingface.co/replit/replit-code-v1-3b","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata: CEO of Replit; educated at Princess Sumaya University for Technology; employers Replit, Codecademy, Meta Platforms","source_url":"https://www.wikidata.org/wiki/Q113856785","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"replit-code-v1-3b is a 2.7B causal code LM credited to 'Replit, Inc.' with no individual author, built with Flash Attention, ALiBi positional embeddings and the LionW optimizer — a downstream code model, not a component frontier models build on","source_url":"https://huggingface.co/replit/replit-code-v1-3b","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Masad founded Replit in 2016 and is its CEO; his prior record is founding engineer at Codecademy and JavaScript infrastructure at Facebook — software/dev-tooling engineering, no language-modeling research affiliation","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata lists him as CEO of Replit, educated at Princess Sumaya University for Technology, employers Replit/Codecademy/Meta — no academic or research post","source_url":"https://www.wikidata.org/wiki/Q113856785","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"replit-code-v1-3b model card lists the developer as 'Replit, Inc.' (no individual named) and describes a 2.7B code LM using Flash Attention, ALiBi positional embeddings and the LionW optimizer — downstream techniques, not frontier building blocks it originated","source_url":"https://huggingface.co/replit/replit-code-v1-3b","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Masad founded Replit in 2016 and was previously a founding engineer at Codecademy and led Facebook's JavaScript infrastructure team — a genuine hands-on technical founder in developer tooling, not a business founder with technical co-founders","source_url":"https://en.wikipedia.org/wiki/Amjad_Masad","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him only as CEO of Replit with employers Replit, Codecademy and Meta and no academic/research affiliation; OpenAlex and Semantic Scholar return no author record, so there is no personal language-modeling publication history","source_url":"https://www.wikidata.org/wiki/Q113856785","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.86,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":68},{"id":111,"slug":"andre-zayarni","name":"André Zayarni","title":"Co-founder & CEO","company":"Qdrant","sector":"general","profile_url":null,"image_url":null,"score":25,"tier":"informed_operator","dimensions":{"foundations":4,"vector_embeddings":7,"transformers_lm":3,"frontier_founder":2,"lm_domain_depth":4,"hands_on_engineering":8,"industry_impact":10,"scientific_founder":4},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The merge flagged an invariant violation on pass 2's vector_embeddings of 13 — no publications, patents or verified work, yet a score above 7 — and that flag is upheld. The dimension may exceed 7 only on cited verified engineering work, so I checked GitHub's commit API directly rather than inferring from profile badges as pass 2 did. Across the entire qdrant organisation his account azayarni has 46 commits, and in the core qdrant/qdrant engine repository just 6: 'Update logo.svg', a roadmap badge adjustment, two cloud-link edits, an ETA update and README spellcheck fixes. Those are documentation and branding changes, not vector-index, quantization or retrieval code. The rest of his organisation commits are merge commits on the landing page and docs repositories. His GitHub account has 7 public repositories and 80 followers. Pass 2's inference that he is 'a working engineer with a real GitHub presence' rather than a commercial founder does not survive inspection of what the commits actually contain, and its own evidence concedes that the engine-internals authorship at Qdrant belongs to co-founder and CTO Andrey Vasnetsov. Pass 1 reached the same conclusion about the division of technical labour and scored accordingly. There is no OpenAlex, Semantic Scholar, Wikipedia or Wikidata record for him, no paper on approximate nearest-neighbour search, quantization, contrastive learning or dense retrieval, and no patent. What he verifiably did is co-found and lead, as CEO, a company whose entire product is an open-source vector similarity search engine written in Rust with ~34.5k GitHub stars — genuinely core infrastructure for embedding-based retrieval, which supports real industry impact and a vector_embeddings score at the top of the permitted band, but as an org-builder rather than an author of the systems.\n\nFrontier models (GPT/Claude/Gemini/Llama) do not descend from Qdrant — it is a downstream RAG/retrieval database used alongside LLMs, not an architecture, objective, optimizer, tokenizer, dataset or scaling result those models are built on, and Zayarni personally authored none of the vector-search engine (the prior passes confirm engine authorship belongs to co-founder/CTO Andrey Vasnetsov; his own commits are logo/link/README edits), so there is no verifiable frontier lineage under his name (score 2). His language-modeling exposure is adjacent embedding-retrieval infrastructure since Qdrant's 2021 founding (~4 years), as CEO rather than as an author of LM/vector-space research — no papers, patents or engine code — placing him in the thin/adjacent band. He is a genuine co-founder and CEO of a real embeddings-infrastructure company since 2021 (~4 years), but the science and engineering were done by his technical co-founder, which is the textbook 'founder/CEO of an AI company whose science was done by others' case rather than a scientific/technical founder.","evidence":[{"claim":"GitHub's commit search attributes only 6 commits in qdrant/qdrant to author azayarni, and their messages are 'Update logo.svg', 'Roadmap Badge Title', 'adjusted the cloud link', 'Added cloud form link', 'Updated ETA' and 'README spellcheck fixes' — documentation and branding, not engine code","source_url":"https://api.github.com/search/commits?q=author:azayarni+repo:qdrant/qdrant","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Across the whole qdrant organisation his account has 46 commits, predominantly merge commits in qdrant/landing_page and qdrant/docs plus initial commits in .github and qdrant-dotnet","source_url":"https://api.github.com/search/commits?q=author:azayarni+org:qdrant","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub user azayarni is Andre Zayarni, company @qdrant, bio 'Co-founder at Qdrant', Berlin, with 7 public repositories and 80 followers","source_url":"https://api.github.com/users/azayarni","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Qdrant was founded by André Zayarni and Andrey Vasnetsov in 2021; Zayarni is CEO and co-founder; Qdrant is an open-source vector similarity search engine built in Rust","source_url":"https://qdrant.tech/about-us/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The qdrant/qdrant repository is a vector similarity search engine and vector database written in Rust with ~34.5k stars","source_url":"https://github.com/qdrant/qdrant","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"M.Sc. in Medieninformatik (Media Informatics), Karlsruhe University of Applied Sciences, 2007; career as Senior Software Developer/Engineer and technical product roles (VZnet Netzwerke, Bigpoint, Spreadsave, Choisr, MoBerries) before co-founding Qdrant","source_url":"https://theorg.com/org/qdrant/org-chart/andre-zayarni","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Qdrant in Berlin in 2021 as Co-founder and CEO; Qdrant is an open-source vector similarity search engine/database","source_url":"https://qdrant.tech/about-us/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CTO Andrey Vasnetsov (ML engineer, prior roles at Tinkoff Bank and MoBerries) proposed the pivot to neural search and built Qdrant's initial engine in Rust — the core vector-search technology's primary technical architect","source_url":"https://theorg.com/org/qdrant/org-chart/andrey-vasnetsov","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Qdrant was founded by André Zayarni and Andrey Vasnetsov, who collaborated in 2021 on a project leveraging vector similarity search to build a matching engine for unstructured data; Zayarni is CEO and co-founder; Qdrant is a vector search engine built in Rust, offered open-source and as a managed cl","source_url":"https://qdrant.tech/about-us/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub profile azayarni identifies André Zayarni as co-founder at Qdrant, Berlin, with work primarily in Rust and TypeScript and Pull Shark and Starstruck achievements","source_url":"https://github.com/azayarni","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CTO Andrey Vasnetsov proposed the pivot to neural search and built Qdrant's initial engine in Rust — the primary technical architect of the core vector-search technology","source_url":"https://theorg.com/org/qdrant/org-chart/andrey-vasnetsov","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub commit search attributes only 6 commits in qdrant/qdrant to azayarni (logo, badge title, cloud links, ETA, README spellcheck) — no vector-index or retrieval code","source_url":"https://api.github.com/search/commits?q=author:azayarni+repo:qdrant/qdrant","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Qdrant is an open-source vector similarity search engine/database and Zayarni is co-founder and CEO, with CTO Andrey Vasnetsov as the engine's primary technical architect","source_url":"https://qdrant.tech/about-us/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Zayarni's 6 commits to the core qdrant/qdrant engine are documentation/branding (logo, badge title, link edits, ETA, README spellcheck), not vector-index or retrieval code","source_url":"https://api.github.com/search/commits?q=author:azayarni+repo:qdrant/qdrant","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub user azayarni identifies André Zayarni as 'Co-founder at Qdrant', Berlin — a founder/CEO role, not an author of the engine science","source_url":"https://api.github.com/users/azayarni","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.78,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":69},{"id":84,"slug":"eli-ben-sasson","name":"Eli Ben-Sasson","title":"Co-founder, President & CEO","company":"StarkWare Industries","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Eli_Ben-Sasson","image_url":"/api/v1/ceo-ai-leaderboard/portrait/eli-ben-sasson.jpg","score":25,"tier":"informed_operator","dimensions":{"foundations":14,"vector_embeddings":1,"transformers_lm":1,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":11,"industry_impact":7,"scientific_founder":7},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The two passes agree entirely on the facts and disagree on how to apply the rubric, so this is adjudicated on rubric interpretation rather than on new evidence. Ben-Sasson is a distinguished theoretical computer scientist: PhD Hebrew University 2001 under Avi Wigderson, postdocs at Harvard and MIT, Technion faculty 2005-2020 rising to Full Professor, author of 'Short proofs are narrow — resolution made simple' (JACM 2001), 'SNARKs for C' (2013), Zerocash (2014) and, as first author, the original STARK paper 'Scalable, transparent, and post-quantum secure computational integrity' (ePrint 2018/046) that introduced FRI. He personally invented and built the proof systems that StarkEx and Starknet run on. The question is what that earns under a rubric that measures depth in the core of AI specifically. Pass 1 scored foundations 19 and industry_impact 17 on the strength of the mathematics and the company; pass 2 scored 12 and 6, reasoning that the rubric names the mathematics of statistical learning and that StarkWare's products are blockchain scaling, not AI. Pass 2 has the better reading, but goes too far in one direction as pass 1 does in the other. The rubric's foundations dimension names linear algebra, matrix methods, optimization and statistical learning; his work is proof complexity, error-correcting codes, algebraic complexity and Reed-Solomon proximity testing — first-principles mathematics of genuine depth in the adjacent-but-different tradition, which warrants a strong score but not the 18-20 'authored canonical work the field builds on' band, because the field in question here is AI and his canonical work is not in it. Industry impact likewise cannot sit at 17: that anchor requires orgs or products whose CORE is these systems, and StarkWare's core is validity rollups. Both lineage dimensions are at the floor: neither pass found, and I could not find, any publication, system or patent by him touching embeddings, attention, pretraining or neural networks.\n\nBen-Sasson's entire research corpus is proof complexity, error-correcting codes and zero-knowledge cryptography (STARKs/FRI, SNARKs, Zerocash); none of it — architecture, attention, embeddings, optimizers, tokenizers, pretraining or alignment — is a building block that GPT/Claude/Gemini/Llama-class models descend from, so frontier_founder sits at the floor. He has zero verifiable years in language modeling specifically (statistical/neural LMs, vector-space text models, seq2seq, transformers), placing lm_domain_depth at the floor. He IS a genuine scientific/technical founder — co-founder of StarkWare (2018), Chief Scientist and later CEO, who personally authored the core STARK/FRI research the company's validity rollups run on, ~8 years — but StarkWare's core is blockchain scaling, not AI/LM systems, so the high scientific_founder bands (which require companies whose core is 'these systems') are unavailable and he lands at the top of the 'technical founder outside this field' band.","evidence":[{"claim":"PhD theoretical computer science, Hebrew University 2001 under Avi Wigderson; postdocs at Harvard and MIT; Technion faculty 2005-2020 (Full Professor 2015); invented STARKs and FRI in 2018; co-founded StarkWare 2018, CEO from February 2024; no machine-learning or AI work documented in his biography","source_url":"https://en.wikipedia.org/wiki/Eli_Ben-Sasson","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Scalable, transparent, and post-quantum secure computational integrity' (IACR ePrint 2018/046) with Bentov, Horesh and Riabzev — the founding STARK/FRI paper","source_url":"https://eprint.iacr.org/2018/046","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records the doctorate from Hebrew University with advisor Avi Wigderson and employers Technion and StarkWare","source_url":"https://www.wikidata.org/wiki/Q102301988","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PhD 2001, Hebrew University of Jerusalem, advisor Avi Wigderson; Technion faculty 2005 (Senior Lecturer) -> 2010 (Associate Professor) -> 2015 (Full Professor), departed 2020; co-founded StarkWare Industries in 2018 with Uri Kolodny, Michael Riabzev, and Alessandro Chiesa; became CEO/president Feb 2","source_url":"https://en.wikipedia.org/wiki/Eli_Ben-Sasson","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of the original zk-STARK paper 'Scalable, transparent, and post-quantum secure computational integrity' (IACR ePrint 2018/046), with Iddo Bentov, Yinon Horesh, and Michael Riabzev -- the founding STARK/FRI protocol paper","source_url":"https://eprint.iacr.org/2018/046","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as Co-Founder and CEO of StarkWare on the company's official team/about page","source_url":"https://starkware.co/about-us/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records doctorate from Hebrew University with advisor Avi Wigderson, employers Technion and StarkWare, and Google Scholar ID M93Auk4AAAAJ","source_url":"https://www.wikidata.org/wiki/Q102301988","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded StarkWare Industries in 2018 and serves as CEO; personally invented the STARK proof system and FRI protocol the company's products (StarkEx/Starknet) run on — a validity-rollup / blockchain-scaling company, not an AI or language-modeling company","source_url":"https://en.wikipedia.org/wiki/Eli_Ben-Sasson","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of the founding STARK/FRI paper 'Scalable, transparent, and post-quantum secure computational integrity' (IACR ePrint 2018/046) — cryptography, not any transformer/embedding/LM lineage cited by frontier model reports","source_url":"https://eprint.iacr.org/2018/046","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of the founding zk-STARK/FRI paper 'Scalable, transparent, and post-quantum secure computational integrity' (ePrint 2018/046) — cryptographic proof systems, not language-model lineage","source_url":"https://eprint.iacr.org/2018/046","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of StarkWare Industries, whose core products (StarkEx, Starknet) are validity rollups for blockchain scaling; he invented the STARK proof system the company runs on","source_url":"https://en.wikipedia.org/wiki/Eli_Ben-Sasson","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records employers Technion and StarkWare and advisor Avi Wigderson; no ML/LM affiliation or work","source_url":"https://www.wikidata.org/wiki/Q102301988","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":70},{"id":82,"slug":"juan-benet","name":"Juan Benet","title":"Founder & CEO","company":"Protocol Labs (IPFS, Filecoin)","sector":"crypto","profile_url":null,"image_url":null,"score":25,"tier":"informed_operator","dimensions":{"foundations":7,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":1,"hands_on_engineering":14,"industry_impact":10,"scientific_founder":6},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Juan Benet earned a BS and MS in Computer Science from Stanford University (2010, 2012), then founded Protocol Labs in 2014 and personally authored the IPFS whitepaper ('IPFS - Content Addressed, Versioned, P2P File System', arXiv:1407.3561), combining distributed-hash-table, content-addressing, and Merkle-DAG ideas — genuine, hands-on distributed-systems engineering and a real authored technical paper, plus he went on to build Filecoin. This demonstrates strong systems-engineering foundations (algorithms, cryptographic hashing, distributed protocols) and personal hands-on building of production infrastructure used at scale. However, none of this work is in the core-AI lineage the rubric measures — no linear algebra/optimization/statistical-learning research, no vector-embeddings or representation-learning work, and no transformer/language-model research or engineering. IPFS/Filecoin are content-addressed storage/distributed-systems protocols, not AI systems; any current 'AI' framing at Protocol Labs is business positioning rather than Benet's personal authored research. Scored with real hands-on-engineering credit for the IPFS whitepaper and system-building but very low on the AI-specific dimensions per rubric instructions to not reward adjacent-but-not-core technical work as if it were core AI depth.\n\nBenet's authored work — the IPFS whitepaper (arXiv:1407.3561) and Filecoin protocol design — is content-addressed distributed storage, not any component (architecture, attention, embeddings, optimizers, tokenizers, pretraining objectives, scaling or alignment methods) that today's frontier language models descend from; there is no citable lineage into GPT/Claude/Gemini/Llama-class systems, so frontier_founder is near-absent. His verifiable record shows zero years in language modeling specifically — no vector-space, LSI, n-gram, neural-LM, seq2seq or transformer work — so lm_domain_depth is essentially nil (the dossier's OpenAlex/Semantic Scholar bibliometrics belong to an unrelated Spanish novelist, 1927-1993, and are disregarded). He does genuinely operate as a scientific/technical founder — sole author of the IPFS paper, lead developer of Kubo/js-ipfs/go-libp2p (GitHub @jbenet), setting Protocol Labs' technical direction from May 2014 (~12 years) — but that is a technical founder OUTSIDE the AI/language-modeling field, which the anchor places in the 3-7 band.","evidence":[{"claim":"Founded Protocol Labs in May 2014; author of the IPFS whitepaper 'IPFS - Content Addressed, Versioned, P2P File System' (arXiv:1407.3561, 2014)","source_url":"https://arxiv.org/abs/1407.3561","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'IPFS - Content Addressed, Versioned, P2P File System', arXiv:1407.3561, sole author Juan Benet, submitted 14 July 2014; describes content-addressed block storage with a Merkle DAG, a distributed hashtable and a self-certifying namespace","source_url":"https://arxiv.org/abs/1407.3561","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub @jbenet, Protocol Labs: creator and lead developer of IPFS (23.1k stars), Kubo the Go IPFS implementation (17.1k stars), js-ipfs (7.4k stars) and go-libp2p (6.9k stars); 286 repositories","source_url":"https://github.com/jbenet","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Protocol Labs was founded in May 2014 by Juan Benet, participated in Y Combinator S14; it built IPFS, Filecoin (mainnet October 2020), libp2p, Multiformats and IPLD; its network also includes AI efforts such as BitRobot Network (2025) and Prime Intellect's INTELLECT-2 decentralized 32B RL training r","source_url":"https://pl.xyz/about/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"IPFS was created by Juan Benet, who later founded Protocol Labs in May 2014; alpha released February 2015","source_url":"https://en.wikipedia.org/wiki/InterPlanetary_File_System","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Protocol Labs and Juan Benet are listed as the original authors of Filecoin","source_url":"https://en.wikipedia.org/wiki/Filecoin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sole author of 'IPFS - Content Addressed, Versioned, P2P File System' (arXiv:1407.3561, 2014) — a content-addressed storage protocol (Merkle DAG, DHT, self-certifying namespace), not language-model or representation-learning work","source_url":"https://arxiv.org/abs/1407.3561","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Creator and lead developer of IPFS/Kubo/js-ipfs/go-libp2p under GitHub @jbenet — distributed-systems engineering, no language-modeling repositories","source_url":"https://github.com/jbenet","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Protocol Labs in May 2014 and is listed as original author of IPFS and Filecoin, personally authoring the core protocol designs the company runs on (technical founder, outside the AI/LM field)","source_url":"https://en.wikipedia.org/wiki/Filecoin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Juan Benet founded Protocol Labs in May 2014 and is sole author of the IPFS whitepaper 'IPFS - Content Addressed, Versioned, P2P File System' (arXiv:1407.3561), a content-addressed storage protocol with a Merkle DAG and distributed hashtable — not AI/language-model research","source_url":"https://arxiv.org/abs/1407.3561","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Protocol Labs / Juan Benet are the original authors of Filecoin, a decentralized storage network (mainnet October 2020) — distributed-systems infrastructure, not frontier-model training or inference stack","source_url":"https://en.wikipedia.org/wiki/Filecoin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.74,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":71},{"id":101,"slug":"nat-friedman","name":"Nat Friedman","title":"Head of Product, Meta Superintelligence Labs; former CEO of GitHub","company":"Meta (Meta Superintelligence Labs)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Nat_Friedman","image_url":"https://thumb.wikimedia.org/wikipedia/commons/thumb/0/0e/20060424_Nat_Friedman.jpg/330px-20060424_Nat_Friedman.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail","score":25,"tier":"informed_operator","dimensions":{"foundations":6,"vector_embeddings":2,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":11,"industry_impact":11,"scientific_founder":6},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Friedman is a career software engineer and executive with a substantial building record and no personal research record in the AI lineage. He earned a BS from MIT in 1999 in computer science and mathematics — real quantitative training, but at undergraduate level and with no thesis, graduate coursework or publication in learning theory or optimization. I retrieved his Semantic Scholar author record directly and it confirms the absence: the only genuine entries are GNOME-era software writing ('The Bonobo component and document model', 1999; 'Bringing Usability to Open Source', 2006), while 'Carmina Gallo: Intertextual Metapoetics in Virgil's Eclogues' (2013) belongs to a different Nat Friedman. There is no paper, preprint or patent by him anywhere in embeddings, attention, pretraining or scaling. His engineering history is genuine and hands-on — co-founding Ximian with Miguel de Icaza and working on GNOME infrastructure, then co-founding Xamarin around Mono, acquired by Microsoft in 2016 — but that is developer tooling and systems software, not machine learning. As CEO of GitHub from 2018 to 2021 he shipped GitHub Copilot, the first mass-deployed LLM coding product, alongside Codespaces; that is the strongest entry on his record and the basis of the industry-impact score, but every available source documents it as product leadership, with no personal contribution to Codex's training, architecture or evaluation verifiable. His subsequent AI involvement — AI Grant and NFDG investing with Daniel Gross, advising Midjourney, nat.dev, and instigating and funding the Vesuvius Challenge, where his listed role is 'Instigator, Director & Founding Sponsor' while the ink-detection and virtual-unwrapping ML is done by competing teams — is capital allocation and convening, which the rubric excludes from research credit.\n\nFriedman contributes nothing that today's frontier models descend from: no architecture, attention, embedding, optimizer, tokenizer, pretraining, scaling or alignment work appears anywhere in his verified record — GitHub Copilot shipped under his CEO tenure is a product built ON OpenAI Codex, not a component the frontier stack cites, so frontier_founder is near-zero. His language-modeling depth is executive/product-adjacent with zero personal LM research over any period (his only genuine publications are GNOME-era systems-software writing from 1999–2006), so lm_domain_depth sits in the 'adjacent, no LM record' band. He is a genuine, hands-on technical co-founder — Ximian/Mono (1999–2003) and Xamarin (2011–2016, acquired by Microsoft), personally authoring core systems code — but that ~9-year technical-founder record is in developer tooling and systems software, entirely OUTSIDE language modeling, which caps scientific_founder in the 3–7 'technical founder outside this field' band.","evidence":[{"claim":"BS from MIT (1999) in computer science and mathematics; co-founded Ximian with Miguel de Icaza 1999-2003; CTO of Open Source at Novell 2003-2010; co-founded and led Xamarin 2011-2016 (acquired by Microsoft); CEO of GitHub 2018-2021, during which Copilot and Codespaces shipped; head of product at Met","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 41234023 contains only four items: 'The Bonobo component and document model' (1999), 'Eof: bringing usability to open source' (2006), 'Bringing Usability to Open Source' (2006) and 'Carmina Gallo: Intertextual Metapoetics in Virgil's Eclogues' (2013) — GNOME-era software writ","source_url":"https://api.semanticscholar.org/graph/v1/author/41234023/papers?fields=title,year,venue,authors&limit=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as 'Instigator, Director & Founding Sponsor' of the Vesuvius Challenge; the machine-learning work (ink detection, virtual unwrapping, segmentation) is performed by the competing technical teams","source_url":"https://scrollprize.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education at MIT, occupations programmer/engineer/computer scientist, employers GitHub and Xamarin, and GitHub username 'nat' — no academic degree beyond the MIT BS and no research affiliation","source_url":"https://www.wikidata.org/wiki/Q92955","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS Computer Science and Mathematics, MIT, 1999; co-founded Ximian in 1999, met Miguel de Icaza via LinuxNet IRC network","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CTO of Open Source at Novell 2003-2010; co-founded and was CEO of Xamarin 2011-2016 (acquired by Microsoft in 2016)","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"CEO of GitHub 2018-2021, during which GitHub Copilot, Codespaces, and the native mobile app shipped; currently head of product at Meta Superintelligence Labs (2025) and advisor to Midjourney, board member at Arc Institute","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar 'Nat Friedman' match shows only 5 papers / 1 citation / h-index 1 with 3 candidates (unresolved homonym risk); PubMed sample for 'Friedman N' returns entirely unrelated authors (cannabis-use research, pediatric genetics, infection control) confirming those are different people, not","source_url":"https://www.semanticscholar.org/author/41234023","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as 'Instigator, Director & Founding Sponsor' of the Vesuvius Challenge with a $2,250,000 donation; the ML/CV work (segmentation, virtual unwrapping, ink detection via iterative pseudo-labeling) is performed by the technical research teams, with Friedman's contributions directorial and financi","source_url":"https://scrollprize.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 41234023 attributes only GNOME-era software papers to him ('The Bonobo component and document model', 1999; 'Bringing Usability to Open Source', 2006), with 5 papers and 1 total citation","source_url":"https://api.semanticscholar.org/graph/v1/author/41234023/papers?fields=title,year,venue,authors","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Career is systems-software and developer-tooling: co-founded Ximian (GNOME/Mono) 1999-2003, co-founded and led Xamarin 2011-2016 (acquired by Microsoft), CEO of GitHub 2018-2021, now head of product at Meta Superintelligence Labs — product/engineering leadership, no personal AI research","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education at MIT and employers GitHub and Xamarin with occupations programmer/engineer/computer scientist — no research affiliation and no work in the language-modeling lineage","source_url":"https://www.wikidata.org/wiki/Q92955","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 41234023 for Nat Friedman contains only GNOME-era software items and 1 total citation — no embeddings, attention, pretraining or scaling paper","source_url":"https://api.semanticscholar.org/graph/v1/author/41234023/papers?fields=title,year,venue,authors","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub Copilot, the first mass-deployed LLM coding product, was built on OpenAI's Codex and shipped as a product during Friedman's GitHub CEO tenure (2018-2021); no personal contribution to its training or architecture is verifiable","source_url":"https://en.wikipedia.org/wiki/Nat_Friedman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Friedman as founder/technical builder at Xamarin (Mono) and GitHub, MIT-educated programmer/engineer — developer-tooling systems work, not machine-learning research","source_url":"https://www.wikidata.org/wiki/Q92955","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.87,"source":"community","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":72},{"id":59,"slug":"vincent-weisser","name":"Vincent Weisser","title":"Co-founder & CEO","company":"Prime Intellect","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/vincent-weisser.jpg","score":25,"tier":"informed_operator","dimensions":{"foundations":4,"vector_embeddings":3,"transformers_lm":7,"frontier_founder":4,"lm_domain_depth":3,"hands_on_engineering":8,"industry_impact":8,"scientific_founder":5},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Vincent Weisser has no traditional CS/math degree: his education is a Product degree from CODE University of Applied Sciences plus an AI Safety Fundamentals course, not a PhD or research-track program. His prior ventures (Molecule GmbH, VitaDAO, Bio.xyz, dex.blue) are in biotech-DeFi and decentralized-exchange infrastructure, unrelated to core AI research. However, at Prime Intellect (co-founded with Johannes Hagemann) he is a listed contributor on real technical output — the dossier's OpenAlex/Semantic Scholar records show him as a co-author on the INTELLECT-3 technical report (arXiv 2512.16144, 2025), and Prime Intellect has shipped genuine decentralized/distributed LLM pretraining systems (the INTELLECT model series), which is hands-on engineering leadership of real training infrastructure rather than pure business role. This is a moderate case: some hands-on technical credibility via team-authored technical reports and building real distributed-training infra, but no personal foundational research record, so scored low-to-mid rather than high.\n\nWeisser's own contribution to the frontier lineage is thin: he is listed among 23 team authors on the INTELLECT-3 technical report (2025) and is NOT an author on INTELLECT-1 or INTELLECT-2, and Prime Intellect's outputs (decentralized/distributed training, prime-rl, TOPLOC, SHARDCAST) are novel training-infrastructure work but are not named building blocks that GPT/Claude/Gemini/Llama-class models descend from or cite. His verifiable language-modeling record begins only with Prime Intellect (~2023-2025), under 3 years, and reads as founder/CEO leadership rather than personal LM research — no CS/math degree, GitHub with 0 public repos, Wikidata occupation 'businessperson'. As a founder he is the business/vision co-founder of a genuinely core-AI company, but the science is carried by technical co-founder Johannes Hagemann and the research team (Jaghouar, Mattern, et al.), so he does not clear the scientific/technical-founder bar of personally authoring the core research, code or patents. Counting only years the record supports, ~2 years as a founder of an AI-core company with others doing the science.","evidence":[{"claim":"Co-founder & CEO of Prime Intellect, building decentralized compute/training infrastructure for open AI models","source_url":"https://www.vincentweisser.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prior roles: Co-Initiator at Bio.xyz, Molecule GmbH, VitaDAO, founding member of dex.blue","source_url":"https://theorg.com/org/prime-intellect/org-chart/vincent-weisser","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed co-author, INTELLECT-3: Technical Report, arXiv 2512.16144 (2025)","source_url":"https://doi.org/10.48550/arxiv.2512.16144","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-3 Technical Report (106B-parameter MoE, prime-rl asynchronous RL framework) lists Vincent Weisser among 23 authors from the Prime Intellect Team","source_url":"https://arxiv.org/abs/2512.16144","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning — author list is Sami Jaghouar, Justus Mattern, Jack Min Ong, Jannik Straube, Manveer Basra, Aaron Pazdera, Kushal Thaman, Matthew Di Ferrante, Felix Gabriel, Fares Obeid, Kemal Erdem, Michael Keiblinger and","source_url":"https://arxiv.org/abs/2505.07291","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-1 Technical Report — authors are Sami Jaghouar, Jack Min Ong, Manveer Basra, Fares Obeid, Jannik Straube, Michael Keiblinger, Elie Bakouch, Lucas Atkins, Maziyar Panahi, Charles Goddard, Max Ryabinin and Johannes Hagemann; Weisser is not an author","source_url":"https://arxiv.org/abs/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prime Intellect's INTELLECT-2 introduces prime-rl, TOPLOC verifiable inference and SHARDCAST weight distribution for permissionless decentralized RL training","source_url":"https://www.primeintellect.ai/blog/intellect-2","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q126287602 lists Weisser's occupation as 'businessperson' and employer as Molecule GmbH, with no research role recorded","source_url":"https://www.wikidata.org/wiki/Q126287602","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub account vincentweisser has 0 public repositories","source_url":"https://github.com/vincentweisser","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-3 Technical Report (2025) lists Vincent Weisser among ~23 Prime Intellect Team authors; frontier-relevant systems are team-authored, not personally his","source_url":"https://arxiv.org/abs/2512.16144","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-1 Technical Report authors are Jaghouar, Ong, Basra, Hagemann et al. — Weisser is not an author, so the core decentralized-training methods are not his personal contribution","source_url":"https://arxiv.org/abs/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q126287602 records Weisser's occupation as 'businessperson' with no research role, consistent with a CEO/business founder rather than a scientific founder","source_url":"https://www.wikidata.org/wiki/Q126287602","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Weisser is listed among the ~23-author Prime Intellect Team on the INTELLECT-3 technical report; not the sole/lead scientific author","source_url":"https://arxiv.org/abs/2512.16144","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"INTELLECT-1 Technical Report author list is led by Sami Jaghouar and includes Johannes Hagemann; Weisser is not an author, indicating the technical/scientific direction is carried by co-founders and the research team","source_url":"https://arxiv.org/abs/2412.01152","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q126287602 records Weisser's occupation as 'businessperson' with employer Molecule GmbH and no research role","source_url":"https://www.wikidata.org/wiki/Q126287602","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Prime Intellect's decentralized-RL stack (prime-rl, TOPLOC verifiable inference, SHARDCAST) is training infrastructure, not a building block frontier labs cite as foundational","source_url":"https://www.primeintellect.ai/blog/intellect-2","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.71,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":73},{"id":81,"slug":"vitalik-buterin","name":"Vitalik Buterin","title":"Co-founder","company":"Ethereum","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Vitalik_Buterin","image_url":"/api/v1/ceo-ai-leaderboard/portrait/vitalik-buterin.jpg","score":25,"tier":"informed_operator","dimensions":{"foundations":10,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":1,"hands_on_engineering":12,"industry_impact":8,"scientific_founder":7},"rubric_version":3,"weighted_score":25,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Buterin never completed a degree (he left the University of Waterloo after taking a Thiel Fellowship, though he did work as an undergraduate research assistant for cryptographer Ian Goldberg), so foundations is scored on verifiable self-taught depth in cryptography, distributed consensus, and mechanism design rather than credentials — this is real mathematical/algorithmic work (he authored the Ethereum whitepaper and Yellow Paper-adjacent design at 19-20) but is cryptography/game-theory/distributed-systems, not the linear-algebra/statistical-learning/ML core this rubric targets. OpenAlex confirms a genuine, non-trivial academic record (22 works, h-index 14, 789 citations) including peer-reviewed papers like 'A Flexible Design for Funding Public Goods' (Management Science, 2019) and 'Combining GHOST and Casper' — real, citable technical output, though entirely in blockchain consensus/cryptoeconomics, not vector embeddings or transformer/LM research. He personally designed and built Ethereum's protocol (hands-on engineering of a system that today runs at global scale), which supports high hands_on_engineering credit even though the system itself is not an AI system. No evidence found of him authoring or leading AI/ML research; his industry impact is enormous in crypto but not in the AI-systems sense this index measures, so transformers_lm and vector_embeddings score near the floor.\n\nNothing of Buterin's work is a building block of today's frontier language models: his verifiable record (Ethereum whitepaper, 'Combining GHOST and Casper', 'Aggregatable Subvector Commitments', public-goods funding mechanisms) is blockchain consensus, cryptography and cryptoeconomics, and no GPT/Claude/Gemini/Llama technical report cites or builds on it, so frontier_founder sits at the floor. He has zero verifiable record in the language-modeling lineage (vector-space/LSI/n-gram/neural LMs, seq2seq, transformers, LLM pretraining/alignment) — his AI involvement is existential-risk philanthropy and commentary, not research — so lm_domain_depth is near zero. He is, however, a genuine scientific/technical founder — he personally authored the whitepaper and core protocol design of Ethereum (2013/2014→present, ~12 years) rather than delegating the science — but that founding work is entirely OUTSIDE this index's field, which caps scientific_founder in the 3-7 'technical founder outside this field' band.","evidence":[{"claim":"Wrote the original Ethereum whitepaper in 2013 at age 19 and launched the network in 2015; worked as an undergraduate research assistant for cryptographer Ian Goldberg at University of Waterloo before leaving via Thiel Fellowship","source_url":"https://en.wikipedia.org/wiki/Vitalik_Buterin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Bitcoin Magazine in 2011 before designing and building Ethereum's smart-contract platform","source_url":"https://btcdirect.eu/en-eu/who-is-vitalik-buterin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Attended University of Waterloo and was a research assistant for cryptographer Ian Goldberg; dropped out in 2014 after a $100,000 Thiel Fellowship; described Ethereum in a white paper in November 2013; his documented AI involvement is existential-risk philanthropy and commentary, with no machine-lea","source_url":"https://en.wikipedia.org/wiki/Vitalik_Buterin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author record (Ethereum Foundation, unambiguous single-candidate match): 22 works, 789 citations, h-index 14, topics blockchain, cryptography and data security, game theory and auction theory","source_url":"https://api.openalex.org/authors/A5069172917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Aggregatable Subvector Commitments for Stateless Cryptocurrencies' (SCN 2020), a vector-commitment cryptography paper","source_url":"https://doi.org/10.1007/978-3-030-57990-6_3","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Combining GHOST and Casper' (2020), specifying Ethereum's proof-of-stake fork-choice and finality gadget","source_url":"https://arxiv.org/abs/2003.03052","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author record: 22 works, h-index 14, topics blockchain, cryptography and game/auction theory — no language-modeling or ML-core output","source_url":"https://api.openalex.org/authors/A5069172917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Buterin wrote the Ethereum whitepaper (2013) and personally designed the protocol, launching the network in 2015 — a technical founder whose company's core is blockchain, not AI/LM","source_url":"https://en.wikipedia.org/wiki/Vitalik_Buterin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Combining GHOST and Casper' (2020) specifies Ethereum's proof-of-stake fork choice — representative of his consensus/cryptoeconomics research, none of which feeds frontier LMs","source_url":"https://arxiv.org/abs/2003.03052","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex record shows all 22 works in blockchain/cryptography/game-theory topics with no AI/ML or language-modeling output; frontier LM training reports do not build on Ethereum consensus work","source_url":"https://api.openalex.org/authors/A5069172917","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Aggregatable Subvector Commitments for Stateless Cryptocurrencies' (SCN 2020) is cryptographic vector commitments for stateless blockchains, not representation-learning embeddings or any LM component","source_url":"https://doi.org/10.1007/978-3-030-57990-6_3","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Buterin authored the original Ethereum whitepaper in 2013 and personally set the protocol's technical direction, operating as its scientific/technical founder — a genuine founder-scientist role, but in blockchain, not AI/language modeling","source_url":"https://en.wikipedia.org/wiki/Vitalik_Buterin","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.8,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":74},{"id":74,"slug":"david-minarsch","name":"David Minarsch","title":"Co-founder & CEO","company":"Valory (Olas)","sector":"crypto","profile_url":null,"image_url":null,"score":24,"tier":"narrative_only","dimensions":{"foundations":8,"vector_embeddings":2,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":10,"industry_impact":6,"scientific_founder":7},"rubric_version":3,"weighted_score":24,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Minarsch holds a PhD in Applied Game Theory from the University of Cambridge and has a modest but genuine peer-reviewed record (OpenAlex: 10 works, 62 citations, h-index 5; Semantic Scholar corroborates 9 papers, 64 citations, h-index 5) spanning conflict/network game theory, auction theory, and multi-agent systems applied to blockchains and supply chains. This is legitimate graduate-level quantitative training (game theory, optimization over strategic interactions) adjacent to but not squarely inside the rubric's core (linear algebra/matrix methods/statistical learning); his multi-agent-systems papers ('Autonomous Economic Agents as a Second Layer Technology for Blockchains,' 'Implementation of Autonomous Supply Chains... Multi-Agent Approach') are about coordinating decentralized software agents on blockchains, not about neural sequence models, embeddings, or attention/transformer architectures, so transformers_lm and vector_embeddings credit stays low. He is the co-founder/CEO of Valory, which built the Open Autonomy framework and Olas protocol for on-chain autonomous agents — real, personally-led engineering of a production multi-agent system — supporting a moderate hands_on_engineering score, but the systems are agent-orchestration/DLT infrastructure rather than AI models themselves, so industry_impact (measured by core-AI-system leadership, citations, patents) is comparatively modest given the small citation base.\n\nNone of Minarsch's verifiable record — Cambridge PhD in applied game theory, conflict/network game theory, auction theory, and multi-agent DLT systems (Autonomous Economic Agent Framework, autonomous supply chains) — feeds the frontier language-model lineage: no attention/transformer, embedding, optimizer, tokenizer, pretraining, scaling or alignment work is cited by or built into GPT/Claude/Gemini/Llama technical reports, so frontier_founder is near-floor. He has zero verifiable years in language modeling specifically (his agents HIRE LLMs via the Mech Marketplace but he did not author neural/statistical LMs, vector-space text models or seq2seq/transformer work), so lm_domain_depth is near-floor. He is, however, a genuine technical founder — co-founder/CEO of Valory (founded ~2021, ~4 years), personally leading and co-authoring the Open Autonomy / Olas multi-agent framework with papers under that affiliation — but that field is blockchain agent orchestration, not the core AI/LM research the rubric scores, placing him at the top of the 'technical founder outside this field' band.","evidence":[{"claim":"Semantic Scholar author record (David E. N. Minarsch): 9 papers, 64 citations, h-index 5","source_url":"https://www.semanticscholar.org/author/David-E.-N.-Minarsch/3438886","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Valory team page: David Minarsch is co-founder and CEO of Valory, holds a PhD in Applied Game Theory from the University of Cambridge, and led the team that built 'the first framework for developing MAS in the DLT space'","source_url":"https://valory.xyz","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Valory; holds a PhD in Applied Game Theory from Cambridge University; led the team that built the first framework for developing multi-agent systems in the DLT space; Valory builds Pearl, the Mech Marketplace and the Olas Stack; co-founder David Galindo is the cryptographer CTO","source_url":"https://www.valory.xyz/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Olas coordinates autonomous AI agents that trade, influence and predict on behalf of owners, with the Mech Marketplace as an agent-hiring platform and Pearl as an agent app store; founded 2021","source_url":"https://olas.network/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Author of 'The Strategy of Conquest', Journal of Economic Theory (2020)","source_url":"https://doi.org/10.1016/j.jet.2020.105161","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'Autonomous Economic Agents as a Second Layer Technology for Blockchains: Framework Introduction and Use-Case Demonstration' (CVCBT 2020)","source_url":"https://doi.org/10.1109/cvcbt50464.2020.00007","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Minarsch co-authored 'Autonomous Economic Agent Framework' (LNCS, 2022), reflecting personally-authored technical work under the Valory/Fetch lineage rather than any transformer/LM contribution","source_url":"https://doi.org/10.1007/978-3-030-97457-2_14","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Olas/Valory (founded 2021) coordinates autonomous AI agents that trade/predict on behalf of owners; the platform orchestrates and hires models rather than training language models, and Minarsch is co-founder/CEO leading the Open Autonomy framework","source_url":"https://olas.network/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Minarsch co-authored 'Autonomous Economic Agents as a Second Layer Technology for Blockchains' (CVCBT 2020) — DLT multi-agent framework work, no LM/transformer lineage","source_url":"https://doi.org/10.1109/cvcbt50464.2020.00007","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Valory, PhD in Applied Game Theory (Cambridge), led the team that built the first framework for multi-agent systems in the DLT space (Open Autonomy / Olas Stack, Pearl, Mech Marketplace)","source_url":"https://www.valory.xyz/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Olas/Valory (founded 2021) coordinates autonomous agents on-chain — agent-orchestration infrastructure, not language-model research","source_url":"https://olas.network/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.78,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":75},{"id":66,"slug":"harry-grieve","name":"Harry Grieve","title":"Co-founder & CEO","company":"Gensyn","sector":"crypto","profile_url":null,"image_url":null,"score":24,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":3,"transformers_lm":7,"frontier_founder":2,"lm_domain_depth":3,"hands_on_engineering":7,"industry_impact":8,"scientific_founder":6},"rubric_version":3,"weighted_score":24,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The dossier's Wikipedia (chess grandmaster) and OpenAlex (University of Bath organometallic chemistry researcher) matches are both clearly the wrong person and are disregarded. However, one OpenAlex-listed 'top_work' — the arXiv paper 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (2509.08721, 2025) — was independently verified via direct arXiv fetch to be a genuine Gensyn systems paper, and Harry Grieve is confirmed as one of its 15 listed co-authors (alongside other Gensyn engineers such as Jeffrey Amico and Matthew J. Wright), with the paper reporting tests on a decentralized RL training network of thousands of Gensyn community nodes. No personal lead-authorship, individually-authored papers, specific contribution statement, university degree, or pre-Gensyn technical role could be verified for Grieve via direct web fetch (search tools were unavailable this session; Gensyn's site has no team/about page and LinkedIn/Crunchbase pages returned errors). Gensyn itself has a substantial, real technical research output (RL post-training, MoE routing, pipeline-parallelism security) rather than being vaporware, which supports a moderate industry_impact and hands_on_engineering score for building/leading the org, but with no verified personal research record Grieve individually scores low on foundations and vector_embeddings per rubric guidance to score lower when unsure. transformers_lm and hands_on_engineering get partial credit strictly for the one verified co-authorship credit on a real transformer/LM post-training systems paper produced by an org he leads, not for demonstrated personal depth.\n\nNothing of Grieve's own work is a building block today's frontier models (GPT/Claude/Gemini/Llama) descend from: his only verifiable technical output is a single co-authorship (6th of 15) on Gensyn's 2025 SAPO decentralized-RL-post-training paper (0 citations), which no frontier lab cites or builds on. His verifiable language-modeling record is under one year (first LM-lineage work 2025); his background is economics/finance (MA Aberdeen, MPA Brown) and applied data science (Director of Data Research at Cytora), not statistical/neural language modeling. He is a genuine co-founder of Gensyn (2020, ~5-6 years) — a real decentralized-ML-training deep-tech company — and is variously listed as co-founder/CTO, but the core research and litepaper are attributed to 'the Gensyn team' (co-founder Ben Fielding holds the AI PhD and leads the science), so his role reads as founder-operator of an AI company whose science is done by others rather than the author of the core research/code/patents.","evidence":[{"claim":"Harry Grieve is confirmed (via direct arXiv fetch) as one of 15 co-authors on 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing', a Gensyn paper on collective RL experience sharing tested on a decentralized network of thousands of Gensyn community nodes.","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The same paper's PDF confirms the 15-author list including Harry Grieve, and cites Gensyn's own prior work (gensyn2025genrl, gensyn2025rlswarm), confirming this is a genuine Gensyn-authored systems paper, not a homonym or unrelated work.","source_url":"https://arxiv.org/pdf/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gensyn maintains an active research publication list (RL post-training, mixture-of-experts routing, pipeline-parallelism security, prediction-market mechanism design), indicating the company does genuine technical AI/ML infrastructure work rather than being purely business/marketing-driven.","source_url":"https://www.gensyn.ai/research","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gensyn's product line (Delphi information markets, RL-Swarm, AXL peer-to-peer AI communication, CodeAssist) confirms the company's core business is decentralized ML training/inference infrastructure, consistent with the dossier's company description.","source_url":"https://www.gensyn.ai/news","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author (sixth of fifteen) of 'Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing' (arXiv:2509.08721, 10 September 2025), introducing SAPO, decentralized RL post-training via shared rollouts across heterogeneous nodes","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The Gensyn litepaper (February 2022) is authored by 'the Gensyn team' and derives its verification approach from Jia et al. (2021) probabilistic proof-of-learning, Zheng et al. (2021) graph-based pinpoint protocol and Truebit-style incentive games, rather than original learning results","source_url":"https://docs.gensyn.ai/litepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"An arXiv author search for 'Grieve_H' returns zero results, indicating no independent arXiv publication record under that name","source_url":"http://export.arxiv.org/api/query?search_query=au:%22Grieve_H%22&start=0&max_results=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gensyn's public product and research line (Delphi, CodeAssist, REE, AXL peer-to-peer communication) is company output listed on its own site, with no individual authorship attributed to Grieve","source_url":"https://www.gensyn.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Grieve's only verified LM-lineage output is co-authorship (6th of 15) on Gensyn's 'Sharing is Caring' SAPO decentralized RL post-training paper (arXiv:2509.08721, Sept 2025, 0 citations); no independent arXiv record under his name.","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gensyn's litepaper and research are attributed to 'the Gensyn team' and derive verification from prior proof-of-learning work (Jia et al. 2021, Zheng et al. 2021), not original learning results authored by Grieve.","source_url":"https://docs.gensyn.ai/litepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Grieve is one of 15 co-authors on Gensyn's 'Sharing is Caring' (SAPO) 2025 RL-post-training paper — his only verifiable LM-lineage work, with no independent authorship and zero citations to date.","source_url":"https://arxiv.org/abs/2509.08721","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.55,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":76},{"id":86,"slug":"gavin-wood","name":"Gavin Wood","title":"Founder","company":"Polkadot, Parity Technologies","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Gavin_Wood","image_url":"/api/v1/ceo-ai-leaderboard/portrait/gavin-wood.jpg","score":22,"tier":"narrative_only","dimensions":{"foundations":8,"vector_embeddings":4,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":11,"industry_impact":5,"scientific_founder":6},"rubric_version":3,"weighted_score":22,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Gavin Wood holds an MEng in Computer Systems and Software Engineering (2002) and a PhD from the University of York (2005), thesis 'Content-based visualization to aid common navigation of musical audio' — genuine graduate-level technical training, but in music-information-retrieval/signal-processing visualization, not the linear-algebra/optimization/statistical-learning core the rubric weights, and not in vector embeddings or the attention/transformer lineage. His major verified technical achievements are in blockchain systems engineering: he wrote most of the original Ethereum client code, authored the Ethereum Yellow Paper formally specifying the EVM, invented the Solidity language, and founded Polkadot/Kusama — substantial hands-on systems-engineering and protocol-design work, but this is cryptographic/distributed-systems engineering, not AI/ML research or infrastructure. No evidence was found of any authored work, patents, or shipped systems touching vector embeddings, transformers, or language modeling. hands_on_engineering is scored solidly for his genuine protocol/systems-building record; the AI-specific dimensions are scored near floor.\n\nGavin Wood's verifiable technical record is entirely in blockchain/distributed-systems protocol design — the Ethereum Yellow Paper (EVM formal spec), the original Ethereum client, the Solidity language, and Polkadot/Kusama — none of which appears in the architecture, embeddings, optimizers, tokenizers, pretraining, scaling or alignment lineage that GPT/Claude/Gemini/Llama-class models descend from, so frontier_founder scores at floor. He has zero record in language modeling — statistical/neural LMs, vector-space text models, seq2seq, transformers or LLM pretraining/alignment — and his 2005 PhD was on content-based visualization of musical audio, not LM lineage, so lm_domain_depth scores at floor. He is, however, a genuine deeply-technical founder who personally authored the core research and code of the companies he built (Parity Technologies founded 2015; Web3 Foundation/Polkadot ~2017), roughly 11 years operating as founder-CTO/chief-scientist — but that field is cryptographic distributed systems, not the language-modeling core the rubric weights, which the anchors place in the 3-7 'technical founder outside this field' band.","evidence":[{"claim":"MEng Computer Systems and Software Engineering (2002) and PhD (2005), University of York, thesis on content-based visualization for musical audio navigation","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wrote most of the code for the first version of Ethereum, served as CTO of the Ethereum Foundation, authored the Ethereum Yellow Paper defining the EVM, credited with inventing Solidity","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'Ethereum: A Secure Decentralised Generalised Transaction Ledger' co-authored work, 5309 citations per OpenAlex, is blockchain protocol design, not AI research","source_url":"https://doi.org/10.48550/arxiv.2005.13456","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MEng in Computer Systems and Software Engineering, University of York (2002); PhD 2005, thesis 'Content-based visualization to aid common navigation of musical audio'; research scientist at Microsoft; authored the Ethereum Yellow Paper specifying the EVM; proposed Solidity; first CTO of the Ethereum","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ethereum: A Secure Decentralised Generalised Transaction Ledger (the Yellow Paper) is credited to Gavin Wood with ~5,309 citations in OpenAlex","source_url":"https://ethereum.github.io/yellowpaper/paper.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Overview of Polkadot and its Design Considerations (arXiv 2005.13456) — Wood co-author","source_url":"https://arxiv.org/abs/2005.13456","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q43379767 records a PhD, education at University of York, and occupations computer scientist, researcher, software developer; notable works Ethereum and Polkadot","source_url":"https://www.wikidata.org/wiki/Q43379767","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wood authored the Ethereum Yellow Paper formally specifying the EVM, wrote most of the first Ethereum client, invented Solidity, and founded Parity Technologies and Polkadot/Kusama — all distributed-systems/cryptography work, none in the transformer/LM/embedding frontier lineage","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wood's PhD (University of York, 2005) thesis was 'Content-based visualization to aid common navigation of musical audio' — music-information-retrieval/signal-processing, not language modeling","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wood co-founded and serves as a technical founder of Parity Technologies (2015) and the Web3 Foundation, personally driving the Polkadot protocol design (arXiv 2005.13456)","source_url":"https://arxiv.org/abs/2005.13456","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wood wrote most of the first Ethereum client, authored the Ethereum Yellow Paper specifying the EVM, invented Solidity, and founded Parity Technologies and Polkadot — all blockchain/distributed-systems work, not AI/LM","source_url":"https://en.wikipedia.org/wiki/Gavin_Wood","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The Yellow Paper 'Ethereum: A Secure Decentralised Generalised Transaction Ledger' is a distributed-ledger protocol spec with no attention/transformer/embedding content","source_url":"https://ethereum.github.io/yellowpaper/paper.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Wood as co-founder of Ethereum and creator of Polkadot/Kusama, occupations computer scientist/researcher/software developer — a technical founder whose companies' core is blockchain, not language modeling","source_url":"https://www.wikidata.org/wiki/Q43379767","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.76,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":77},{"id":45,"slug":"mark-zuckerberg","name":"Mark Zuckerberg","title":"Founder, Chairman & CEO","company":"Meta Platforms","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Mark_Zuckerberg","image_url":"/api/v1/ceo-ai-leaderboard/portrait/mark-zuckerberg.jpg","score":22,"tier":"narrative_only","dimensions":{"foundations":3,"vector_embeddings":3,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":6,"industry_impact":12,"scientific_founder":6},"rubric_version":3,"weighted_score":22,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Zuckerberg attended Harvard studying psychology and computer science but dropped out in his second year (2004) without completing a degree, and has no graduate training, thesis, or authored research in linear algebra, optimization, statistical learning, embeddings, or the attention/transformer lineage. His youth programming (ZuckNet, the Synapse Media Player using a basic recommendation heuristic, ~2002) shows early general coding aptitude but predates and is unrelated to the vector-space/embeddings/transformer research lineage the rubric asks about, and is not itself ML research. He is listed as a co-founder of FAIR (Facebook AI Research, 2013) alongside Yann LeCun, Rob Fergus, and Serkan Piantino, but every source describes his role as organizational/funding leadership — LeCun directed FAIR's actual research program, and Zuckerberg is not credited as a contributor to FAIR's canonical outputs (fastText, PyTorch, Llama). His OpenAlex entry (1,219 works, h-index 15) is a clear name-collision aggregate — the top-cited 'works' are business/patent-law/operations-research papers (SIAM Journal on Optimization, LP relaxation) by other people named Mark Zuckerberg, not this individual, and Semantic Scholar's clean 2-paper/0-citation match is the more trustworthy proxy for his actual personal authorship record, which is essentially nil. Per the rubric, industry_impact still credits him meaningfully for having founded and funded Meta, whose org (under researchers he hired and funded, not personally led technically) produced PyTorch and the Llama model family, which the field runs on — but foundations/embeddings/transformers_lm dimensions stay low because he has no personal research record, exactly the rubric's stated case for 'famous CEO with no personal research record scores LOW' on those axes.\n\nToday's frontier models draw on Meta org outputs — PyTorch, fastText and the Llama family — but those are the work of researchers Zuckerberg hired and funded (LeCun, and the GenAI/FAIR teams), not of his own authorship; no paper, architecture, optimizer, tokenizer or training method traces to him personally, so his frontier lineage is organizational-funder only, not a named building block (frontier_founder 3). He has no verifiable personal record in language modeling at any point in the 1975→2017→2020+ lineage — his youth work (Synapse audio recommender, ~2002) is unrelated, and Semantic Scholar shows 2 papers / 0 citations — so lm_domain_depth is essentially nil (2). He is a genuine technical founder who personally wrote Facebook's original code in 2004 and co-founded FAIR (2013), which is why he clears the floor, but the science and engineering of Meta's AI/LM systems were and are executed by others, placing him squarely in the 'technical founder outside this field / founder-CEO whose AI science is done by others' band (scientific_founder 5).","evidence":[{"claim":"Attended Harvard 2002-2004 studying psychology and computer science; dropped out without completing a degree","source_url":"https://en.wikipedia.org/wiki/Mark_Zuckerberg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder of Facebook AI Research (FAIR) in 2013 alongside Yann LeCun, Rob Fergus, Serkan Piantino; LeCun directed FAIR's research 2013-2018, not Zuckerberg","source_url":"https://en.wikipedia.org/wiki/Meta_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Meta AI/FAIR produced fastText (2016), PyTorch (2017), and Llama (2023) as organizational outputs, attributed to the research org rather than to Zuckerberg personally","source_url":"https://en.wikipedia.org/wiki/Meta_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Studied psychology and computer science at Harvard from 2002, dropped out in his second year; built ZuckNet, Synapse Media Player (machine learning for listening preferences), CourseMatch and Facemash; Wikipedia documents no AI research, papers or technical contributions to ML, Llama or FAIR","source_url":"https://en.wikipedia.org/wiki/Mark_Zuckerberg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The SIAM Journal on Optimization paper 'Subset Algebra Lift Operators for 0-1 Integer Programming' (2004) attributed to a Mark Zuckerberg is by D. Bienstock and M. Zuckerberg (DBLP key journals/siamjo/BienstockZ04), an integer-programming researcher, not the Meta CEO","source_url":"https://api.semanticscholar.org/graph/v1/paper/DOI:10.1137/S1052623402420346?fields=title,year,authors,venue,externalIds","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record for the exact name 'Mark Zuckerberg' shows 2 papers and 0 citations, h-index 0","source_url":"https://api.semanticscholar.org/graph/v1/author/2075634992?fields=name,paperCount,citationCount,hIndex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata lists his occupation as programmer, entrepreneur, computer scientist and CEO, education at Harvard (psychology, computer science) from 2002, employer Meta Platforms from 2004; no doctoral degree or advisor is recorded","source_url":"https://www.wikidata.org/wiki/Q36215","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Meta AI/FAIR produced fastText (2016), PyTorch (2017) and Llama (2023) as organizational outputs; Yann LeCun directed FAIR's research 2013-2018, not Zuckerberg","source_url":"https://en.wikipedia.org/wiki/Meta_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Zuckerberg co-founded Facebook in 2004, personally writing its original code, and is CEO/chairman/controlling shareholder of Meta — a technical founder whose company's core was a social network, not AI/LM systems","source_url":"https://en.wikipedia.org/wiki/Mark_Zuckerberg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"FAIR/Meta AI's canonical outputs (fastText 2016, PyTorch 2017, Llama 2023) are attributed to the research org under Yann LeCun's direction, not to Zuckerberg personally","source_url":"https://en.wikipedia.org/wiki/Meta_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Zuckerberg co-founded Facebook in 2004 and personally wrote its original code as a programmer, becoming founder-CEO of what is now Meta; his technical founding is in social networking, not AI/language-modeling science","source_url":"https://en.wikipedia.org/wiki/Mark_Zuckerberg","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.81,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":78},{"id":43,"slug":"sam-altman","name":"Sam Altman","title":"Co-founder & CEO","company":"OpenAI","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Sam_Altman","image_url":"/api/v1/ceo-ai-leaderboard/portrait/sam-altman.jpg","score":22,"tier":"narrative_only","dimensions":{"foundations":3,"vector_embeddings":1,"transformers_lm":4,"frontier_founder":4,"lm_domain_depth":3,"hands_on_engineering":5,"industry_impact":13,"scientific_founder":4},"rubric_version":3,"weighted_score":22,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Altman is the clearest case in this batch of the pattern the rubric explicitly separates out: enormous influence over the field with no personal record in its core. He studied computer science at Stanford for two years and left in 2005 without a degree, so there is no thesis, no graduate training in linear algebra, optimization or statistical learning, and no verifiable coursework record. He has authored no research in the lineage: his only OpenAlex-indexed item is the GPT-5 System Card (2025), an institutional document listing OpenAI staff, and he appears on the GPT-4 Technical Report the same way — corporate authorship convention across hundreds of names, not a technical contribution to embeddings, attention, pretraining or scaling. No first-author paper, no research-lead role, and no AI/ML patent as inventor were found in any source; the patents on his record are Loopt-era location-based social networking. His hands-on engineering is not zero because he wrote part of the original Loopt codebase as a working founder-engineer from 2005, but that is mobile social software, not AI systems, models or the infrastructure under them, and the engineering at Loopt was led by co-founder Nick Sivo. Industry impact is the one genuinely high dimension, credited strictly on the permitted ground that he co-founded and leads the laboratory that produced GPT-3, InstructGPT/RLHF and GPT-4 — canonical work the field builds on — and that he set its research direction and compute strategy. His fundraising, the Microsoft deal, ChatGPT's user numbers, his investor record and his public profile are excluded entirely; stripped of those, his personal technical record in the core of AI is thin.\n\nNothing of Altman's OWN authorship is part of the foundation frontier models are built on: the transformer, attention, RLHF, scaling-law and pretraining-objective work that GPT/Claude/Gemini/Llama descend from was authored by researchers (Vaswani et al.; OpenAI's Radford, Brown, Ouyang, Christiano et al.), and his only lineage appearances are institutional bylines on the GPT-4 Technical Report and GPT-5 System Card — corporate authorship convention, not a named building block, so frontier_founder sits in the 'applies/leads but no foundational contribution' band. He has no verifiable personal language-modeling record — no statistical/neural LM, embedding, seq2seq or transformer work under his own name across the pre-word2vec-to-transformer arc — so lm_domain_depth is near the floor despite OpenAI's founding in 2015; his role is organizational, not hands-on LM research. He is a co-founder and CEO of OpenAI but the science and engineering are done by others (Sutskever as Chief Scientist and the research staff), which is precisely the 'founder/CEO of an AI company whose science was done by others' anchor (3-7), not a technical/scientific founder who authored the core research, code or patents.","evidence":[{"claim":"Attended Stanford University for two years studying computer science and left in 2005 without earning a degree","source_url":"https://en.wikipedia.org/wiki/Sam_Altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records his occupations as businessperson, programmer, entrepreneur and chief executive officer, educated at Stanford in computer science, with no doctoral or research affiliation","source_url":"https://www.wikidata.org/wiki/Q7407093","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of many co-authors on the GPT-4 Technical Report (2023), an organizational authorship credit across hundreds of names rather than a personal research contribution","source_url":"https://arxiv.org/abs/2303.08774","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Dropped out of Stanford University after two years studying computer science; no degree completed","source_url":"https://finance.yahoo.com/technology/ai/articles/sam-altman-dropped-stanford-2-181500595.html","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Loopt (2005); technical/engineering work led by co-founder Nick Sivo, Altman in CEO/business role","source_url":"https://interestingengineering.com/culture/who-is-sam-altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"President of Y Combinator 2014-2019 (investor/accelerator leadership, not research)","source_url":"https://techcrunch.com/2019/03/08/y-combinator-president-sam-altman-is-stepping-down-amid-a-series-of-changes-at-the-accelerator","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Attended Stanford University for two years studying computer science and dropped out without earning a bachelor's degree in 2005; co-founded Loopt at 19; joined Y Combinator 2011 and became president 2014; co-founded OpenAI in 2015 and has been CEO since 2019; described in executive and entrepreneur","source_url":"https://en.wikipedia.org/wiki/Sam_Altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Altman appears on the GPT-4 Technical Report only as one of hundreds of organizational co-authors, not as a technical contributor to attention, pretraining or scaling","source_url":"https://arxiv.org/abs/2303.08774","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Altman is CEO of OpenAI; Ilya Sutskever served as co-founder and Chief Scientist responsible for the research direction, indicating the science was led by others","source_url":"https://en.wikipedia.org/wiki/Sam_Altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Altman is CEO of OpenAI since 2019 and a co-founder (2015); the lab's language-model research is authored by its research staff, not by him","source_url":"https://en.wikipedia.org/wiki/Sam_Altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His OpenAlex/authorship footprint is limited to institutional documents (GPT-4 Technical Report, GPT-5 System Card) carrying hundreds of corporate co-authors, with no personal contribution to attention, pretraining or scaling","source_url":"https://arxiv.org/abs/2303.08774","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Left Stanford computer science after two years with no degree and co-founded Loopt (mobile social software) as founder-CEO, engineering led by co-founder Nick Sivo — no technical-founder record in language modeling","source_url":"https://en.wikipedia.org/wiki/Sam_Altman","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.92,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":79},{"id":41,"slug":"arvind-krishna","name":"Arvind Krishna","title":"Chairman & CEO","company":"IBM","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","image_url":"/api/v1/ceo-ai-leaderboard/portrait/arvind-krishna.jpg","score":21,"tier":"narrative_only","dimensions":{"foundations":8,"vector_embeddings":2,"transformers_lm":3,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":6,"industry_impact":10,"scientific_founder":2},"rubric_version":3,"weighted_score":21,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Krishna holds a PhD in electrical engineering from the University of Illinois Urbana-Champaign (1991), with graduate research reported as being in distributed systems and data management — genuine doctoral-level technical training, but not in the AI/ML/transformer lineage this rubric targets, giving modest foundations credit. He joined IBM's Thomas J. Watson Research Center in 1990 and spent ~18 years in research roles (database servers, security software) before moving into executive leadership; no evidence was found of personally authored papers, patents, or code in linear algebra/optimization, embeddings, or transformer/LM research specifically — his later Director of IBM Research and CEO roles (2015-2020-present) are organizational leadership over research divisions (including Watson-era AI), not personal authorship of core-AI work. As CEO, he architected the Red Hat acquisition and has directed IBM's AI/cloud/quantum strategy, which is real industry impact on AI-adjacent business lines, but the rubric explicitly excludes 'manages builders, no personal record' from higher scores, so industry_impact reflects organizational leadership of a major tech company rather than lab leadership that personally produced canonical AI work.\n\nNothing of Krishna's own authorship sits in the lineage that GPT/Claude/Gemini/Llama-class models descend from — no attention, transformer, embedding, optimizer, tokenizer, scaling or alignment contribution is retrievable under his name; the only matching OpenAlex record (A5071248780) is a homonym electronics-packaging engineer, and his verified technical work was in distributed systems, databases and security software at IBM Watson (1990–~2009), not language modeling, so his years of continuous personal language-modeling research are effectively zero. He is CEO/chairman of IBM, a century-old company he did not found and whose AI science is produced by others under his organizational leadership, so he does not operate as a scientific/technical founder of any company whose core is these systems. His AI-era role is executive and strategic (Red Hat acquisition, cloud/cognitive strategy), which the rubric explicitly excludes from these founder/depth dimensions.","evidence":[{"claim":"PhD Electrical Engineering, University of Illinois Urbana-Champaign, 1991; BTech Electrical Engineering, IIT Kanpur, 1985.","source_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Joined IBM's Thomas J. Watson Research Center in 1990, PhD research reported in distributed systems and data management, spent 18 years at Watson Research through 2009 in technical/software roles (database servers, security software).","source_url":"https://grainger.illinois.edu/alumni/distinguished/Arvind-Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Named Senior Vice President and Director of IBM Research in January 2015; CEO of IBM since April 2020, chairman since January 2021; principal architect of the Red Hat acquisition.","source_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BTech in electrical engineering, IIT Kanpur (1985); PhD in electrical engineering, University of Illinois Urbana-Champaign (1991); joined IBM Thomas J. Watson Research Center 1990 and stayed 18 years; SVP IBM Research 2015; SVP Cloud and Cognitive Software; chairman and CEO of IBM from April 2020; c","source_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education at IIT Kanpur (BTech electrical engineering, 1980-1985) and University of Illinois Urbana-Champaign (MS and PhD in electrical engineering, 1985-1991), occupation chief executive officer from 2020, employer IBM","source_url":"https://www.wikidata.org/wiki/Q56276330","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No AI-lineage authorship is retrievable: the OpenAlex author record matched to this name (A5071248780, 24 works, 66 citations, h-index 4) consists of electronic-packaging, solder-void and polymer-fracture papers with affiliations at Aptiv, Qualcomm UK, PES University and Ohio State, which do not cor","source_url":"https://api.openalex.org/authors/A5071248780","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Krishna is chairman and CEO of IBM since April 2020; he began at IBM's Thomas J. Watson Research Center in 1990 and rose through research/software roles — he did not found IBM or any company in the AI lineage.","source_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The only OpenAlex author record matching the name (24 works, h-index 4) is electronic-packaging/solder-void/polymer-fracture research at Aptiv/Qualcomm UK/PES University/Ohio State — a homonym, not the IBM CEO, and contains no language-modeling or transformer-lineage work.","source_url":"https://api.openalex.org/authors/A5071248780","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Krishna joined IBM's Thomas J. Watson Research Center in 1990 and rose through research and executive roles to become CEO in 2020 and chairman in 2021 — never a founder; his research background is databases/security, not the transformer/LM lineage.","source_url":"https://en.wikipedia.org/wiki/Arvind_Krishna","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.77,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":80},{"id":79,"slug":"alex-blania","name":"Alex Blania","title":"Co-founder & CEO","company":"Tools for Humanity / World (Worldcoin)","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Alex_Blania","image_url":"/api/v1/ceo-ai-leaderboard/portrait/alex-blania.jpg","score":20,"tier":"narrative_only","dimensions":{"foundations":8,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":8,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":20,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Blania holds a dual bachelor's in physics and industrial engineering from the University of Erlangen-Nuremberg (FAU) and pursued a physics master's, doing thesis research at the Max Planck Institute for the Science of Light and finishing at Caltech's Institute for Quantum Information and Matter. He co-authored one real, verifiable paper — 'Deep learning of spatial densities in inhomogeneous correlated quantum systems' (arXiv 2211.09050, 2022) with Sandro Herbig, Fabian Dechent, Evert van Nieuwenburg, and Florian Marquardt (a well-known quantum-machine-learning group leader) — which applies convolutional neural networks to predict physical observables in quantum many-body systems; this is genuine hands-on deep-learning work and gives some graduate-level statistical-learning/optimization credibility, but it is a single co-authored paper (likely thesis-derived, 3 citations) in computational physics, not language modeling, embeddings, or transformer research. He has no found publication, patent, or system in vector embeddings or transformer/LM research, consistent with his training being physics/quantum-computing rather than NLP. His hands-on engineering record is strongest as a builder of the Worldcoin/World Orb biometric hardware and World ID/World App systems since 2020 — real, personally-led systems engineering, but the core technology (iris-biometric hardware, blockchain identity) is not core language-modeling/transformer work, so industry_impact is scored on the basis of building real, complex technical systems rather than on funding raised or market cap.\n\nBlania's only verifiable research is a single 2022 CNN paper on quantum many-body densities (arXiv:2211.09050) — no attention, transformer, embedding, tokenizer, scaling, alignment or dataset work; nothing today's frontier language models (GPT/Claude/Gemini/Llama) descend from, so frontier_founder sits at the floor. He has zero verifiable years in language modeling specifically (statistical/neural LMs, vector-space text, seq2seq, transformers, LLM pretraining/alignment); his lineage is physics/quantum ML and biometric-identity hardware, so lm_domain_depth is at the floor. He is a genuine co-founder and CEO of Tools for Humanity/World since ~2019-2020 (~6 years) with a real technical (physics/ML) background, but the company's core science is iris-biometric hardware, zero-knowledge identity and blockchain — a technical founder OUTSIDE the language-modeling field — which places scientific_founder in the 3-7 band; he operates as founder-CEO rather than the author of the core LM research/patents these systems would need.","evidence":[{"claim":"Blania holds degrees in physics and industrial engineering from the University of Erlangen-Nuremberg (FAU) and pursued a physics master's, doing thesis work at Max Planck Institute for the Science of Light and Caltech's Institute for Quantum Information and Matter","source_url":"https://www.fau.eu/2024/11/news/fau-alumni-mystory-alex-blania-ceo-tools-for-humanity/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-authored 'Deep learning of spatial densities in inhomogeneous correlated quantum systems' (arXiv:2211.09050, 2022) with Sandro Herbig, Fabian Dechent, Evert van Nieuwenburg, and Florian Marquardt, applying CNNs to predict observables in correlated quantum many-body systems","source_url":"https://arxiv.org/abs/2211.09050","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Is CEO of Tools for Humanity and co-founder/CEO of World (Worldcoin), which built the Orb iris-biometric verification hardware (development began 2020, manufacturing established in Erlangen, field testing by 2021), World ID, and World App","source_url":"https://en.wikipedia.org/wiki/Alex_Blania","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Tools for Humanity has raised over $300 million at approximately a $3 billion valuation from established investors (a16z, Blockchain Capital, others), not family/friends money","source_url":"https://en.wikipedia.org/wiki/Alex_Blania","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Dual bachelor's in physics and industrial engineering from University of Erlangen-Nuremberg; master's research in quantum computing and AI at Caltech's Institute for Quantum Information and Matter; left Caltech in October 2019 after Sam Altman and Max Novendstern approached him, to found World and T","source_url":"https://en.wikipedia.org/wiki/Alex_Blania","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"First author of 'Deep learning of spatial densities in inhomogeneous correlated quantum systems' (arXiv:2211.09050, submitted 16 November 2022) with Sandro Herbig, Fabian Dechent, Evert van Nieuwenburg and Florian Marquardt — CNNs trained on random potentials to predict densities in 1D and 2D lattic","source_url":"https://arxiv.org/abs/2211.09050","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q135216343 records him as a German entrepreneur born 1993, occupation computer scientist, with no academic identifiers (no ORCID, no Google Scholar id).","source_url":"https://www.wikidata.org/wiki/Q135216343","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Blania's sole indexed publication is 'Deep learning of spatial densities in inhomogeneous correlated quantum systems' (arXiv:2211.09050, 2022) — CNNs for quantum physics, not any transformer/LM/embedding lineage frontier models build on","source_url":"https://arxiv.org/abs/2211.09050","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Blania co-founded Tools for Humanity and World (Worldcoin) and is CEO, with the core technology being the Orb iris-biometric hardware and World ID/World App — a biometric-identity and blockchain company, not a language-modeling lab","source_url":"https://en.wikipedia.org/wiki/Alex_Blania","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"He left Caltech's Institute for Quantum Information and Matter in October 2019 to co-found the company, giving ~6 years as a founder-CEO with a physics/quantum-ML background outside the LM field","source_url":"https://www.fau.eu/2024/11/news/fau-alumni-mystory-alex-blania-ceo-tools-for-humanity/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Blania's only verifiable paper applies CNNs to spatial densities in correlated quantum systems (arXiv:2211.09050, 2022) — computational physics, not any transformer/embedding/LM building block used by frontier models","source_url":"https://arxiv.org/abs/2211.09050","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Blania co-founded Tools for Humanity and World (Worldcoin), leaving Caltech in October 2019; the company's core technology is the Orb iris-biometric verification hardware and World ID/World App decentralized identity — not language modeling","source_url":"https://en.wikipedia.org/wiki/Alex_Blania","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him as a German entrepreneur (occupation computer scientist) with no ORCID/Google Scholar identifiers and no language-modeling publication record","source_url":"https://www.wikidata.org/wiki/Q135216343","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.75,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":81},{"id":80,"slug":"dominic-williams","name":"Dominic Williams","title":"Founder & Chief Scientist","company":"DFINITY (Internet Computer)","sector":"crypto","profile_url":null,"image_url":null,"score":20,"tier":"narrative_only","dimensions":{"foundations":6,"vector_embeddings":1,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":11,"industry_impact":6,"scientific_founder":7},"rubric_version":3,"weighted_score":20,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Williams has a verifiable and substantial technical record entirely in distributed systems and cryptography, with nothing anywhere in the AI lineage. I confirmed the authorship of his principal paper directly against the IACR ePrint archive: 'Internet Computer Consensus' (ePrint 2021/632, with Jan Camenisch, Manu Drijvers, Timo Hanke, Yvonne-Anne Pignolet and Victor Shoup) introduces the ICC family of Byzantine fault-tolerant atomic-broadcast protocols under partial synchrony, with probabilistic leader rotation and optimistic responsiveness. He is also a co-author of the earlier 'DFINITY Technology Overview Series, Consensus System' (2018). Co-authoring a consensus protocol alongside cryptographers of Shoup's and Camenisch's standing is real mathematical competence in probability, cryptography and distributed algorithms, which supports a foundations score adjacent to graduate training — but it is not the linear algebra, optimization or statistical learning the rubric names, and none of it touches representation learning. He founded DFINITY in 2016 and is credibly the principal architect of the Internet Computer's chain-key design, which is why hands-on engineering is his highest dimension. I found no paper, model or system authored by him in vector-space models, embeddings, retrieval, attention, transformers, pretraining, scaling or alignment. The Internet Computer now markets 'Caffeine AI' and an AI app builder, but that is a platform consuming third-party models, and marketing positioning is not evidence under this rubric.\n\nNothing Williams has authored enters the frontier language-model lineage: his verifiable output is the ICC Byzantine-consensus protocol family (IACR ePrint 2021/632, PODC 2022) and the 2018 DFINITY consensus overview — distributed systems and cryptography, not attention, transformers, embeddings, tokenizers, optimizers, pretraining, scaling or alignment — and no frontier model's technical report descends from it, so frontier_founder and lm_domain_depth are essentially absent (0 verifiable years in language modeling; the dossier's 1971/55-year figures are an OpenAlex homonym artifact from a Holocaust-studies scholar). He is, however, a genuine scientific/technical founder: he founded DFINITY in 2016 and personally co-authored the core consensus research and chain-key design the Internet Computer runs on, ~9 years in that role — but that role sits entirely OUTSIDE this field (crypto/distributed systems, not LM), which caps scientific_founder in the 'technical founder outside this field' band. Score 7 reflects roughly nine years of real, hands-on technical founding, discounted because none of it is language-model science.","evidence":[{"claim":"Co-author of 'Internet Computer Consensus' (Camenisch, Drijvers, Hanke, Pignolet, Shoup, Williams, 2021), introducing the ICC family of leader-based Byzantine fault-tolerant consensus protocols assuming partial synchrony, with probabilistic leader rotation and optimistic responsiveness","source_url":"https://eprint.iacr.org/2021/632","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 2115924821 lists the 'DFINITY Technology Overview Series, Consensus System' (2018, with T. Hanke and M. Movahedi) among his papers","source_url":"https://api.semanticscholar.org/graph/v1/author/2115924821?fields=name,paperCount,citationCount","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata describes him as an 'Erlang programmer', software developer and entrepreneur, with no academic degree, research affiliation or doctoral record recorded","source_url":"https://www.wikidata.org/wiki/Q115266892","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BSc Computer Science, King's College London (1992-1995); pre-blockchain career founding Smartdrivez and the MMO game Fight My Monster","source_url":"https://usethebitcoin.com/crypto-personalities/all-you-need-to-know-about-dominic-williams-the-co-founder-of-dfinity/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata describes him as an 'Erlang programmer' / software developer and entrepreneur, consistent with a distributed-systems (not AI research) background","source_url":"https://www.wikidata.org/wiki/Q115266892","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-author of 'DFINITY Technology Overview Series, Consensus System' (2018, 319 citations, with T. Hanke and M. Movahedi); Semantic Scholar author 2115924821 records 5 papers and 319 citations","source_url":"https://api.semanticscholar.org/graph/v1/author/2115924821?fields=name,paperCount,citationCount,hIndex,papers.title,papers.year,papers.citationCount,papers.authors","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Williams co-authored 'Internet Computer Consensus' (Camenisch, Drijvers, Hanke, Pignolet, Shoup, Williams), a Byzantine fault-tolerant atomic-broadcast protocol — distributed-systems/cryptography work with no attention/transformer/LM content","source_url":"https://eprint.iacr.org/2021/632","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him as an Erlang programmer, software developer and entrepreneur with no academic degree, research affiliation or LM record — consistent with a technical founder outside the language-modeling field","source_url":"https://www.wikidata.org/wiki/Q115266892","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 2115924821 lists only the two DFINITY consensus papers (2018, 2021) as genuinely his — no embedding, retrieval, transformer, pretraining or alignment work","source_url":"https://api.semanticscholar.org/graph/v1/author/2115924821?fields=name,paperCount,citationCount,papers.title,papers.year","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Williams is co-author of 'Internet Computer Consensus' (Camenisch, Drijvers, Hanke, Pignolet, Shoup, Williams), a Byzantine fault-tolerant atomic-broadcast protocol — distributed-systems/cryptography work, not any component frontier language models build on","source_url":"https://eprint.iacr.org/2021/632","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Williams only as an 'Erlang programmer', software developer and entrepreneur with no LM/AI research affiliation, degree or record — consistent with zero verifiable years in language modeling","source_url":"https://www.wikidata.org/wiki/Q115266892","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Williams founded DFINITY in 2016 and is credibly the principal architect of the Internet Computer's chain-key/consensus design, co-authoring the core papers — a genuine technical-founder role, but in distributed systems/cryptography rather than language modeling","source_url":"https://usethebitcoin.com/crypto-personalities/all-you-need-to-know-about-dominic-williams-the-co-founder-of-dfinity/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.84,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":82},{"id":75,"slug":"sam-blackshear","name":"Sam Blackshear","title":"Co-founder & CTO (former)","company":"Mysten Labs (Sui)","sector":"crypto","profile_url":null,"image_url":null,"score":20,"tier":"narrative_only","dimensions":{"foundations":8,"vector_embeddings":1,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":10,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":20,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Sam Blackshear holds a PhD in Programming Languages from the University of Colorado Boulder and a BA in CS and Philosophy from Williams College; his entire publication record (26 OpenAlex works, h-index 11, topics: software testing/debugging, formal methods in verification, malware detection) is in static analysis, program verification and compositional race detection (e.g. RacerD, PACM PL 2018; Thresher 2013), not in machine learning, statistical learning theory, or optimization for learning. At Meta he was a principal engineer on the Infer/RacerD static-analysis team and then created the Move smart-contract programming language for the Diem/Libra project, later co-founding Mysten Labs and building the Sui blockchain (2021); this is deep, personal, PhD-level systems/languages engineering, but it is compiler/VM/verification infrastructure, not AI model or embedding/representation-learning infrastructure. No evidence in the dossier or in verification searches of any personal research, code, or shipped system involving embeddings, attention, transformers, pretraining, or neural network training. Mysten Labs/Sui's core product is blockchain L1 infrastructure, not an AI/LM system, so industry_impact is capped by the rubric's 'org's CORE is these systems' requirement despite his strong citations/engineering leadership. Per WebSearch, he has since left Mysten Labs to join Anthropic for defensive security research (2026) -- a security, not ML-research, role -- which does not change the AI-core scoring.\n\nBlackshear's body of work — the Move programming language, RacerD/Infer static analysis, program verification and the Sui object-centric L1 — has no lineage into today's frontier language models: none of it concerns attention, transformers, embeddings, optimizers, tokenizers, pretraining objectives, scaling, or alignment, and nothing of his is cited by or built into GPT/Claude/Gemini/Llama technical reports (his 2024 'Collaboration is all you need' is a Move-smart-contract paper, a title pun, not LM research). He has zero verifiable years in natural-language modeling — his record is entirely programming-languages/formal-methods/blockchain, an adjacent CS field, not the vector-space→LSA→neural-LM→transformer lineage. He is, however, a genuine and exemplary scientific/technical founder: he personally created Move at Meta's Diem project and co-founded Mysten Labs (Sept 2021), where the core technology (Move variant + Sui) is his own research and code — roughly 4–5 years operating in that founder-scientist role — but of a blockchain company, not an AI/LM company, so he scores as a strong technical founder outside this field.","evidence":[{"claim":"PhD in Programming Languages, University of Colorado Boulder; BA Computer Science and Philosophy, Williams College; ~6 years at Meta as Principal Engineer leading development of Move before co-founding Mysten Labs","source_url":"https://www.sui.io/blog/move-origins-sam-blackshear","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored 'RacerD: compositional static race detection' (PACM PL / OOPSLA 2018), a compositional static analysis tool for Java built on Facebook's Infer framework -- program analysis/verification, not ML","source_url":"https://research.facebook.com/publications/racerd-compositional-static-race-detection/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar profile corroborates: 17 papers, 665 citations, h-index 13, consistent with the OpenAlex program-analysis/formal-methods focus","source_url":"https://www.semanticscholar.org/author/Sam-Blackshear/1748060","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Mysten Labs (Sept 2021) and built the Sui Layer-1 blockchain, using a variant of Move; recently departed Mysten Labs to join Anthropic for defensive security research","source_url":"https://www.tradingview.com/news/coinpedia:3d1cb34b4094b:0-move-creator-sam-blackshear-leaves-mysten-labs-what-next-for-sui/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv record shows six papers: Resources: A Safe Language Abstraction for Money (2020, with Dill, Qadeer, Barrett, Mitchell), Robust Safety for Move (2021), The Move Borrow Checker (2022), Sui Lutris (2023), Generating Move Smart Contracts based on Concepts (2024), and Collaboration is all you need:","source_url":"http://export.arxiv.org/api/query?search_query=au:%22Blackshear%22&start=0&max_results=30&sortBy=submittedDate&sortOrder=ascending","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Creator of the Move programming language at Meta's Diem project; co-founded Mysten Labs September 2021 with Evan Cheng, Adeniyi Abiodun, George Danezis and Kostas Chalkias; no AI or machine-learning systems documented for Sui or Mysten Labs","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar records 17 papers, 665 citations, h-index 13 under an exact name match","source_url":"https://www.semanticscholar.org/author/1748060","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Mysten Labs in September 2021 and built the Sui Layer-1 blockchain using a variant of the Move language he created — a blockchain/PL company, not an AI/LM system","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Creator of the Move programming language and PhD/PL researcher; entire arXiv/OpenAlex output (RacerD, Move safety, Sui Lutris) is program-analysis and blockchain, with no embeddings/transformer/LM work","source_url":"https://www.sui.io/blog/move-origins-sam-blackshear","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Creator of the Move programming language at Meta's Diem/Libra project and co-founder (Sept 2021) of Mysten Labs, which built the Sui L1 blockchain using a Move variant — a PL/blockchain technical-founder record with no AI/LM systems","source_url":"https://en.wikipedia.org/wiki/Sui_(blockchain_platform)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Publication record (RacerD compositional static race detection, Thresher, Verification modulo versions) is entirely in static analysis and formal verification, not embeddings, transformers or language modeling","source_url":"https://research.facebook.com/publications/racerd-compositional-static-race-detection/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"arXiv papers (Resources: A Safe Language Abstraction for Money 2020, Robust Safety for Move 2021, The Move Borrow Checker 2022, Sui Lutris 2023) confirm a founder-scientist authoring the core research/code his company runs on, all in PL/blockchain rather than the frontier-LM stack","source_url":"https://www.sui.io/blog/move-origins-sam-blackshear","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.86,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":83},{"id":44,"slug":"satya-nadella","name":"Satya Nadella","title":"Chairman & CEO","company":"Microsoft","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Satya_Nadella","image_url":"/api/v1/ceo-ai-leaderboard/portrait/satya-nadella.jpg","score":20,"tier":"narrative_only","dimensions":{"foundations":6,"vector_embeddings":2,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":5,"industry_impact":12,"scientific_founder":1},"rubric_version":3,"weighted_score":20,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Nadella holds a BE in Electrical Engineering (Manipal), an MS in Computer Science (University of Wisconsin-Milwaukee), and an MBA (Chicago Booth) — solid technical education but no PhD, no publications, no AI/ML research record, and no canonical papers of any kind found in Wikidata, OpenAlex, Semantic Scholar, or web search. His entire post-1992 career at Microsoft is executive/managerial: Bing, Server & Tools, Cloud & Enterprise/Azure, then CEO since 2014, where he has directed (not personally authored) Microsoft's massive OpenAI partnership and Copilot rollout. The dossier's OpenAlex 'publications' (Shaping the Fourth Industrial Revolution, Navigating Digital Transformation, Hit Refresh) are business/leadership commentary and a memoir, not technical AI research, and per rubric these do not count as core-AI depth. Patents attributed to him (34, per patent-analytics sites) are typical of a senior tech executive named as co-inventor on business/product patents, not evidence of personal hands-on model-building. Industry impact is scored moderately for directing one of the largest AI deployments in the industry (Azure OpenAI Service, Copilot) even though this is managerial rather than a personal research/engineering record — per rubric, a famous CEO with no personal technical record scores low on the research dimensions specifically.\n\nNothing of Nadella's own authorship — no paper, code, architecture, dataset, optimizer or training method — is part of the lineage that GPT/Claude/Gemini/Llama-class models descend from; he directs Microsoft's OpenAI partnership and Copilot rollout as an executive, not as a contributor to their technical foundation, so frontier_founder is near-zero. He has no verifiable personal record in language modeling (statistical/neural LMs, vector-space text models, seq2seq, transformers, pretraining or alignment): his OpenAlex/Semantic Scholar output is business-strategy commentary and a memoir, giving zero years of hands-on LM work. He is not a founder of Microsoft (he joined in 1992, decades after its 1975 founding) and holds no scientific/technical-founder role authoring core research, code or patents a company runs on — his career is entirely executive/managerial, so scientific_founder scores at the floor.","evidence":[{"claim":"MS in Computer Science, University of Wisconsin-Milwaukee; BE Electrical Engineering, Manipal Institute of Technology; MBA, University of Chicago Booth School of Business","source_url":"https://en.wikipedia.org/wiki/Satya_Nadella","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Joined Microsoft in 1992; held engineering-leadership (not individual-contributor research) roles across Bing, Server & Tools, and Cloud & Enterprise/Azure before becoming CEO in 2014","source_url":"https://www.ebsco.com/research-starters/biography/satya-nadella/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Patent portfolio (~34 patents) is business/systems-oriented, not AI-model research; no AI/ML papers found on Google Scholar, arXiv, or DBLP under his name","source_url":"https://insights.greyb.com/satya-nadella-patents/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BE Electrical Engineering, Manipal Institute of Technology 1988; MS Computer Science, University of Wisconsin-Milwaukee 1990; MBA University of Chicago Booth 1997; member of technology staff at Sun Microsystems before joining Microsoft in 1992; president of Server & Tools Division 2011-2014, then EV","source_url":"https://en.wikipedia.org/wiki/Satya_Nadella","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5067894343 lists only 4 works, 232 citations, h-index 3, all business/strategy items (Hit Refresh, Navigating Digital Transformation, Shaping the Fourth Industrial Revolution, Global maxima through local action); topics are Big Data and Business Intelligence and Business Strategies","source_url":"https://api.openalex.org/authors/A5067894343","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q7426870 records education at Manipal Institute of Technology (Bachelor of Engineering), University of Wisconsin-Milwaukee (MS, computer science) and Booth School of Business (MBA), employers Sun Microsystems and Microsoft from 1992; no doctoral advisor and no Google Scholar ID are recorded","source_url":"https://www.wikidata.org/wiki/Q7426870","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Nadella joined Microsoft in 1992 and rose through executive/managerial roles (Bing, Server & Tools, Cloud & Enterprise/Azure) to CEO in 2014; he is not a founder of Microsoft and has no individual-contributor research record","source_url":"https://en.wikipedia.org/wiki/Satya_Nadella","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5067894343 lists only 4 works (Hit Refresh, Navigating Digital Transformation, Shaping the Fourth Industrial Revolution, Global maxima through local action) — business/strategy items, no language-modeling or transformer research","source_url":"https://api.openalex.org/authors/A5067894343","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q7426870 records education and Microsoft/Sun employment but no doctoral advisor, no Google Scholar ID, and no founder or chief-scientist position","source_url":"https://www.wikidata.org/wiki/Q7426870","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5067894343 lists only 4 works (Hit Refresh memoir, 'Navigating Digital Transformation', 'Shaping the Fourth Industrial Revolution', 'Global maxima through local action'), all business/strategy items — no AI/ML, attention, transformer, embedding or scaling paper the frontier stack ci","source_url":"https://api.openalex.org/authors/A5067894343","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q7426870 records Nadella as an employee of Sun Microsystems and Microsoft (from 1992) and CEO from 2014, with no founder position and no doctoral/research advisor — no technical-founder role exists in the record","source_url":"https://www.wikidata.org/wiki/Q7426870","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikipedia describes Nadella's entire Microsoft career as engineering-leadership and executive roles (Bing, Server & Tools, Cloud & Enterprise/Azure, then CEO) — directing platforms, not personally authoring language-modeling research","source_url":"https://en.wikipedia.org/wiki/Satya_Nadella","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.89,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":84},{"id":70,"slug":"anna-kazlauskas","name":"Anna Kazlauskas","title":"Co-founder & CEO","company":"Vana (Open Data Labs)","sector":"crypto","profile_url":null,"image_url":null,"score":19,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":3,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":7,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":19,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Kazlauskas is not a co-founder of Gensyn (the task context's suggestion appears mistaken) — she is the co-founder/CEO of Vana, a data-ownership/DePIN protocol for AI training data, and this is corroborated across multiple independent sources (MIT News, Crunchbase, podcast profiles). She studied computer science and economics at MIT (no evidence of a completed degree or thesis found — sources describe her as a student/dropout rather than a graduate) and ran a YC-backed (W18) fintech/document-automation ML startup, Iambiq, and later worked as an early engineer at the Celo Foundation adapting the Celo blockchain for mobile. No peer-reviewed papers, patents, or canonical AI research were found under her name; the sole OpenAlex-listed work is a 2026 SSRN research-agenda essay ('The Economics of AI Training Data') with zero citations, which is a position paper, not primary technical research. Her verifiable technical record is real but shallow — production engineering on a blockchain (Celo) and a small ML-for-documents startup — rather than personal contributions to embeddings, attention, or transformer/LM research; Vana's core protocol design (data attestation, proof-of-contribution) is infrastructure/tokenomics work adjacent to AI data pipelines rather than model research itself.\n\nNothing of Kazlauskas's authorship enters the frontier-model foundation: no architecture, attention, embedding, optimizer, dataset or benchmark of hers is cited or built into GPT/Claude/Gemini/Llama technical reports — her sole indexed work is a 2026 SSRN research-agenda essay ('The Economics of AI Training Data') with zero citations, and Vana is user-owned-data / DePIN infrastructure and tokenomics adjacent to training-data pipelines, not language modeling. She has no verifiable language-modeling record at all (statistical/neural LMs, seq2seq, transformers, pretraining or alignment): her hands-on work is blockchain engineering at Celo and an ML document-automation startup (Iambiq, YC W18), so LM domain depth is near-zero. She is, however, a genuine technical founder-CEO with a CS background who set technical direction across roughly eight years (Iambiq 2018 → Vana ~2021–present), which places her in the 'technical founder outside this field' band rather than the LM-core band.","evidence":[{"claim":"Anna Kazlauskas is co-founder and CEO of Vana (Open Data Labs), a protocol for user-owned AI training data — not affiliated with Gensyn","source_url":"https://news.mit.edu/2025/vana-lets-users-own-piece-ai-models-trained-on-their-data-0403","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Before Vana she was an early engineer at the Celo Foundation, working on adapting the Celo blockchain for mobile devices","source_url":"https://nocap.blog/founder/anna-kazlauskas/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"MIT class of 2019; joined the MIT Bitcoin Club in 2015; met co-founder Art Abal in the Media Lab class 'Emergent Ventures' taught by Ramesh Raskar, who still advises Vana on AI research; worked at Celo before founding Vana; Vana uses data DAOs so users pool exported personal data and receive proport","source_url":"https://news.mit.edu/2025/vana-lets-users-own-piece-ai-models-trained-on-their-data-0403","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Vana's documentation describes it as an open protocol for private, user-owned data with encrypted storage, on-chain permission management and a local Personal Server; DataDAOs are an optional application layer, not the core.","source_url":"https://docs.vana.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Vana is an open protocol for private, user-owned data (encrypted storage, on-chain permissioning, DataDAOs) — data-ownership infrastructure for AI training data, not language-model research","source_url":"https://docs.vana.org/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kazlauskas co-founded and is CEO of Vana; her prior technical work was early engineering at the Celo Foundation and a YC-backed ML startup, with no language-modeling research record","source_url":"https://news.mit.edu/2025/vana-lets-users-own-piece-ai-models-trained-on-their-data-0403","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kazlauskas is co-founder and CEO of Vana, a protocol for user-owned AI training data (data-ownership/DePIN infrastructure), not an author of frontier-model architecture or training methods","source_url":"https://news.mit.edu/2025/vana-lets-users-own-piece-ai-models-trained-on-their-data-0403","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Her hands-on record is early engineering at the Celo Foundation (mobile blockchain) plus the YC W18 ML-document startup Iambiq — general ML and crypto, with no language-modeling research","source_url":"https://nocap.blog/founder/anna-kazlauskas/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.64,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":85},{"id":92,"slug":"anatoly-yakovenko","name":"Anatoly Yakovenko","title":"Co-Founder & CEO","company":"Solana Labs","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","image_url":"/api/v1/ceo-ai-leaderboard/portrait/anatoly-yakovenko.jpg","score":18,"tier":"narrative_only","dimensions":{"foundations":6,"vector_embeddings":1,"transformers_lm":1,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":10,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":18,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Yakovenko holds a BS in computer science from UIUC and spent roughly a decade at Qualcomm as an engineer on wireless and distributed systems, followed by roles at Mesosphere and Dropbox — a real, verifiable, hands-on distributed-systems engineering record. He personally authored the Solana whitepaper and the 'Proof of History' technical paper (2017-2018), and Solana's consensus design is his own engineering work, not a business-only role, which supports hands_on_engineering and foundations (distributed-systems math/algorithms) scores. However none of this touches the rubric's core: there is no vector-embeddings work, no seq2seq/attention/transformer/language-model authorship or training, and no OpenAlex/arXiv record in the AI research lineage — Solana is a blockchain consensus protocol, not an AI system. industry_impact reflects a real, large engineering organization (Solana Labs) but its product is not core-AI infrastructure per the rubric's definition, so it is scored moderately for general technical leadership rather than AI industry impact.\n\nNone of Yakovenko's work is part of the foundation that today's frontier AI models build on: his verifiable output is the Solana whitepaper and the 'Proof of History' consensus design (2017-2018), a blockchain-throughput mechanism cited by no LLM technical report — frontier_founder is essentially nil. He has no verifiable record in language modeling of any era (no vector-space/LSI, n-gram, neural-LM, seq2seq or transformer work; the sole computing paper under his Semantic Scholar id is the Solana architecture paper, the rest being a metallurgy homonym), so lm_domain_depth is nil. He is, however, a genuine scientific/technical founder — he personally authored the core papers and consensus design Solana Labs runs on and co-founded it in 2017 (~8-9 years) — but that company's core is a distributed-ledger protocol, not the AI/language-modeling systems this dimension measures, which caps scientific_founder in the 'technical founder outside this field' band.","evidence":[{"claim":"BS in computer science, University of Illinois Urbana-Champaign; immigrated from Ukraine as a child","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Spent over a decade at Qualcomm as an engineer on wireless and distributed systems before later roles at Mesosphere and Dropbox","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Authored 'Solana: A new architecture for a high performance blockchain' and the 'Proof of History: A Clock for Blockchain' whitepaper, and co-founded Solana Labs starting 2017","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar match (A. Yakovenko, 20 papers, 279 citations) is name_exact:false with 3 candidates — homonym risk not independently resolved to this Yakovenko; not relied on for AI-lineage claims","source_url":"https://www.semanticscholar.org/author/114558484","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BS in computer science, University of Illinois Urbana-Champaign; more than a decade at Qualcomm as an engineer on wireless and distributed systems; engineering roles at Mesosphere and Dropbox from 2016; developed Proof of History in 2017 and co-founded Solana Labs; authored 'Solana: A new architectu","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 114558484 lists 'Solana: A new architecture for a high performance blockchain v0.8' (2018) as his sole computing paper; the remaining ~19 works under that id are 1980-2011 Russian-language metallurgy papers (blast-furnace stoves, lime kilns, steel-teeming ladles) by a differe","source_url":"https://api.semanticscholar.org/graph/v1/author/114558484/papers?fields=title,year,venue,authors","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata identifies him only as 'Co-Founder - Solana', with no recorded doctorate, advisor, research occupation or publication identifiers","source_url":"https://www.wikidata.org/wiki/Q115947586","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Yakovenko authored the Solana whitepaper and developed the Proof of History consensus mechanism in 2017 and co-founded Solana Labs — a blockchain, not an AI/language-modeling system, and not cited by frontier LLM technical reports","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author 114558484 lists only the Solana architecture paper as his computing work; the remaining ~19 items are unrelated Russian-language metallurgy papers by a homonym — no AI/language-modeling authorship","source_url":"https://www.semanticscholar.org/author/114558484","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records him solely as 'Co-Founder - Solana' with no doctorate, research occupation or publication identifiers in the AI lineage","source_url":"https://www.wikidata.org/wiki/Q115947586","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Yakovenko authored 'Solana: A new architecture for a high performance blockchain' and the 'Proof of History' whitepaper and co-founded Solana Labs in 2017 — a distributed-systems/blockchain consensus contribution, with no AI/LM research in any source","source_url":"https://en.wikipedia.org/wiki/Anatoly_Yakovenko","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata identifies him solely as 'Co-Founder - Solana' with no doctorate, research occupation or publication identifiers; the sole Semantic Scholar computing paper under his id is the Solana whitepaper, the rest being homonym metallurgy papers","source_url":"https://www.wikidata.org/wiki/Q115947586","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.77,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":86},{"id":102,"slug":"daniel-gross","name":"Daniel Gross","title":"Co-founder (former, June 2024-July 2025); investor/operator","company":"Safe Superintelligence Inc. (formerly); Meta Superintelligence Labs (from July 2025)","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(entrepreneur)","image_url":"https://upload.wikimedia.org/wikipedia/commons/thumb/9/97/TechCrunch_Disrupt_San_Francisco_2018_-_day_2_%2843613758885%29.jpg/330px-TechCrunch_Disrupt_San_Francisco_2018_-_day_2_%2843613758885%29.jpg","score":18,"tier":"narrative_only","dimensions":{"foundations":2,"vector_embeddings":5,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":6,"industry_impact":6,"scientific_founder":5},"rubric_version":3,"weighted_score":18,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Gross has no degree, thesis, or authored paper in linear algebra, optimization, or statistical learning that any source corroborates (Wikidata's only 'educated_at' entry is a pre-military prep academy in Israel, not a university; no evidence of a CS/ML PhD or dropout-from-Stanford narrative could be verified). His one concrete technical artifact is Greplin/Cue (founded 2010, launched at 19), a product letting users search across email, social media and cloud-storage accounts from one place with 'predictive search features' added in 2012 - this is consumer full-text/cross-account search and light personalization, not documented vector-space, embedding, or LM research, so it earns modest vector_embeddings/hands_on_engineering credit for shipping a real search system, not for research depth. After Apple acquired Cue in 2013 (Apple then shut Cue down), Gross became 'a director focused on machine learning' at Apple - a leadership/management title with no corroborated description of him personally building models or infrastructure. His subsequent record (YC partner running the YC AI program 2017+, prolific angel investor in Uber/GitHub/Figma/Perplexity/CoreWeave, co-deploying the Andromeda Cluster of 2,512 H100 GPUs for startups with Nat Friedman) is investing/infrastructure-provisioning, which the rubric explicitly excludes from credit. He co-founded SSI with Ilya Sutskever and Daniel Levy in June 2024 but left after about a year (July 2025) for Meta Superintelligence Labs; no source found describes his specific day-to-day role at SSI as research/engineering versus operations, fundraising, or recruiting, so industry_impact credit reflects only being an early co-founder of a lab whose stated mission is building safe superintelligence, not personal technical leadership of its research. Semantic Scholar's 17-paper/h-index-5 match and the PubMed entries (yeast transcription/genome biology by 'Gross DS', viral immunology by 'Gross DA') are unrelated homonyms with no overlap in co-authors, venue, or subject matter and are excluded entirely.\n\nNothing of Gross's own work sits in the lineage today's frontier models descend from: no authored architecture, attention/embedding method, optimizer, tokenizer, dataset, scaling result or alignment technique, and no paper cited by any frontier technical report (OpenAlex/arXiv cs.LG return nothing; Semantic Scholar and PubMed hits are confirmed homonyms). His one built system, Greplin/Cue (2010–2013), was consumer cross-account full-text search with light personalization, not language modeling — so his verifiable LM-domain record is essentially nil, adjacent at best, well under the 3-year floor for continuous personal LM work. He was a genuine young technical/product founder of Cue for roughly three years, but that work was outside the LM core; at SSI (co-founded June 2024, left July 2025) the science belongs to Ilya Sutskever and Daniel Levy, and no source documents Gross personally authoring core research, code or patents there — placing scientific_founder in the 'technical founder outside this field / AI-company founder whose science was done by others' band.","evidence":[{"claim":"Gross launched Greplin (later renamed Cue) in 2010 at age 19; it let users search online accounts (social media, email, cloud storage) from one place, with predictive search features added in 2012; Apple acquired Cue in 2013 for a reported $40-60M and shut it down shortly after.","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"After the acquisition, Gross joined Apple as a director focused on machine learning; in 2017 he joined Y Combinator as a partner and created the 'YC AI' program; he is described as a notable technology investor (Uber, Instacart, Figma, GitHub, Airtable, Rippling, CoreWeave, Character.ai, Perplexity","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gross and Nat Friedman deployed the Andromeda Cluster, a supercomputer cluster of 2,512 H100 GPUs for startup use.","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"In June 2024 Gross co-founded Safe Superintelligence Inc. with Ilya Sutskever and Daniel Levy; in July 2025 Gross left SSI to join Meta Superintelligence Labs.","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Safe Superintelligence Inc.'s stated mission is 'Building safe superintelligence (SSI) is the most important technical problem of our time' and it is presented as the company's sole focus, though the SSI website itself lists no founder biographical detail confirming Gross's specific technical role.","source_url":"https://ssi.inc","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No university degrees documented; co-founded Greplin 2010 (rebranded Cue 2012, a unified search product with predictive search), acquired by Apple 2013; became a director focused on machine learning at Apple; joined Y Combinator as partner 2017 and created the YC AI program; co-founded Safe Superint","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"An arXiv author search for 'Daniel Gross' restricted to cs.LG returns zero results","source_url":"http://export.arxiv.org/api/query?search_query=au:%22Daniel_Gross%22+AND+cat:cs.LG&start=0&max_results=20","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records occupation 'businessperson', education limited to Bnei David Mechina, employer Cue, and notable work Cue — no academic degree, affiliation or research output","source_url":"https://www.wikidata.org/wiki/Q19364797","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Gross co-founded Greplin/Cue in 2010 (cross-account personal search), acquired by Apple 2013; became a director focused on machine learning at Apple; co-founded SSI with Ilya Sutskever and Daniel Levy June 2024 and left for Meta July 2025 — no authored papers or research artifacts in the AI core.","source_url":"https://en.wikipedia.org/wiki/Daniel_Gross_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"An arXiv author search for 'Daniel Gross' restricted to cs.LG returns zero results, and Wikidata records occupation 'businessperson' with no academic degree, affiliation or research output.","source_url":"https://www.wikidata.org/wiki/Q19364797","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"An arXiv cs.LG author search for 'Daniel Gross' returns zero results, and Wikidata records occupation 'businessperson' with no academic degree, affiliation or research output — no authored LM or transformer lineage work.","source_url":"https://www.wikidata.org/wiki/Q19364797","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.69,"source":"community","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":87},{"id":69,"slug":"sandeep-nailwal","name":"Sandeep Nailwal","title":"Co-founder, Polygon; co-founder, Sentient","company":"Polygon / Sentient","sector":"crypto","profile_url":null,"image_url":"/api/v1/ceo-ai-leaderboard/portrait/sandeep-nailwal.jpg","score":18,"tier":"narrative_only","dimensions":{"foundations":3,"vector_embeddings":2,"transformers_lm":4,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":7,"industry_impact":5,"scientific_founder":5},"rubric_version":3,"weighted_score":18,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Nailwal is a blockchain engineer and founder, not an AI researcher. His verifiable technical record is Ethereum scaling infrastructure: he co-founded Matic Network, later Polygon, in 2017 with Jaynti Kanani, Anurag Arjun and Mihailo Bjelic, all described in the sources only as software engineers, with no biographical or educational detail confirmable from any primary source. His single indexed publication is 'OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution' (arXiv:2411.03887, 2024), and I verified the author list directly: he is ninth of twelve, on a paper whose senior names are Sewoong Oh, Himanshu Tyagi and Pramod Viswanath. I also confirmed the paper's subject matter from the abstract — it introduces a primitive for cryptographically enforced usage authorization of freely distributed models, with security definitions for model-extraction and permission-forgery resistance implemented via fingerprinting and crypto-economic enforcement. That is security and mechanism design about distributing models, not work on the models themselves: no architecture, training, embedding or language-modelling contribution. The paper has zero citations, it is his only one, and it dates to 2024, giving him two years in this lineage. Sentient, which he co-founded, does release open models, but nothing in the verifiable record shows him personally designing, training or authoring language-model work, so the research dimensions sit in the 3-7 'uses the tools, manages builders' band, with engineering credited slightly higher for real protocol-level building at Polygon that lies outside the AI core.\n\nNothing of Nailwal's own work sits in the foundation that GPT/Claude/Gemini/Llama-class models are built on: his single paper, OML (arXiv:2411.03887, 2024), is a crypto-economic model-distribution/licensing primitive with zero citations, not an architecture, attention, embedding, optimizer, tokenizer, pretraining or scaling contribution the frontier stack descends from — frontier_founder is near the floor. His verifiable language-modeling record is that one 2024 co-authorship (ninth of twelve authors), about fingerprinting and usage authorization rather than the models themselves, giving under two years of adjacent — not core — LM exposure, so lm_domain_depth is minimal. He is a genuine technical co-founder, but of Polygon (Matic Network, 2017 → present, ~9 years of shipped Ethereum-scaling protocol engineering), a field OUTSIDE language modeling; at Sentient he co-founded an AI venture but no primary source shows him personally authoring its core research, so scientific_founder lands in the 3-7 'technical founder outside this field / AI company whose science is done by others' band.","evidence":[{"claim":"'OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution', arXiv:2411.03887 (2024); Nailwal is ninth of twelve authors, after Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi and Anshul Nasery, and before Sewoong O","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The OML paper addresses cryptographically enforced usage authorization for locally executed models — security definitions for model-extraction and permission-forgery resistance via AI-native fingerprinting and crypto-economic enforcement — not model training or architecture","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Polygon was founded as Matic Network in 2017 by Jaynti Kanani, Sandeep Nailwal, Mihailo Bjelic and Anurag Arjun, described as software engineers; it is an Ethereum-compatible proof-of-stake scaling platform","source_url":"https://en.wikipedia.org/wiki/Polygon_(blockchain)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OpenAlex author A5114645762: 1 work, 0 citations, h-index 0, earliest year 2024","source_url":"https://api.openalex.org/authors/A5114645762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sandeep Nailwal is one of 12 co-authors on 'OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution' (arXiv, 2024), alongside academics including Sewoong Oh and Andrew Miller.","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sandeep Nailwal co-founded Polygon (originally Matic Network) in 2017 with Jaynti Kanani, Anurag Arjun, and Mihailo Bjelic; the founders are described generically as software engineers, with no AI/ML focus — Polygon's core is Ethereum-compatible blockchain scaling.","source_url":"https://en.wikipedia.org/wiki/Polygon_(blockchain)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Sentient Labs (the organization behind the OML paper) is a real open-source AI reasoning research lab publishing at NeurIPS, ICML, and COLM, and OML is one of its shipped systems.","source_url":"https://sentient.xyz","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution, arXiv 2411.03887 (2024); Nailwal is ninth of twelve authors, paper led by Cheng, Contente, ... Oh, Tyagi, Viswanath","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Polygon founded as Matic Network in 2017 by Jaynti Kanani, Sandeep Nailwal, Mihailo Bjelic and Anurag Arjun, described as software engineers; no educational or biographical detail given","source_url":"https://en.wikipedia.org/wiki/Polygon_(blockchain)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Nailwal co-founded Polygon (originally Matic Network) in 2017 as one of its software-engineer founders; Polygon's core is Ethereum-compatible PoS scaling, not AI/language modeling","source_url":"https://en.wikipedia.org/wiki/Polygon_(blockchain)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"OML (arXiv:2411.03887, 2024) is a primitive for reconciling open access with owner control in AI model distribution — cryptographic usage-authorization and fingerprinting, not model architecture/training/embeddings; Nailwal is ninth of twelve authors and the paper has zero citations","source_url":"https://arxiv.org/abs/2411.03887","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Nailwal co-founded Polygon (Matic Network) in 2017 with Jaynti Kanani, Anurag Arjun and Mihailo Bjelic as software engineers building Ethereum-compatible PoS scaling — a technical-founder role, but in blockchain, outside the AI/language-modeling core","source_url":"https://en.wikipedia.org/wiki/Polygon_(blockchain)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.83,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":88},{"id":46,"slug":"sundar-pichai","name":"Sundar Pichai","title":"CEO of Alphabet Inc. and Google","company":"Alphabet & Google","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","image_url":"/api/v1/ceo-ai-leaderboard/portrait/sundar-pichai.jpg","score":18,"tier":"narrative_only","dimensions":{"foundations":5,"vector_embeddings":2,"transformers_lm":4,"frontier_founder":1,"lm_domain_depth":2,"hands_on_engineering":4,"industry_impact":11,"scientific_founder":2},"rubric_version":3,"weighted_score":18,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Pichai holds a B.Tech in metallurgical engineering from IIT Kharagpur, an MS in materials science and engineering from Stanford, and an MBA from Wharton — engineering-adjacent graduate training but not in CS, ML, or applied mathematics, and no doctoral or thesis-level research record of any kind. His Google career (Toolbar, Chrome, Chrome OS, Google Drive, Gmail/Maps oversight, Android, then CEO) is documented as product management and executive leadership, not individual-contributor engineering or research; no personally authored papers, no patents, and no Google Scholar profile were found. The dossier's OpenAlex match (4 works, all 2019-2021 congressional-testimony reprints and a Fox Business interview) is not scientific authorship and is excluded from scoring; the PubMed 'Pichai S' records (dental/orthodontic finite-element papers, hepatitis seroprevalence, orthopedic surgery, all India-affiliated, 2012-2025) are a clear homonym — a different person entirely — and are also excluded. Industry_impact is scored moderately: as CEO he leads the organization that ships Gemini, TensorFlow, and Search/Ads infrastructure and that employed the original Transformer authors and Google DeepMind, but this credits organizational leadership of a company whose core is these systems, not personal authorship of the underlying research (Attention Is All You Need, BERT, etc. were built by named researchers, not Pichai). All core research dimensions (foundations, vector_embeddings, transformers_lm, hands_on_engineering) reflect the complete absence of a personal, verifiable technical/research record per the rubric's explicit instruction that fame and company branding do not count.\n\nNothing of Pichai's own authorship sits in the frontier lineage: the Transformer, BERT, T5, word2vec and the scaling/alignment methods GPT/Claude/Gemini/Llama descend from were authored by named Google/DeepMind researchers (Vaswani et al., Mikolov et al.), never by Pichai, who has no papers, patents or code in the record. He has zero verifiable years of personal language-modeling research — his career (Toolbar, Chrome, ChromeOS, Android, then Google/Alphabet CEO) is product management and executive leadership, and as CEO he oversees Gemini/DeepMind/TensorFlow as an organizational leader, not a first-principles LM practitioner. He is not a founder of Google (Page and Brin founded it in 1998); he is a professional CEO of a company whose AI science and engineering are done by others, so he earns only the low 'CEO of an AI company, science by others' band and zero years as a technical founder.","evidence":[{"claim":"B.Tech in metallurgical engineering, IIT Kharagpur; MS materials science and engineering, Stanford University; MBA, Wharton School (Siebel Scholar, Palmer Scholar)","source_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata confirms MS from Stanford (materials science field), MBA from Wharton (business management field), employer Google from 2004, CEO of Google 2015 and Alphabet 2019","source_url":"https://www.wikidata.org/wiki/Q3503829","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Career at Google was product management and leadership across Chrome, Chrome OS, Google Drive, Gmail/Maps oversight, and Android, not individual-contributor engineering or research; no research papers, patents, or hands-on AI/ML engineering documented","source_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PubMed 'Pichai S[Author]' records (17 results, 2012-2025) are dental/orthodontic, hepatitis, and orthopedic-surgery papers by India-affiliated co-authors (Vetriselvan A, Peddu R, Bose VC, etc.) — a different person, not Google's Sundar Pichai","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Pichai+S%5BAuthor%5D","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"B.Tech metallurgical engineering IIT Kharagpur (1993), MS materials science and engineering Stanford, MBA Wharton; engineering/product roles at Applied Materials then McKinsey; joined Google 2004 as a product manager over Chrome, ChromeOS, Drive, Gmail, Maps; added Android 2013; CEO of Google 2015 a","source_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education limited to Stanford (MS, materials science) and Wharton (MBA), with employers Google (from 2004) and Alphabet (from 2019) and no doctorate or academic affiliation","source_url":"https://www.wikidata.org/wiki/Q3503829","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The transformer paper that anchors this lineage was authored by Google Brain and Google Research staff (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin), not by Pichai","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Attention Is All You Need was authored by Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser and Polosukhin — Google Brain/Research staff, not Pichai","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Pichai's degrees are metallurgical engineering (IIT Kharagpur), materials science (Stanford MS) and an MBA (Wharton), with a Google career in product management and executive leadership — no research papers or language-modeling record","source_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Google was founded by Larry Page and Sergey Brin in 1998; Pichai joined in 2004 as a product manager and became CEO in 2015 (Alphabet 2019), i.e. a hired executive, not a scientific/technical founder","source_url":"https://en.wikipedia.org/wiki/Google","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The transformer paper that anchors the frontier lineage was authored by Google Brain/Research staff (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin), not Pichai","source_url":"https://arxiv.org/abs/1706.03762","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Pichai joined Google in 2004 as a product manager and rose to CEO (2015) and Alphabet CEO (2019); he is an employee-turned-executive, not a founder, with no personal research or patents","source_url":"https://en.wikipedia.org/wiki/Sundar_Pichai","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education limited to Stanford (MS materials science) and Wharton (MBA) and employers Google (from 2004) and Alphabet (from 2019) — no doctorate, no academic affiliation, no founder role","source_url":"https://www.wikidata.org/wiki/Q3503829","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.89,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":89},{"id":47,"slug":"elon-musk","name":"Elon Musk","title":"Founder & CEO, xAI; CEO, Tesla and SpaceX","company":"xAI, Tesla, SpaceX","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Elon_Musk","image_url":"/api/v1/ceo-ai-leaderboard/portrait/elon-musk.jpg","score":17,"tier":"narrative_only","dimensions":{"foundations":3,"vector_embeddings":1,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":5,"industry_impact":10,"scientific_founder":5},"rubric_version":3,"weighted_score":17,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Musk holds a BA in physics and a BS in economics from the University of Pennsylvania (1997) and never enrolled in the Stanford graduate programme he was admitted to in 1995, so there is no thesis, graduate coursework or publication record in linear algebra, optimization or statistical learning behind him. His entire indexed scholarly output is a single 2019 Neuralink white paper, 'An Integrated Brain-Machine Interface Platform With Thousands of Channels' (JMIR, doi 10.2196/16194) — electrode arrays and signal acquisition hardware, published under Neuralink's collective authorship convention for its founder-CEO, and neuroscience hardware rather than machine learning in any case; his OpenAlex profile's five works are duplicate versions of that one document plus two 2025 Zenodo preprints of uncertain provenance that neither pass could attribute to him. I found no paper, preprint, patent as inventor or public code by him on embeddings, attention, transformers, pretraining, scaling or alignment; on this rubric's spine his personal record is effectively empty. His association with the lineage is as founder and funder — OpenAI co-founder in 2015, departing the board in 2018, and xAI founder and CEO from 2023 — with no documented hands-on research role at either; Grok was built by researchers and engineers hired from Google, DeepMind and OpenAI. Hands-on engineering earns a little more than the research axes but not much: his documented personal coding is Zip2-era web software in the 1990s, and his technical role at Tesla and SpaceX is documented as detailed engineering direction rather than personally designing the Dojo training stack or the FSD networks. Industry impact is genuine on the rubric's terms because Tesla's autonomy programme and xAI's Grok have machine learning at their core and he built those organizations, but it is organizational impact with no citation, inventor-patent or canonical-paper record underneath it; his net worth, fame and 'AI company' branding are excluded entirely.\n\nNo architecture, attention/embedding method, optimizer, tokenizer, dataset, scaling result or alignment technique in the transformer→LLM lineage is attributable to Musk — his sole substantive indexed paper is the 2019 Neuralink brain-machine-interface white paper (neuroscience hardware, not language modeling), so nothing of his is built into or cited by GPT/Claude/Gemini/Grok-class systems (frontier_founder 2). He has zero verifiable years of personal, continuous language-modeling research or systems work: founding OpenAI as a funder/board member (2015–2018) and xAI as CEO (2023–present) is organizational, and Grok was built by researchers hired from Google/DeepMind/OpenAI, not by him (lm_domain_depth 2). He operates as founder-CEO of xAI and Tesla — AI companies whose core science and engineering were done by others rather than authored by him in core code, papers or inventor-patents — which is precisely the rubric's 3–7 'founder/CEO of an AI company whose science was done by others' band, so scientific_founder is a mid 5, not the technical-founder tier.","evidence":[{"claim":"BA in physics and BS in economics, University of Pennsylvania (1997); admitted to a Stanford materials-science graduate programme in 1995 but never enrolled; co-founded OpenAI in December 2015 as funder and board member and departed the board in 2018; founded xAI in 2023","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"His entire OpenAlex record (A5026992422) is 5 works with h-index 3, comprising duplicate versions of the 2019 Neuralink brain-machine-interface paper plus two 2025 Zenodo preprints with 0 citations each","source_url":"https://api.openalex.org/authors/A5026992422","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"'An Integrated Brain-Machine Interface Platform With Thousands of Channels', J Med Internet Res 2019;21(10):e16194 — his sole substantive indexed publication, describing electrode and signal-acquisition hardware under Neuralink's collective authorship","source_url":"https://doi.org/10.2196/16194","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar author record shows 2 papers and 0 citations, confirming no independent research corpus","source_url":"https://www.semanticscholar.org/author/Elon-Musk/2064796567","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BA Physics, BS Economics, University of Pennsylvania (1997); no completed graduate degree (admitted to Stanford materials science PhD 1995, did not enroll)","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded OpenAI in Dec 2015 as funder/board member, pledged $1B but donated far less, departed board 2018; no documented hands-on research role","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded xAI (2023) as CEO; built Grok by hiring researchers/engineers from Google and OpenAI rather than personally engineering the models","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"BA physics and BS economics, University of Pennsylvania 1997; accepted to Stanford materials-science graduate programme but did not enrol; co-founded OpenAI 2015 and left its board 2018; launched xAI July 2023; father contributed 10% of a later Zip2 funding round","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Entire OpenAlex record for Elon Musk is 5 works / h-index 3, all versions of the 2019 Neuralink brain-machine-interface paper plus two 2025 Zenodo items; topics are brain-computer interfaces and neural engineering, not machine learning","source_url":"https://api.openalex.org/authors/A5026992422","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Entire OpenAlex record (A5026992422) is 5 works / h-index 3, all versions of the 2019 Neuralink brain-machine-interface paper plus two 2025 Zenodo preprints — topics are brain-computer interfaces and neural engineering, not language modeling; no embeddings/attention/transformer/pretraining/scaling/a","source_url":"https://api.openalex.org/authors/A5026992422","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded and funded OpenAI (Dec 2015), left its board in 2018 with no documented hands-on research role; founded xAI in 2023 as CEO and built Grok by hiring researchers/engineers from Google, DeepMind and OpenAI rather than personally engineering the models","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Entire OpenAlex record (A5026992422) reduces to versions of one 2019 Neuralink brain-machine-interface paper plus two unattributable 2025 Zenodo preprints; topics are brain-computer interfaces and neural engineering, with no work on embeddings, attention, transformers, pretraining, scaling or alignm","source_url":"https://api.openalex.org/authors/A5026992422","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Musk co-founded OpenAI in Dec 2015 as funder/board member and left the board in 2018, and founded xAI in 2023 as CEO; there is no documented hands-on research role and Grok was built by hired researchers/engineers","source_url":"https://en.wikipedia.org/wiki/Elon_Musk","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.9,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":90},{"id":83,"slug":"emad-mostaque","name":"Emad Mostaque","title":"Founder (former Co-founder & CEO, Stability AI)","company":"Stability AI / Intelligent Internet","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","image_url":"/api/v1/ceo-ai-leaderboard/portrait/emad-mostaque.jpg","score":17,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":2,"transformers_lm":3,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":4,"industry_impact":8,"scientific_founder":4},"rubric_version":3,"weighted_score":17,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Mostaque holds an MA in mathematics and computer science from Oxford but his career before Stability AI was hedge-fund management (crude oil trading) and geopolitical consulting, with no personal record of AI/ML research, code, or publications prior to 2022 — the OpenFold paper (Nature Methods, 2024; bioRxiv preprint 2022) lists him among 28 authors from Stability AI/Columbia/OpenFold-consortium, but no author-contributions detail surfaces any technical (modeling/coding/math) role for him specifically, consistent with his documented pattern as a funder/resource-provider rather than a hands-on researcher. Stable Diffusion itself was developed by Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz (CompVis/LMU Munich, building on their earlier latent-diffusion research), with Stability AI providing compute and organizational backing — Mostaque was not a co-inventor of the diffusion/transformer techniques involved. A June 2023 investigative report (30+ sources) found he had misrepresented his educational background and overstated his personal involvement in Stable Diffusion's development, and a former co-founder sued him alleging fraud in a stock buyback; these findings directly bear on the reliability of his own self-claims and support scoring his research/engineering dimensions on verified record only, which is thin. Industry_impact reflects that he did found and lead (2020-2024) the organization that funded and released Stable Diffusion, a genuinely significant open-weights system, even though his personal technical contribution to its science is not established.\n\nNo method, architecture, dataset or objective authored by Mostaque personally sits in the frontier LLM lineage — Stable Diffusion's latent-diffusion architecture was authored by the CompVis/LMU team (Rombach, Blattmann, Esser, Lorenz) with Stability AI supplying compute and funding, and the OpenFold paper (protein structure, not language modeling) lists him among 28 authors with no established technical role, so frontier_founder scores as applies/funds rather than builds. He has no verifiable personal language-modeling research record at all — his pre-2022 career was hedge-fund oil trading and geopolitical consulting, and even at Stability AI (LLM efforts such as StableLM) the modeling was done by employed researchers — so lm_domain_depth is adjacent-at-best under three years of personal record. As founder-CEO of Stability AI (2019–2024, ~5 years) he set organizational direction but the science and engineering were done by others, and a June 2023 Forbes investigation citing 30+ sources found he overstated his personal involvement in developing Stable Diffusion; that places him at the 'founder/CEO of an AI company whose science was done by others' band, not a technical/scientific founder who wrote the core research the company runs on.","evidence":[{"claim":"MA mathematics and computer science, University of Oxford; pre-Stability AI career was hedge fund management (crude oil) and geopolitical consulting; no documented AI research background before founding Stability AI in 2019/2020","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of 28 authors (affiliation: Stability AI) on the OpenFold paper (Nature Methods, 2024) and its 2022 bioRxiv preprint, alongside the actual OpenFold/AlphaFold-retraining research team; no contribution statement in the fetched source specifies his individual technical role","source_url":"https://www.biorxiv.org/content/10.1101/2022.11.20.517210v1","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"June 2023 investigation citing 30+ sources (investors, former employees) found Mostaque misled investors and the public about his educational background, an AWS partnership, and the extent of his personal involvement in developing Stable Diffusion","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stable Diffusion was developed by Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz (university researchers), with Stability AI providing computational resources rather than the core research","source_url":"https://en.wikipedia.org/wiki/Stability_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder Cyrus Hodes sued Mostaque in July 2023 alleging he was fraudulently induced to sell his 15% Stability AI stake for $100 (across two 2021-2022 transactions) shortly before a $1B-valuation raise made it worth ~$150M; Mostaque stepped down as CEO March 23, 2024","source_url":"https://en.wikipedia.org/wiki/Stability_AI","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Holds an MA in mathematics and computer science from Oxford; Forbes reported June 2023, citing over 30 sources, that he misled investors and the public about his educational background and misrepresented his involvement in developing Stable Diffusion, and made unsubstantiated claims of partnerships","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stable Diffusion's latent-diffusion architecture was developed by Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz of the CompVis group at LMU Munich with Runway, trained on LAION-5B data; Stability AI's role was compute (256 A100 GPUs, ~150,000 GPU-hours), funding, employing the r","source_url":"https://en.wikipedia.org/wiki/Stable_Diffusion","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Listed as one of 28 authors on the OpenFold preprint with affiliation 'Stability AI'; the paper is a trainable reimplementation of AlphaFold2 for protein structure prediction","source_url":"https://www.biorxiv.org/content/10.1101/2022.11.20.517210v1","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records education at the University of Oxford and CEO of Stability AI 2019-2024; no doctorate, thesis or advisor is recorded","source_url":"https://www.wikidata.org/wiki/Q114049362","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Forbes (June 2023), citing over 30 sources, reported Mostaque misled investors and the public about his educational background and misrepresented the extent of his personal involvement in developing Stable Diffusion","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Mostaque as CEO of Stability AI 2019–2024, educated at Oxford, with no doctorate, thesis or advisor recorded; a business/founder role rather than a documented research career","source_url":"https://www.wikidata.org/wiki/Q114049362","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Stable Diffusion's architecture was developed by Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz of CompVis/LMU Munich; Stability AI provided compute and funding, not the core research","source_url":"https://en.wikipedia.org/wiki/Stable_Diffusion","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Mostaque was co-founder and CEO of Stability AI (2019–March 2024); his background was hedge-fund management and consulting, with no documented personal AI/LM research record","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Forbes (June 2023, 30+ sources) reported he misled investors and the public about his educational background and overstated his personal involvement in developing Stable Diffusion","source_url":"https://en.wikipedia.org/wiki/Emad_Mostaque","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.79,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":91},{"id":90,"slug":"nikil-viswanathan","name":"Nikil Viswanathan","title":"Co-founder & CEO","company":"Alchemy","sector":"crypto","profile_url":null,"image_url":null,"score":17,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":9,"industry_impact":7,"scientific_founder":4},"rubric_version":3,"weighted_score":17,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Viswanathan holds a BS and MS in Computer Science from Stanford focused on distributed systems and computer networks — real, verifiable technical education, though at the master's (not PhD) level and not in AI/ML specifically. He worked as an engineer on core products at Google, Microsoft and Facebook before co-founding Down To Lunch (a consumer social app) and then Alchemy, a blockchain infrastructure company (node/API infrastructure, not an AI/ML company). No evidence was found of him authoring papers, patents, or systems in vector embeddings, transformers, or language modeling; Alchemy's core product is Web3 developer infrastructure (RPC nodes, indexing) rather than AI systems, so hands_on_engineering and industry_impact are scored for legitimate infrastructure-engineering leadership rather than AI-specific depth. He is a real hands-on technical co-founder (not merely a business-side CEO), which supports moderate hands_on_engineering credit, but there is no verifiable connection between his work and the core-AI lineage this rubric measures.\n\nNothing in Viswanathan's record enters the frontier-model lineage: Alchemy is Web3 RPC/node/indexing infrastructure, and no paper, architecture, dataset, optimizer, tokenizer, or training/inference component of his is cited by or built into GPT/Claude/Gemini/Llama-class systems — the Semantic Scholar (h-index 1) and PubMed hits are homonym noise (linguistics/MOF chemistry) unrelated to him. He has zero verifiable years in language modeling — no vector-space, LSI, n-gram, neural-LM, seq2seq, transformer, or LLM pretraining/alignment work of any kind. He is a genuine hands-on technical co-founder (Stanford BS/MS CS in distributed systems, engineering roles at Google/Microsoft/Facebook, co-founder of Down To Lunch ~2015 and Alchemy 2017–present, ~9 years), but that founding work is entirely OUTSIDE this field — a blockchain-infrastructure company, not one whose core is these AI/LM systems — which caps scientific_founder in the 3–7 'technical founder outside this field' band.","evidence":[{"claim":"BS and MS in Computer Science from Stanford University, focused on distributed systems and computer networks","source_url":"https://www.clay.com/dossier/alchemy-ceo","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founder and CEO of Alchemy, a blockchain developer platform (Web3 node/API infrastructure) used by JPMorgan, Robinhood, Visa, Stripe, Polymarket","source_url":"https://www.alchemy.com/company","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Nikil Viswanathan as an American software engineer, born 6 October 1987 in Chicago, educated at Stanford University and Stanford University School of Engineering, co-founder and CEO of Alchemy, notable work 'Down To Lunch'","source_url":"https://www.wikidata.org/wiki/Q30069857","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The Wikipedia article titled 'Alchemy (company)' is an unrelated American film distributor that filed for bankruptcy in 2016, confirming there is no Wikipedia record of his blockchain company or of any technical/AI work by him","source_url":"https://en.wikipedia.org/wiki/Alchemy_(company)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Alchemy's Semantic Scholar name match ('N. Viswanathan', 7 papers, 6 citations, h-index 1) is a non-exact match and does not correspond to him; no AI-lineage publication is attributable","source_url":"https://api.semanticscholar.org/graph/v1/author/40791257?fields=name,paperCount,citationCount,hIndex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Alchemy's core product is Web3 developer infrastructure (RPC nodes, APIs, indexing), not AI/ML or language-modeling systems, so no frontier-model lineage exists","source_url":"https://www.alchemy.com/company","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Viswanathan as an American software engineer educated at Stanford, co-founder/CEO of Alchemy, notable work 'Down To Lunch' — no AI/LM research attributed","source_url":"https://www.wikidata.org/wiki/Q30069857","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar match ('N. Viswanathan', 7 papers, 6 citations, h-index 1) is non-exact and, with the PubMed linguistics/chemistry hits, is homonym noise — no language-modeling publication is attributable to him","source_url":"https://api.semanticscholar.org/graph/v1/author/40791257?fields=name,paperCount,citationCount,hIndex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records Nikil Viswanathan as an American software engineer, educated at Stanford, notable work 'Down To Lunch' — no AI/ML/language-modeling work listed","source_url":"https://www.wikidata.org/wiki/Q30069857","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Semantic Scholar 'N. Viswanathan' (7 papers, 6 citations, h-index 1) is a non-exact homonym match with no AI-lineage publication attributable to the Alchemy founder","source_url":"https://api.semanticscholar.org/graph/v1/author/40791257?fields=name,paperCount,citationCount,hIndex","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.72,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":92},{"id":88,"slug":"jeff-yan","name":"Jeff Yan","title":"Founder","company":"Hyperliquid","sector":"crypto","profile_url":null,"image_url":null,"score":16,"tier":"narrative_only","dimensions":{"foundations":6,"vector_embeddings":0,"transformers_lm":0,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":10,"industry_impact":4,"scientific_founder":6},"rubric_version":3,"weighted_score":16,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"This dossier's programmatic matches (Wikidata Q6175301 'Jeff Yang' the Harvard journalist/businessman, the OpenAlex/Semantic Scholar CAPTCHA-security author, and the PubMed hits) are all different people and must be disregarded. The real Jeff Yan of Hyperliquid studied mathematics and computer science at Harvard College (2013-2017), was an International Physics Olympiad medalist, then worked as a low-latency algorithm/market-making developer at Hudson River Trading before founding Chameleon Trading and Hyperliquid, a custom L1 perpetuals DEX. This is strong quantitative/systems foundations and personal, hands-on trading-infrastructure engineering, but there is no verifiable paper, patent, thesis, or public code repository establishing any personal record in vector embeddings, attention, or language modeling — his technical output is exchange/market-microstructure engineering, not AI research. industry_impact is scored for building and leading the engineering of a real, technically substantial trading system (Hyperliquid), not for its market cap or fame.\n\nNothing of Jeff Yan's work sits in the lineage of frontier language models: Hyperliquid is a custom Layer-1 perpetuals exchange (HyperBFT consensus, HyperCore on-chain order books) with no attention/transformer/embedding/optimizer/dataset/scaling contribution that GPT/Claude/Gemini/Llama-class systems cite or build on, so frontier_founder and lm_domain_depth are near-zero (zero verifiable years in statistical/neural language modeling — his corpus is market-microstructure and blockchain engineering, and the CAPTCHA/security 'Jeff Yan' in the dossier is a different person). He IS, however, a genuine scientific/technical founder who personally architected and wrote the core system his company runs on (self-funded, first-principles L1), just OUTSIDE this field — a language-modeling technical-founder record does not exist — which the anchor caps at 3-7. Counting from Chameleon Trading (~2020) and Hyperliquid (~2022) gives roughly 5-6 years as a hands-on technical founder, all in trading/exchange infrastructure rather than AI.","evidence":[{"claim":"Jeff Yan studied mathematics and computer science at Harvard College, 2013-2017, and was a physics olympiad medalist","source_url":"https://www.datawallet.com/crypto/who-is-jeff-yan-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"After Harvard, Yan worked at Hudson River Trading as an algorithm developer in low-latency equities market-making before founding Chameleon Trading and then Hyperliquid","source_url":"https://colossus.com/article/beyond-the-sky-jeffrey-yan-hyperliquid/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid is a self-funded, custom Layer-1 decentralized perpetuals exchange founded by Jeff Yan and co-founder iliensinc, with no outside VC funding","source_url":"https://hyperliquidguide.com/ecosystem/who-created-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid is a layer-1 blockchain 'written and optimized from first principles' using HyperBFT, a custom consensus algorithm inspired by HotStuff; HyperCore runs fully on-chain perpetual and spot order books with one-block finality at 200k orders/second; HyperEVM adds general smart contracts.","source_url":"https://hyperliquid.gitbook.io/hyperliquid-docs/about-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The official hyperliquid-dex/node repository (Apache-2.0) contains node binaries and validator documentation and attributes no named individuals or founders.","source_url":"https://github.com/hyperliquid-dex/node","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata has no entity for Hyperliquid's Jeff Yan — a search for 'Jeff Yan' returns only Jeff Yang (a Taiwanese-American writer), a researcher named Jeff Yang, a Chinese urologist and Jeff D Yanosky.","source_url":"https://www.wikidata.org/w/index.php?search=Jeff+Yan","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The academic 'Jeff Yan' in the dossier is a security researcher whose corpus is CAPTCHA-breaking, password guessing, acoustic side channels and image forensics (Newcastle/Strathclyde/Linkoping) — a different person.","source_url":"https://www.semanticscholar.org/author/1704945","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid is a self-funded custom Layer-1 built 'from first principles' with HyperBFT consensus and HyperCore fully on-chain order books, founded by Jeff Yan — a blockchain/exchange system, not an AI or language-model contribution","source_url":"https://hyperliquid.gitbook.io/hyperliquid-docs/about-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jeff Yan personally sets and executes Hyperliquid's technical direction as its founder after building low-latency trading systems at Hudson River Trading and founding Chameleon Trading, with no academic publication or AI-research record","source_url":"https://colossus.com/article/beyond-the-sky-jeffrey-yan-hyperliquid/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid was founded (Chameleon Trading precursor ~2020, Hyperliquid ~2022) as a self-funded venture with no outside VC, with Yan as the technical founder","source_url":"https://hyperliquidguide.com/ecosystem/who-created-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid is a self-funded custom Layer-1 perpetuals DEX Jeff Yan personally architected (HyperBFT consensus, on-chain order books) after founding market-maker Chameleon Trading following Hudson River Trading — a technical founder role outside AI/language modeling.","source_url":"https://colossus.com/article/beyond-the-sky-jeffrey-yan-hyperliquid/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Hyperliquid's stack (HyperBFT inspired by HotStuff, HyperCore on-chain order books, HyperEVM) is blockchain/exchange engineering with no language-model or frontier-AI component, confirming no frontier-model lineage.","source_url":"https://hyperliquid.gitbook.io/hyperliquid-docs/about-hyperliquid","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.55,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":93},{"id":77,"slug":"paolo-ardoino","name":"Paolo Ardoino","title":"CEO, Tether / CTO, Bitfinex","company":"Tether","sector":"crypto","profile_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","image_url":"/api/v1/ceo-ai-leaderboard/portrait/paolo-ardoino.jpg","score":16,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":2,"hands_on_engineering":8,"industry_impact":4,"scientific_founder":5},"rubric_version":3,"weighted_score":16,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Paolo Ardoino holds a Computer Science degree from the University of Genoa (2008) and has a genuine hands-on software engineering background — early work as a researcher on a military high-availability/self-recovering-networks/cryptography project, then joining Bitfinex in 2014 as a senior developer optimizing its matching engine, rising to CTO. This shows real systems/infrastructure engineering competence, but none of it is in the core AI lineage: no publications, degrees, or shipped systems in linear algebra/optimization/statistical learning, vector embeddings, or transformer/language-model research were found in Wikipedia, Wikidata, OpenAlex, PubMed, or web search. His current role leading Tether (stablecoin infrastructure) and Bitfinex is financial-exchange and crypto-infrastructure engineering, not an AI company whose core is these systems. hands_on_engineering is scored moderately for genuine matching-engine/distributed-systems work; all AI-specific dimensions are scored near floor since there is no verifiable AI research or engineering record.\n\nNothing of Ardoino's own work sits in the attention→transformer→language-model lineage that today's frontier models descend from: no OpenAlex, Semantic Scholar, PubMed or patent record exists, and his engineering output is a crypto-exchange matching engine and stablecoin infrastructure, not any architecture, objective, embedding, optimizer or dataset the frontier stack cites — so frontier_founder is at floor. He has zero verifiable years in language modeling specifically; Tether AI Research's 2026 offline-translation model releases are organizational output he did not personally author, so lm_domain_depth is at floor. He is a genuine technical founder with roughly a decade of hands-on technical leadership (Fincluster founder 2013, Bitfinex CTO from 2016, Tether CEO from 2023), but that founder-technical record is entirely OUTSIDE this field — exchange and stablecoin systems, not language modeling — which the anchor places at 3-7, hence scientific_founder of 5.","evidence":[{"claim":"Computer Science degree, University of Genoa, graduated 2008","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Joined Bitfinex in 2014 as Senior Software Developer optimizing the matching engine; promoted to CTO in 2016; CEO of Tether since December 2023","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No OpenAlex, PubMed, or Semantic Scholar record found for Paolo Ardoino","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Bachelor's degree in computer science from the University of Genoa; worked as a researcher there on cybersecurity and cryptography; developed trading algorithms at a hedge fund; founded Fincluster in 2013; joined Bitfinex 2014 as software engineer, CTO from 2016 working on the trading engine and bac","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q126537585 records only University of Genoa as education, with no doctorate and no research occupation listed","source_url":"https://www.wikidata.org/wiki/Q126537585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PubMed search 'Ardoino P[Author]' returns 0 results; no OpenAlex or Semantic Scholar author record exists for him","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Ardoino+P%5BAuthor%5D","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Tether AI Research released open-source offline translation model families (TranslatePsy-AfriSLM, TranslatePsy-EuroNano) in September 2026 — organisational output, not personally authored by Ardoino","source_url":"https://tether.io/news/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"GitHub account PaoloArdoino shows a single public repository","source_url":"https://github.com/paoloardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No OpenAlex, Semantic Scholar, or PubMed author record exists for Paolo Ardoino; his record is crypto-exchange and stablecoin engineering, with no contribution to transformer/LM lineage","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q126537585 lists only University of Genoa education and 'computer scientist and manager' occupation — no research/LM record","source_url":"https://www.wikidata.org/wiki/Q126537585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded Fincluster (2013), Bitfinex CTO from 2016, Tether CEO since December 2023 — a technical founder/executive, but of exchange/stablecoin companies, not AI/LM systems","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ardoino has no OpenAlex, Semantic Scholar or PubMed author record; no papers or patents in the AI/LM lineage, so no frontier-model component traces to him","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Ardoino+P%5BAuthor%5D","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ardoino's verifiable technical work is Bitfinex's matching engine (Senior Developer 2014, CTO from 2016) and Tether infrastructure — exchange/stablecoin systems, not language modeling","source_url":"https://en.wikipedia.org/wiki/Paolo_Ardoino","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata records only a University of Genoa education with no doctorate or research occupation, consistent with a technical-founder role rather than an AI research career","source_url":"https://www.wikidata.org/wiki/Q126537585","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.81,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":94},{"id":72,"slug":"yu-hu","name":"Yu Hu","title":"Founder & CEO","company":"Kaito AI","sector":"crypto","profile_url":null,"image_url":null,"score":16,"tier":"narrative_only","dimensions":{"foundations":2,"vector_embeddings":4,"transformers_lm":3,"frontier_founder":1,"lm_domain_depth":2,"hands_on_engineering":6,"industry_impact":6,"scientific_founder":4},"rubric_version":3,"weighted_score":16,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"The dossier's Wikipedia/Wikidata block ('Yu (Jeffrey) Hu', Purdue/Georgia Tech business professor, PhD MIT Sloan under Erik Brynjolfsson) is a homonym and is NOT the Kaito AI founder — the real Yu Hu of Kaito AI studied at the University of Cambridge, then worked as an investment banking analyst at Deutsche Bank and a portfolio manager at Citadel before founding Kaito AI, an AI-powered search/analytics engine for digital assets. No verifiable degree in computer science, mathematics, or a technical PhD was found for him; his background is finance, not AI research. Kaito AI is a real, shipped product (crypto-market search/analytics using LLMs), so there is some hands-on-engineering/industry-impact credit for building and leading an AI-product company, but no evidence of Yu Hu personally authoring papers, code, or research in vector embeddings, transformers, or LM training — the rubric requires personal, verifiable technical depth, which is not established here. Scored low across all core-research dimensions, with modest industry-impact credit for founding and running a functioning AI product company.\n\nNothing of Yu Hu's own authorship — no architecture, embedding method, dataset, optimizer, alignment technique or training/inference stack — is cited by or built into any frontier model's technical report; Kaito AI consumes LLMs for a crypto attention/search product rather than contributing to the lineage, so frontier_founder is essentially nil. His verifiable background is finance (University of Cambridge, Deutsche Bank IB analyst, Citadel PM), with no found CS/ML degree and no personal record in vector-space models, neural or statistical language modeling, so lm_domain_depth is negligible — the dossier's 2006-onward publications and 20 'years active' belong to homonymous 'Yu Hu's (a Purdue business professor, COVID-19 clinicians, semiconductor/robotics researchers), not the Kaito founder. He is the founder-CEO of a real, shipped AI-product company (Kaito, founded ~2022, ~3-4 years), but its science and engineering are done by others and his own contribution is business/product rather than authoring the core research, code or patents, so scientific_founder lands only in the 'CEO of an AI company whose science was done by others' band.","evidence":[{"claim":"Kaito AI founder Yu Hu studied at the University of Cambridge, previously an Investment Banking Analyst at Deutsche Bank and managed a $500M portfolio at Citadel","source_url":"https://x.com/Param_eth/status/1965085813236002964","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founder/CEO of Kaito AI, an AI-powered search engine for digital assets, backed by Dragonfly, Sequoia and Jane Street","source_url":"https://www.linkedin.com/in/yuhu9277/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The Wikipedia/Wikidata entry titled 'Yu (Jeffrey) Hu' (Q15109655) describes a Purdue University Daniels School of Business professor and MIT Digital Fellow, a different person from the Kaito AI founder","source_url":"https://en.wikipedia.org/wiki/Yu_(Jeffrey)_Hu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito's own site describes it as 'The intelligence and financial markets platform for the attention economy', offering Mindshare Arena, Aura profiles, Capital Launchpad and trading rewards; it names no founders and details no model or retrieval architecture","source_url":"https://www.kaito.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito's documentation frames the company around 'InfoFi' and the attention economy rather than any described AI/search technology stack, and contains no founder biography","source_url":"https://docs.kaito.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The Wikipedia article 'Yu (Jeffrey) Hu' used by the dossier is a Purdue University business-school professor (Accenture Chair, Daniels School of Business, INFORMS Distinguished Fellow, MIT IDE Digital Fellow) with no mention of Kaito AI, crypto or startup founding — a different person","source_url":"https://en.wikipedia.org/wiki/Yu_(Jeffrey)_Hu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito AI founder Yu Hu studied at the University of Cambridge and worked as an Investment Banking Analyst at Deutsche Bank and a portfolio manager at Citadel before founding Kaito — a finance, not AI-research, background","source_url":"https://www.linkedin.com/in/yuhu9277/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito describes itself as 'the intelligence and financial markets platform for the attention economy' (Mindshare, Aura, Capital Launchpad) and names no founders or model/retrieval architecture — the company consumes LLMs rather than contributing foundational work to frontier models","source_url":"https://www.kaito.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's Wikipedia/Wikidata 'Yu (Jeffrey) Hu' (Q15109655) is a Purdue Daniels School of Business professor (PhD MIT Sloan under Erik Brynjolfsson), a homonym unrelated to the Kaito founder, so its publication timeline does not establish any language-modeling record for him","source_url":"https://en.wikipedia.org/wiki/Yu_(Jeffrey)_Hu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito AI founder Yu Hu's background is finance — University of Cambridge, Investment Banking Analyst at Deutsche Bank, portfolio manager at Citadel — before founding Kaito, with no CS/ML degree or research record","source_url":"https://www.linkedin.com/in/yuhu9277/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Kaito positions itself as 'the intelligence and financial markets platform for the attention economy' (Mindshare, Aura, Capital Launchpad), naming no founders and describing no model or retrieval architecture of its own — it applies LLMs rather than contributing foundational methods","source_url":"https://www.kaito.ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's Wikipedia/Wikidata/OpenAlex records ('Yu (Jeffrey) Hu' Q15109655, a Purdue business professor; plus COVID-19/semiconductor/robotics 'Yu Hu' papers) are homonym mismatches, not the Kaito founder — so no verifiable papers, citations or LM lineage attach to him","source_url":"https://en.wikipedia.org/wiki/Yu_(Jeffrey)_Hu","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.45,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":95},{"id":73,"slug":"niraj-pant","name":"Niraj Pant","title":"Co-founder","company":"Ritual","sector":"crypto","profile_url":null,"image_url":null,"score":15,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":2,"hands_on_engineering":4,"industry_impact":6,"scientific_founder":4},"rubric_version":3,"weighted_score":15,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Niraj Pant studied Computer Science at the University of Illinois Urbana-Champaign, including some privacy research in the school's Decentralized Systems Lab as an undergraduate, but dropped out at 19 after about a month to intern at Polychain Capital, where he spent roughly six years as a General Partner investing in crypto/AI infrastructure startups (Offchain Labs, EigenLayer, Polymarket, dYdX, Compound). He then co-founded Ritual, a decentralized execution layer for AI/compute, in 2023. This is a genuine crypto-investing and infrastructure-entrepreneurship record, but there is no evidence of a completed degree, published research, authored papers, patents, or personally built AI/ML systems — his technical depth is that of an investor/allocator who backed AI-adjacent crypto infrastructure, not a researcher or engineer with a first-principles technical record in the core-AI lineage (math foundations, embeddings, transformers). Per rubric, this scores low on the research dimensions; industry_impact reflects Ritual's stated mission (decentralizing AI compute/inference) and his investing track record, but with low confidence given the dossier's OpenAlex match is a wrong-person homonym and little independent technical verification exists.\n\nNothing of Niraj Pant's own — no paper, code, architecture, dataset, optimizer or training/inference stack — is traceable into the frontier-model lineage; the dossier's only publication hits are a wrong-person astrophysics homonym (TeV blazar VLBA papers, Whittier College), so frontier_founder is essentially nil. He has no verifiable language-modeling record at all: his ~six years at Polychain were as an investing General Partner and Ritual (founded 2023, ~2 years) is AI-infrastructure/compute whose actual LLM research (verifiable/private inference, speculative decoding, watermarking) is authored by others (Arka Pal, Akilesh Potti, Rahul Thomas, Micah Goldblum), not him — so lm_domain_depth is adjacent-at-best with no personal LM history. He is a co-founder-CEO of an AI company but not its scientific/technical founder in the rubric sense: the science and engineering are done by technical co-founders and a research team, placing scientific_founder in the 3-7 'AI-company founder whose science was done by others' band (~2 years).","evidence":[{"claim":"Attended University of Illinois Urbana-Champaign for Computer Science, conducted privacy research at the school's Decentralized Systems Lab, but dropped out after ~1 month of a Polychain internship at age 19","source_url":"https://siebelschool.illinois.edu/about/awards/alumni-awards/alumni-awards-past-recipients/81999","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Spent roughly six years as a General Partner at Polychain Capital, leading investment rounds in Offchain Labs, EigenLayer, and Polymarket, among 30+ companies","source_url":"https://fortune.com/crypto/2023/11/08/two-former-polychain-partners-fundraise-25-million-ritual-decentralize-ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Ritual, described as 'the decentralized execution layer for AI', raising $25M in 2023","source_url":"https://fortune.com/crypto/2023/11/08/two-former-polychain-partners-fundraise-25-million-ritual-decentralize-ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual lists Niraj Pant among its team alongside Akilesh Potti and Arka Pal; the company builds decentralized AI infrastructure and an SDK for integrating AI into decentralized applications, emphasising censorship resistance, privacy and verifiable computation","source_url":"https://ritual.net/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual's research/blog output on verifiable and privacy-preserving LLM inference, speculative decoding and sampling, model watermarking and execution-aware consensus is authored by Arka Pal, Rahul Thomas, Micah Goldblum, Maryam Bahrani and Naveen Durvasula; Niraj Pant does not appear as an author on","source_url":"https://ritual.net/blog","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual describes itself as a lab for autonomous intelligence with research frontiers in AI, mechanism design, systems and cryptography, and publishes a whitepaper on delegated execution and attestation rather than named academic papers","source_url":"https://ritual.net/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The OpenAlex author record A5078600276 attached to this slug (3 works, 220 citations, Whittier College) consists of TeV blazar VLBA observation papers in The Astrophysical Journal, i.e. a different person","source_url":"https://doi.org/10.1088/0004-637x/723/2/1150","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual's technical/research output on verifiable and privacy-preserving LLM inference, speculative decoding, watermarking and execution-aware consensus is authored by Arka Pal, Rahul Thomas, Micah Goldblum, Maryam Bahrani and Naveen Durvasula; Niraj Pant does not appear as an author","source_url":"https://ritual.net/blog","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Niraj Pant spent roughly six years as a General Partner at Polychain Capital (an investing role) before co-founding Ritual, described as 'the decentralized execution layer for AI', in 2023","source_url":"https://fortune.com/crypto/2023/11/08/two-former-polychain-partners-fundraise-25-million-ritual-decentralize-ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The OpenAlex record attached to this slug (A5078600276, TeV blazar VLBA observations, Whittier College) is an unrelated astrophysics homonym, not the Ritual co-founder, so there is no verifiable academic/LM publication record","source_url":"https://doi.org/10.1088/0004-637x/723/2/1150","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded Ritual (2023), a 'decentralized execution layer for AI', after ~6 years as a Polychain Capital GP; role is founder/investor, not research author","source_url":"https://fortune.com/crypto/2023/11/08/two-former-polychain-partners-fundraise-25-million-ritual-decentralize-ai/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual's research output on verifiable/private LLM inference, speculative decoding, sampling and watermarking is authored by Arka Pal, Rahul Thomas, Micah Goldblum, Maryam Bahrani and Naveen Durvasula; Niraj Pant does not appear as an author","source_url":"https://ritual.net/blog","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Ritual describes itself as a lab for autonomous intelligence with the technical direction/team (Akilesh Potti, Arka Pal) doing the science; Pant is the business/founder-CEO side","source_url":"https://ritual.net/about","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.57,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":96},{"id":85,"slug":"ahmad-shadid","name":"Ahmad Shadid","title":"Founder & CEO, O.XYZ; founder and former CEO, io.net","company":"O.XYZ","sector":"crypto","profile_url":null,"image_url":null,"score":14,"tier":"narrative_only","dimensions":{"foundations":3,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":1,"lm_domain_depth":1,"hands_on_engineering":7,"industry_impact":5,"scientific_founder":3},"rubric_version":3,"weighted_score":14,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"I could not verify any personal research record in the AI lineage for this person, and the two academic identities the dossier supplies are both homonyms, so the research dimensions are scored near the bottom on absence of evidence rather than on disproof. The dossier's OpenAlex profile belongs to 'Ahmad Jamal Shadid', a University of Ottawa master's student whose 2008 thesis is on load balancing for real-time HLA/RTI distributed simulation - verified directly from the uOttawa repository record (doi 10.20381/ruor-19046) - and whose five works from 2007-2008 are on DEVS, Petri nets and discrete-event simulation, not machine learning; the PubMed cluster is a third, medical, Shadid. What is verifiable about the subject is organisational and infrastructural rather than scientific: io.net, which he founded, is a decentralised GPU compute platform whose own documentation describes its technical basis as adopting the open-source Ray library to distribute workloads across heterogeneous GPUs, and O.XYZ is a follow-on venture. Assembling and operating distributed GPU capacity is real systems work adjacent to the training stack, which is why hands_on_engineering is scored at the 'uses the tools / senior engineering adjacent to the core' band rather than lower, but it is compute brokerage, not model design: I found no paper, patent, preprint, public repository or shipped model by him on embeddings, retrieval, attention, transformers, pretraining, scaling or alignment. Industry impact is low on this rubric's terms because the rubric excludes fundraising, token market capitalisation and 'AI company' branding as evidence, and what remains - an infrastructure marketplace - has ML systems as its customers rather than its core. I report both penalties as zero because I have no citable source for pay-for-play, purchased reach or family funding; that is an absence of evidence, not a clearance, and my overall confidence is correspondingly low.\n\nNo work by the subject enters the frontier-model lineage: io.net is a decentralized GPU-compute marketplace built on the existing open-source Ray library (its own docs), not an architecture, embedding, optimizer, tokenizer, pretraining objective, scaling result or alignment method that GPT/Claude/Gemini/Llama-class systems descend from — frontier_founder is near-bottom. There is zero verifiable language-modeling record for him: the dossier's 2007 first year and 19 years active come entirely from a University of Ottawa distributed-simulation homonym (Ahmad Jamal Shadid, thesis doi 10.20381/ruor-19046), the Semantic Scholar 'K. Shadid' and the 92-hit PubMed medical cluster are two further distinct people, so lm_domain_depth rests on absence of evidence, not depth. As a founder he registers on scientific_founder only in the low band — founder of an AI-infrastructure company (io.net, ~2022; O.XYZ/ORGN follow-on) whose science and engineering are built on others' tools, with no paper, patent, repository or shipped model authored by him personally that I could verify; I therefore count zero verifiable years authoring the core research the company runs on.","evidence":[{"claim":"The dossier's OpenAlex author A5069023772 is a University of Ottawa researcher with 5 works (2007-2008) on distributed simulation, DEVS, HLA/RTI load balancing and Petri nets - topics with no machine-learning content","source_url":"https://api.openalex.org/authors/A5069023772","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"That OpenAlex identity's thesis (doi 10.20381/ruor-19046) is by 'Ahmad Jamal Shadid', a 2008 University of Ottawa master's thesis - establishing the OpenAlex cluster as an academic homonym, not the io.net/O.XYZ founder","source_url":"http://ruor.uottawa.ca/handle/10393/28023","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"io.net's own documentation describes its technical basis as adopting the open-source Ray library to distribute AI workloads across GPUs, cutting infrastructure build time, motivated by the cost of NVIDIA A100 capacity - i.e. a compute-aggregation platform built on existing distributed-computing tool","source_url":"https://io.net/docs","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"O.XYZ's site now redirects to orgn.com, which presents a 'confidential agentic stack' product for defense and regulated teams and names no individual founders","source_url":"https://orgn.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"io.net's technical basis is adopting the open-source Ray library to distribute AI workloads across heterogeneous GPUs — compute aggregation on existing distributed-computing tooling, not a frontier-model building block","source_url":"https://io.net/docs","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's only scholarly identities are homonyms: OpenAlex A5069023772 is 'Ahmad Jamal Shadid', a 2008 University of Ottawa master's thesis on HLA/RTI distributed-simulation load balancing, with no language-modeling content","source_url":"http://ruor.uottawa.ca/handle/10393/28023","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"O.XYZ now redirects to orgn.com, presenting an 'agentic stack' product and naming no individual founder or authored research","source_url":"https://orgn.com/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's OpenAlex author A5069023772 (5 works 2007-2008 on HLA/RTI distributed simulation, DEVS, Petri nets) is 'Ahmad Jamal Shadid', a University of Ottawa master's student — an academic homonym with no ML/LM content, not the io.net/O.XYZ founder","source_url":"http://ruor.uottawa.ca/handle/10393/28023","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.5,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":97},{"id":48,"slug":"andy-jassy","name":"Andy Jassy","title":"President & CEO","company":"Amazon","sector":"general","profile_url":"https://en.wikipedia.org/wiki/Andy_Jassy","image_url":"/api/v1/ceo-ai-leaderboard/portrait/andy-jassy.jpg","score":14,"tier":"narrative_only","dimensions":{"foundations":1,"vector_embeddings":2,"transformers_lm":2,"frontier_founder":2,"lm_domain_depth":1,"hands_on_engineering":4,"industry_impact":8,"scientific_founder":4},"rubric_version":3,"weighted_score":14,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Andy Jassy has no personal research, publication, or engineering record in AI: he holds an AB from Harvard and an MBA from Harvard Business School, joined Amazon in 1997 in a marketing role, and built his career in product management and business leadership. He founded and led AWS (2003-2021) as an executive, which today is critical infrastructure for AI training and inference (Bedrock, Trainium, SageMaker), but this is organizational/business leadership of a cloud platform, not personal authorship of AI research, models, or the math/embeddings/transformer lineage. There is no evidence in Wikipedia, Wikidata, OpenAlex, PubMed, or web search of any authored paper, patent, degree in a technical field, or hands-on coding/model-building record. industry_impact is scored moderately (not low) because AWS under his leadership became foundational infrastructure many AI labs run on, but foundations/vector_embeddings/transformers_lm are near-floor since he has no personal technical record in the core lineage — exactly the 'famous CEO with no personal research or engineering record scores low' anchor case for the core dimensions.\n\nJassy has no personal contribution — no paper, architecture, dataset, optimizer, tokenizer, benchmark or training/inference method — that today's frontier models descend from; the AWS silicon (Trainium/Inferentia) and services (Bedrock, SageMaker) were built by AWS engineers, not authored by him, so frontier_founder is near-floor. He has zero verifiable years in language modeling: his record is business/product leadership from a 1997 Amazon marketing role through AWS and the Amazon CEO seat, with no statistical/neural LM, vector-space, seq2seq or transformer work of his own. He founded and led AWS (2003–2021) as a business/executive founder, but the science and engineering were done by others and AWS is cloud infrastructure rather than a language-modeling company he technically directs, so scientific_founder scores only in the 'founder whose science was done by others' band with no verifiable years as a technical founder.","evidence":[{"claim":"AB Harvard University, MBA Harvard Business School; joined Amazon 1997 in a marketing role","source_url":"https://en.wikipedia.org/wiki/Andy_Jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Founded and led AWS from inception in 2003, CEO of AWS 2016-2021, then Amazon CEO from July 2021","source_url":"https://www.businessbecause.com/news/mba-degree/7456/andy-jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No OpenAlex, PubMed, or Semantic Scholar record found for Andy Jassy","source_url":"https://en.wikipedia.org/wiki/Andy_Jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jassy holds a BA in Government from Harvard College and an MBA from Harvard Business School; joined Amazon in 1997 as a marketing manager; conceived AWS with Bezos in 2003, led the original 57-person team, AWS CEO 2016-2021, Amazon CEO from July 2021. No engineering credentials, research background","source_url":"https://en.wikipedia.org/wiki/Andy_Jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q41812531 lists his education as Harvard Business School (MBA) and Harvard University (BA) and his occupation solely as 'business executive'","source_url":"https://www.wikidata.org/wiki/Q41812531","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"PubMed search 'Jassy A[Author]' returns 0 results","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Jassy+A%5BAuthor%5D","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"AWS custom AI silicon (Trainium, Inferentia) is built by the AWS organisation Jassy founded and led","source_url":"https://aws.amazon.com/ai/machine-learning/trainium/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jassy founded and led AWS from inception and was AWS CEO 2016–2021, then Amazon CEO from July 2021 — an executive/business role, with education limited to a Harvard AB and Harvard MBA and no technical or research credentials","source_url":"https://en.wikipedia.org/wiki/Andy_Jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata Q41812531 lists occupation solely as 'business executive' and education as Harvard University (BA) and Harvard Business School (MBA) — no OpenAlex, PubMed or Semantic Scholar record exists","source_url":"https://www.wikidata.org/wiki/Q41812531","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"AWS custom AI silicon (Trainium, Inferentia) is built by the AWS organisation, an engineering output of the company, not personal authorship by Jassy","source_url":"https://aws.amazon.com/ai/machine-learning/trainium/","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Wikidata lists his occupation solely as 'business executive' and education as Harvard BA and Harvard Business School MBA; no technical/research role","source_url":"https://www.wikidata.org/wiki/Q41812531","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Jassy founded and led AWS from its inception and served as its CEO April 2016–July 2021, as a business leader rather than an engineer","source_url":"https://en.wikipedia.org/wiki/Andy_Jassy","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"No OpenAlex, Semantic Scholar, or PubMed authored record exists — 'Jassy A[Author]' returns 0 results — confirming no language-modeling or frontier research lineage","source_url":"https://pubmed.ncbi.nlm.nih.gov/?term=Jassy+A%5BAuthor%5D","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.88,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":98},{"id":99,"slug":"hayden-adams","name":"Hayden Adams","title":"Founder & CEO","company":"Uniswap Labs","sector":"crypto","profile_url":null,"image_url":null,"score":14,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":0,"transformers_lm":0,"frontier_founder":0,"lm_domain_depth":0,"hands_on_engineering":10,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":14,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Adams holds a BS in mechanical engineering from Stony Brook University (2016), not a computer science, mathematics, or AI-related degree, and has no verifiable AI/ML research record — his canonical contribution (the Uniswap constant-product automated market maker, x*y=k, and the v3 concentrated-liquidity design) is a real, personally-authored and personally-built piece of financial/algorithmic engineering, but it is decentralized-exchange mechanism design, not vector embeddings, attention, transformers, or language modeling. This supports real hands_on_engineering credit (he taught himself Solidity after being laid off from Siemens and single-handedly built and shipped Uniswap v1-v3, a system that today processes billions in volume) and some industry_impact as the builder of foundational DeFi infrastructure, but the rubric explicitly scores AI-core dimensions, and there is no verifiable evidence he has authored or built anything in that space. All three AI-core dimensions (foundations beyond generic engineering, vector_embeddings, transformers_lm) score at or near the floor.\n\nNone of Adams's work sits in the lineage of frontier language models: the Uniswap constant-product AMM (x*y=k) and v3 concentrated-liquidity design are DeFi mechanism design in Solidity, not attention, transformers, embeddings, optimizers, tokenizers, pretraining objectives, or alignment methods that GPT/Claude/Gemini/Llama-class systems descend from, and no frontier technical report cites his work. He has zero verifiable years in language modeling — no vector-space, LSI, n-gram, neural-LM, seq2seq, transformer, or LLM-pretraining record in any source. He is, however, a genuine hands-on technical founder — he taught himself Solidity and personally built and shipped Uniswap v1–v3 (launched Nov 2018, ~7 years) and co-authored the v3 whitepaper — but that founding role is entirely OUTSIDE this field, which caps scientific_founder in the 3–7 (technical founder outside the AI/LM core) band.","evidence":[{"claim":"Author of the Uniswap v3 Core whitepaper (March 2021), introducing concentrated liquidity","source_url":"https://app.uniswap.org/whitepaper-v3.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Adams was a former mechanical engineer at Siemens, launched Uniswap in November 2018 inspired by a Vitalik Buterin blog post, and is credited as co-author of the Uniswap v3 whitepaper (March 2021) with Noah Zinsmeister; no AI or machine learning is mentioned","source_url":"https://en.wikipedia.org/wiki/Uniswap","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uniswap is a decentralized exchange protocol on Ethereum using smart contracts and liquidity pools (constant-product rule), i.e. its core technology is not AI systems","source_url":"https://www.wikidata.org/wiki/Q104438477","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uniswap v3 core is a Solidity smart-contract codebase (5.0k stars, BUSL-1.1/GPL-2.0 licensed) with no machine-learning components","source_url":"https://github.com/Uniswap/v3-core","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Adams launched Uniswap in November 2018 after teaching himself Solidity, and is credited as author of the Uniswap v3 whitepaper (2021) — a DeFi AMM protocol with no AI/ML or language-modeling component","source_url":"https://en.wikipedia.org/wiki/Uniswap","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Uniswap v3-core is a Solidity smart-contract codebase with no machine-learning or language-model components","source_url":"https://github.com/Uniswap/v3-core","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Adams authored/co-authored the Uniswap v3 Core whitepaper (March 2021) introducing concentrated liquidity — a DeFi AMM design, not any language-model component","source_url":"https://app.uniswap.org/whitepaper-v3.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Adams, a former Siemens mechanical engineer, taught himself Solidity and launched Uniswap in November 2018; the protocol's core is smart contracts and liquidity pools, with no AI/ML or language-modeling technology","source_url":"https://en.wikipedia.org/wiki/Uniswap","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.76,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":99},{"id":97,"slug":"sergey-nazarov","name":"Sergey Nazarov","title":"Co-founder & CEO","company":"Chainlink Labs","sector":"crypto","profile_url":null,"image_url":null,"score":14,"tier":"narrative_only","dimensions":{"foundations":4,"vector_embeddings":1,"transformers_lm":0,"frontier_founder":1,"lm_domain_depth":0,"hands_on_engineering":8,"industry_impact":6,"scientific_founder":6},"rubric_version":3,"weighted_score":14,"penalties":{"bought_popularity":0,"capital_without_competence":0},"rationale":"Nazarov holds a 2007 NYU bachelor's degree in philosophy and management (no math/CS/ML graduate training), then built a sequence of early startups (ExistLocal, QED Capital, CryptaMail/Secure Asset Exchange) before co-founding SmartContract.com in 2014, which became Chainlink. He co-authored the 2017 Chainlink whitepaper 'A Decentralized Oracle Network' together with Steve Ellis and Cornell professor Ari Juels, and has personally led Chainlink's technical roadmap (oracle network design, Proof-of-Reserve, CCIP) for nearly a decade — this is real, sustained hands-on systems-building and technical leadership, credited under hands_on_engineering/industry_impact. However, Chainlink's core technology is decentralized-oracle/distributed-systems and applied cryptography engineering, not language modeling, vector embeddings, or transformer architectures; no publication, patent, or project ties him to that lineage, so those dimensions score at floor per the brief's explicit guidance on Chainlink. foundations is scored low-but-nonzero for the applied cryptographic/distributed-systems design work embedded in the whitepaper, not for math/ML theory.\n\nNo verifiable line runs from any of Nazarov's work to today's frontier language models: Chainlink's published corpus (the 2017 'Decentralized Oracle Network' whitepaper, Chainlink 2.0, Town Crier, DECO, CCIP) is applied cryptography and distributed-systems / oracle engineering, and no attention, embedding, optimizer, tokenizer, pretraining, scaling, dataset or alignment contribution of his is cited by or built into GPT/Claude/Gemini/Llama-class systems, so frontier_founder is at floor. He has zero verifiable record in language modeling — statistical/neural LMs, vector-space text models, seq2seq, transformers or LLM pretraining/alignment — giving lm_domain_depth 0. He is, however, a genuine, long-tenured technical/scientific founder OUTSIDE this field: he co-founded SmartContract.com in 2014, co-authored the whitepaper his company runs on (with Steve Ellis and Ari Juels), and has personally driven Chainlink's technical roadmap for ~12 years, which the anchors place in the 3-7 band ('a technical founder outside this field'), scored near the top for the duration and depth of that founder-CTO-equivalent record.","evidence":[{"claim":"Nazarov graduated NYU in 2007 with a bachelor's degree in philosophy and management; no graduate STEM degree found","source_url":"https://en.wikipedia.org/wiki/Sergey_Nazarov_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded SmartContract.com with Steve Ellis in 2014; co-authored the Chainlink whitepaper 'A Decentralized Oracle Network' with Steve Ellis and Ari Juels, published 2017","source_url":"https://research.chain.link/whitepaper-v1.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"As CEO of Chainlink Labs, Nazarov has led development of Proof-of-Reserve and the Cross-Chain Interoperability Protocol (CCIP), and joined the CFTC's Innovation Advisory Committee in Feb 2026","source_url":"https://en.wikipedia.org/wiki/Sergey_Nazarov_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"The dossier's Wikipedia/Wikidata match (Q130442938, Ukrainian political strategist and former MP from Odesa) is a namesake, not the Chainlink co-founder","source_url":"https://en.wikipedia.org/wiki/Sergey_Nazarov","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Verified from the whitepaper's own title page: 'ChainLink: A Decentralized Oracle Network — Steve Ellis, Ari Juels, and Sergey Nazarov, 4 September 2017 (v1.0)', on oracle connectivity, on-chain data aggregation, off-chain consensus and reputation/security monitoring.","source_url":"https://research.chain.link/whitepaper-v1.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Born 1986; graduated NYU 2007 with a bachelor's degree in philosophy and management; founded ExistLocal (2009), QED Capital (2011), CryptaMail and Secure Asset Exchange (2014); co-founded SmartContract.com with Steve Ellis in 2014; no AI work mentioned.","source_url":"https://en.wikipedia.org/wiki/Sergey_Nazarov_(businessman)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chainlink was created in 2017 by Sergey Nazarov and Steve Ellis, who co-authored the whitepaper with Cornell professor Ari Juels; it is a decentralized blockchain oracle network bridging on-chain contracts to off-chain data.","source_url":"https://en.wikipedia.org/wiki/Chainlink_(blockchain_oracle)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chainlink's published research corpus (Chainlink 1.0/2.0, Town Crier, Mixicles, DECO, OCR3, Confidential Compute) is cryptography and oracle-network design; none of it is machine-learning research.","source_url":"https://chain.link/whitepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Nazarov co-authored 'ChainLink: A Decentralized Oracle Network' (2017) with Steve Ellis and Ari Juels — cryptography/oracle-network design, not machine-learning or language-modeling research; nothing frontier LMs descend from.","source_url":"https://research.chain.link/whitepaper-v1.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Chainlink's research corpus (Chainlink 1.0/2.0, Town Crier, Mixicles, DECO, OCR3) is applied cryptography and oracle-network design with no ML/LM component.","source_url":"https://chain.link/whitepaper","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Nazarov co-founded SmartContract.com with Steve Ellis in 2014 and has led Chainlink's technical direction (Proof-of-Reserve, CCIP) as CEO for ~12 years — a sustained technical-founder role, but in blockchain oracles, not language modeling.","source_url":"https://en.wikipedia.org/wiki/Chainlink_(blockchain_oracle)","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"},{"claim":"Co-founded SmartContract.com with Steve Ellis in 2014 and co-authored the 2017 Chainlink whitepaper 'A Decentralized Oracle Network' with Steve Ellis and Ari Juels, personally leading the technical roadmap — a technical-founder role, but in decentralized oracles, not AI/LM.","source_url":"https://research.chain.link/whitepaper-v1.pdf","verified":true,"verified_at":"2026-09-14T02:18:59.774252+00:00"}],"confidence":0.73,"source":"seeded","status":"published","scored_at":"2026-09-14T02:18:59.774252+00:00","rank":100}]}