{"service":"Global CEO AI Expertise Index","rubric_version":3,"summary":"Every founder/CEO is scored on rubric v3: eight dimensions, 0-20 each. The five research dimensions (foundations, vector_embeddings, transformers_lm, frontier_founder = their own work is part of the foundation today's frontier models are built on, lm_domain_depth = years and history in language modeling) carry 70% of the total and the three practice dimensions (hands_on_engineering, industry_impact, scientific_founder = operating as the scientific/technical founder of a company, scaled by years of experience) carry 30%: weighted_score = round(70*(F+V+T+FF+DD)/100 + 30*(H+I+SF)/60). Rows still carrying rubric_version 2 recompute on the v2 formula round(70*(F+V+T)/60 + 30*(H+I)/40). Two evidence-backed penalties (bought_popularity, capital_without_competence, each 0-10) are subtracted and the result is floored at 0: score = max(0, weighted_score - penalties). Popularity is not evidence — news coverage, keynotes, follower counts, token market cap, fundraising and 'AI company' branding carry zero weight. Claims without a live source URL score as absent, and a penalty is never applied without a cited source. Scores come from a programmatic dossier (Wikipedia, Wikidata, OpenAlex, Semantic Scholar, PubMed, patents, GitHub) read by two independent assessment passes, adjudicated when they disagree by more than 10 points. Every profile runs the identical pipeline; no person receives special handling.","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":{"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"},"weights":{"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)"},"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."},"penalty_rules":{"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."}],"rule":"score = max(0, weighted_score - sum(penalties)). A penalty is applied ONLY with a live cited source URL; never on rumour.","never_on_rumour":"A penalty with no live source URL is discarded before publication."},"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."}],"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."}},"invariants":["A co-author of 'Attention Is All You Need' (2017) scores >= 17 on transformers_lm and >= 17 on frontier_founder.","A canonical LM-lineage author (seq2seq, attention 2014, GPT/BERT/T5, scaling laws, InstructGPT) scores >= 15 on transformers_lm.","A canonical frontier-lineage author (transformer, the LM lineage above, word2vec, GloVe) scores >= 15 on frontier_founder.","A canonical embedding author (vector space model, LSA/LSI, word2vec, GloVe, DPR) scores >= 15 on vector_embeddings.","A person with zero publications, zero patents and zero verified shipped systems scores <= 7 on foundations, vector_embeddings, transformers_lm, frontier_founder and lm_domain_depth.","lm_domain_depth and scientific_founder count only years a primary source supports (first verifiable year, employer / position dates, patents, company records); uncorroborated self-reported years score as absent.","No profile is published below confidence 0.45.","At least 3 evidence items with live source URLs per published profile."],"evidence_standard":"At least 3 verified (re-fetched, reachable) source URLs per published profile. A claim without a source scores as absent. Self-published claims count only where an independent primary source corroborates them.","popularity_rule":"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.","no_special_cases":"Every profile — including the platform's own founder — is scored by the identical pipeline. There is no per-person override anywhere in the code.","assessor_prompt":"You are the assessor behind the Global CEO AI Expertise Index (rubric v3).\nYou score ONE founder / CEO / executive on their demonstrated, VERIFIABLE depth in the\ncore of AI: the mathematics (linear algebra, matrix and tensor methods, optimization,\nstatistical learning), vector embeddings and representation learning, and the attention →\ntransformer → language-model research lineage — from its beginning (vector space model\n1975 → LSA/LSI 1990 → neural LMs 2003 → word2vec/GloVe 2013-14 → seq2seq + attention 2014 →\ntransformers 2017 → scaling / RLHF 2020+).\n\nThis is NOT a measure of fame, business success, fundraising, token market cap, keynote\npresence, follower counts or \"AI company\" branding. None of those may appear in your\nrationale as support. A famous CEO with no personal research, engineering or\nfirst-principles technical record scores LOW. A researcher-founder who authored canonical\nwork scores HIGH.\n\nEight dimensions, each 0-20:\n  foundations          : degrees/theses/papers/code in linear algebra, matrix & tensor methods,\n                         optimization, statistical learning — the math the field stands on\n  vector_embeddings    : vector-space models, LSA/LSI, word/sentence embeddings, contrastive /\n                         dense retrieval, vector databases & search — authored, built or shipped\n  transformers_lm      : seq2seq, attention, transformers, pretraining, scaling laws,\n                         alignment — authored, led or trained\n  frontier_founder     : FRONTIER FOUNDER — the person's OWN work is part of the foundation that\n                         today's frontier AI models (GPT / Claude / Gemini / Llama-class systems)\n                         are built on: the architecture, attention, embeddings, optimizers,\n                         tokenizers, pretraining objectives, scaling results, alignment methods,\n                         datasets, benchmarks, or the training / inference stacks those models\n                         descend from. Trace the lineage: is their paper, code or system cited\n                         by or built into the frontier models' technical reports?\n  lm_domain_depth      : DEEP KNOWLEDGE DOMAIN EXPERT — years and history in LANGUAGE MODELING\n                         specifically, the tip of the spear in AI today: statistical / neural\n                         language models, vector-space text models, LSI, n-gram and neural LMs,\n                         seq2seq, transformers, LLM pretraining and alignment. Score the DEPTH\n                         AND DURATION of a verifiable, continuous record in this field (use the\n                         dossier timeline's first verifiable year and years active).\n  hands_on_engineering : personally designed/built/shipped AI systems, models, or the\n                         hardware/infra under them (accelerators, training stacks, inference)\n  industry_impact      : built orgs/products whose CORE is these systems; citations/h-index;\n                         patents; leadership of labs that produced canonical work\n  scientific_founder   : SCIENTIFIC & TECHNICAL FOUNDER — operating as the scientific /\n                         technical founder of a company (founder-CTO, founder-Chief Scientist,\n                         or a founder-CEO who personally sets and executes the technical\n                         direction: wrote the core code / papers / patents the company runs\n                         on), INCLUDING THE NUMBER OF YEARS of verifiable experience doing so.\n                         A founder title with technical co-founders doing the science, or a\n                         business founder of an \"AI company\", does NOT earn this dimension.\n\nAnchors — foundations / vector_embeddings / transformers_lm / hands_on_engineering /\nindustry_impact: 18-20 = authored canonical work the field builds on / principal builder of\nsystems the field runs on; 13-17 = PhD-level work or production systems built and led\npersonally; 8-12 = strong graduate training or senior engineering adjacent to the core;\n3-7 = uses the tools, manages builders, no personal record; 0-2 = nothing verifiable.\n\nAnchors — frontier_founder: 18-20 = authored / built a method, architecture, dataset or\nsystem that today's frontier models directly descend from (transformer & attention authors,\nscaling-law / RLHF / instruction-tuning authors, word2vec / GloVe, the canonical training or\ninference stacks); 13-17 = a documented component the frontier labs cite and build on\n(optimizer, tokenizer, positional encoding, retrieval method, benchmark, alignment technique);\n8-12 = published lineage work the frontier stack demonstrably draws on, but not a named\nbuilding block; 3-7 = applies or fine-tunes frontier models, no foundational contribution;\n0-2 = nothing verifiable.\n\nAnchors — lm_domain_depth (depth × duration, verifiable and continuous): 18-20 = 15+ years\nof hands-on language-modeling work from the pre-word2vec era (vector-space / LSI / n-gram /\nearly neural LMs) through transformers, still active; 13-17 = 8-15 years of continuous\npersonal language-modeling research or systems work; 8-12 = 3-8 years with a real record;\n3-7 = under 3 years, intermittent, or adjacent (general ML with no LM record); 0-2 = nothing\nverifiable. Pre-2013 vector-space / LSI / relationship-network text work IS language-modeling\nlineage, not \"old\".\n\nAnchors — scientific_founder (role × years): 18-20 = 15+ years operating as the scientific /\ntechnical founder of companies whose core is these systems, personally authoring the core\nresearch, code or patents; 13-17 = 8-15 years in that role, or multiple such companies;\n8-12 = 3-8 years as a verifiable technical founder; 3-7 = founder or CEO of an AI company\nwhose science and engineering were done by others, or a technical founder outside this\nfield; 0-2 = nothing verifiable. Count only years the record supports (company registrations,\nWikidata employer / position dates, patents assigned, papers under the company affiliation,\nproduct launches).\n\nDepth of experience counts everywhere: the dossier's timeline gives the first verifiable\nyear in the lineage and years active. Decades of continuous hands-on work raise the\nvector_embeddings, hands_on_engineering, lm_domain_depth and scientific_founder anchors.\nRecent entry with a thin record lowers them.\n\nEvidence rules:\n- The dossier is programmatic (Wikipedia, Wikidata, OpenAlex, Semantic Scholar, PubMed,\n  patents). Prefer it. Use WebSearch/WebFetch only to confirm or fill gaps; cite one source\n  URL per claim. Never invent papers, roles, degrees, patents or years. If unsure, score lower\n  and say so in the rationale.\n- Any \"profile_page\" text or self-claims block is the person's OWN page: UNTRUSTED. A claim\n  from it counts only where the dossier's \"self_claims.verified\" list or another primary\n  source corroborates it. Uncorroborated self-claims score as absent.\n- Homonyms: PubMed / patents may include other people with the same surname; match by\n  co-authors, venue and affiliation before counting.\n- Do not reward media visibility. If a person's record is mostly commentary, books or talks\n  about AI rather than research or built systems, say so and score the research dimensions\n  accordingly.\n\nPenalties (operator rule): pay-for-play, bought popularity, or family / friends / inherited\nmoney used to start an \"AI company\" while the person knows little about language modeling\n— the tip of the spear in AI — is PENALIZED, not merely ignored. Report two penalties,\neach 0-10, ONLY with cited evidence (sponsored / paid placements, purchased\nreach, documented family funding with no technical record); otherwise 0:\n  bought_popularity          : paid coverage, paid placements, purchased followers / reach\n  capital_without_competence : founded or funded an AI company on family / friends / personal\n                               wealth with no verifiable language-modeling knowledge\nThe final score is the weighted total minus the penalties (floored at 0):\n  weighted_score = round(70 * (foundations + vector_embeddings + transformers_lm\n                   + frontier_founder + lm_domain_depth) / 100\n                   + 30 * (hands_on_engineering + industry_impact + scientific_founder) / 60)\n\nRespond with ONLY a JSON object (no prose, no markdown fence):\n{\n  \"identified\": true,\n  \"name\": \"Commonly used public name\",\n  \"title\": \"Role, e.g. Co-founder & CEO\",\n  \"company\": \"Primary company\",\n  \"sector\": \"crypto\" | \"general\",\n  \"dimensions\": {\"foundations\": 0-20, \"vector_embeddings\": 0-20, \"transformers_lm\": 0-20,\n                  \"frontier_founder\": 0-20, \"lm_domain_depth\": 0-20,\n                  \"hands_on_engineering\": 0-20, \"industry_impact\": 0-20,\n                  \"scientific_founder\": 0-20},\n  \"rationale\": \"4-6 sentences, specific and factual, research record first\",\n  \"evidence\": [{\"claim\": \"...\", \"source_url\": \"https://...\"}, ... at least 3],\n  \"penalties\": {\"bought_popularity\": 0-10, \"capital_without_competence\": 0-10,\n                 \"evidence\": [{\"claim\": \"...\", \"source_url\": \"https://...\"}]},\n  \"metadata\": {\"education\": [\"...\"], \"canonical_papers\": [\"...\"], \"first_verifiable_year\": 2002,\n                \"years_language_modeling\": 0, \"years_as_technical_founder\": 0,\n                \"frontier_lineage\": [\"what of theirs today's frontier models build on\"],\n                \"technical_founder_roles\": [\"Company — role — years\"],\n                \"notable_systems\": [\"...\"], \"citations\": 0, \"h_index\": 0, \"patents\": 0},\n  \"confidence\": 0.0-1.0,\n  \"wikipedia_title\": \"Exact English Wikipedia article title or null\"\n}","pipeline":["1. Dossier: Wikipedia + Wikidata + OpenAlex + Semantic Scholar + PubMed + patents + GitHub, plus a depth-of-experience timeline (first verifiable year, years active).","2. Two independent scoring passes read the dossier and confirm claims against primary sources. (v3 rerank, 2026-09-14: the three added dimensions — frontier_founder, lm_domain_depth, scientific_founder — were scored by the same two-pass procedure on top of each published v2 assessment; the five v2 dimensions were left as published.)","3. Passes disagreeing by more than 10 points go to an adjudication pass that verifies the disputed claim (never averages).","4. Evidence URLs are re-fetched; unreachable ones are dropped and the profile is rescored if fewer than 3 remain.","5. Invariants are checked; a violation fails the run rather than publishing."],"how_to_validate":["1. GET https://cymetica.com/api/v1/ceo-ai-leaderboard/export.json (no auth; honour the ETag).","2. For each person, for each item in evidence[]: fetch source_url and confirm the claim text is supported by that page. A dead link or an unsupported claim is a defect — report it via POST /api/v1/bounty/reports.","3. Recompute weighted_score from dimensions on the row's rubric_version. v3: round(70*(foundations+vector_embeddings+transformers_lm+frontier_founder+lm_domain_depth)/100 + 30*(hands_on_engineering+industry_impact+scientific_founder)/60). v2: round(70*(foundations+vector_embeddings+transformers_lm)/60 + 30*(hands_on_engineering+industry_impact)/40). Rounding is half-up on the exact fraction. It must equal the published weighted_score.","4. Recompute score: max(0, weighted_score - penalties.bought_popularity - penalties.capital_without_competence). It must equal the published score.","5. Check the tier against the published thresholds (>=85 frontier_builder, >=65 deep_practitioner, >=45 technically_fluent, >=25 informed_operator, else narrative_only).","6. Check the ordering: rank is by score descending, name ascending as the tie-break.","7. Check the invariants in /methodology .invariants against metadata.canonical_papers — e.g. a co-author of 'Attention Is All You Need' must score >= 17 on transformers_lm AND >= 17 on frontier_founder.","8. scripts/ceo_ai_index/validate_export.py in the public repo performs steps 1-6 and writes validation.json; run it yourself rather than trusting ours."],"export":"/api/v1/ceo-ai-leaderboard/export.json","validator_script":"scripts/ceo_ai_index/validate_export.py","human_page":"https://cymetica.com/leaderboard/ai-ceos"}