{"service":"Global CEO AI Expertise Index","rubric_version":3,"description":"Ranks founders and CEOs (crypto + general industry) on demonstrated, verifiable depth in the CORE of AI — the mathematics (linear algebra, matrix and tensor methods, optimization, statistical learning), vector embeddings, and the attention -> transformer -> language-model research lineage — NOT on business success. Rubric v3: eight dimensions, 0-20 each. The five research dimensions (foundations, vector_embeddings, transformers_lm, frontier_founder, lm_domain_depth) carry 70% of the total and the three practice dimensions (hands_on_engineering, industry_impact, scientific_founder) carry 30%; two evidence-backed penalties (bought popularity, capital without competence) are subtracted. Popularity is not evidence. Submit any founder/CEO profile URL to get a 0-100 score with a per-dimension breakdown and source-cited evidence.","usage":{"board":"GET /api/v1/ceo-ai-leaderboard?sector=crypto|general&limit=100","export":"GET /api/v1/ceo-ai-leaderboard/export.json  -> every published person with dimensions, weighted_score, penalties, evidence[] and metadata (ETag, no auth)","methodology":"GET /api/v1/ceo-ai-leaderboard/methodology  -> rubric v3 prompt, anchors, weights, penalty rules, invariants and the how-to-validate recipe","profile":"GET /api/v1/ceo-ai-leaderboard/profile/{slug}","score_post":"POST /api/v1/ceo-ai-leaderboard/score?wait=45  {\"url\": \"<profile url>\"}  -> 200 result | 202 pending (poll status_url)","score_status":"GET /api/v1/ceo-ai-leaderboard/score/status?url=<profile url>  -> 200 result | 202 pending | 422 not identified | 503 busy","score_sse":"GET /api/v1/ceo-ai-leaderboard/score/stream?url=<profile url>","human_page":"https://cymetica.com/leaderboard/ai-ceos"},"auth":"none","rate_limit":"3 scorings per IP per hour; results cached 7 days per URL","validation":"Every score is independently re-verifiable: GET /export.json, fetch each evidence source_url, confirm the claim, then recompute weighted_score and score from dimensions + penalties using the formula in /methodology. scripts/ceo_ai_index/validate_export.py in the repo does exactly this.","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"}}}