{"service":"Global CEO AI Expertise Index","rubric_version":4,"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 v4: nine dimensions, 0-20 each. The six research dimensions (foundations, vector_embeddings, transformers_lm, frontier_founder, lm_domain_depth, lm_domain_breadth) 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 v4 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":4,"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. word2vec (2013) is the foundational building block of today's language modeling, so the foundation includes the verifiable pre-2013 vector-space / distributional-semantics / relationship-network lineage that produced it (papers, patents, shipped systems) as well as the later blocks: attention, transformers, embeddings, optimizers, tokenizers, pretraining objectives, scaling results, alignment methods, datasets, benchmarks, training / inference stacks. Scored by verifiable position in that lineage, not by whether a frontier lab cites the person by name.","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, hands-on record from vector-space / LSI / n-gram and neural LMs through transformers and LLM pretraining / alignment. Hands-on years count across papers, patents and shipped language-modeling / vector-space systems; the score sits in the band matching the reported year count.","group":"core_research","added_in":3},{"key":"lm_domain_breadth","label":"Cross-Domain Language Modeling","max":20,"description":"Cross-Domain Language Modeling — hands-on experience in different TYPES of language modeling, not only natural-language text: biological (DNA / RNA / protein sequence models, gene-expression and biomedical-literature mining), financial (market / filings / news / prediction-market models), source code, chemistry / materials, legal / clinical / scientific-literature mining, music / media sequence models. Scored on the number of distinct domains with a verifiable hands-on record (papers, patents, shipped systems) and the depth in each; applying a vendor's chatbot to a domain does not count.","group":"core_research","added_in":4},{"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 AND the number of such companies founded in that role. 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","lm_domain_breadth":"Cross-Domain Language Modeling","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 build on (optimizer, tokenizer, positional encoding, retrieval method, benchmark, alignment technique), OR verifiable pre-word2vec (pre-2013) vector-space / distributional-semantics / relationship-network patents or shipped systems in the lineage word2vec-era embeddings descend from.","8-12":"Published lineage work the frontier stack draws on that is neither a named block nor a pre-2013 precursor system.","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 (papers, patents or shipped LM / vector-space systems) from the pre-word2vec era (vector-space / LSI / n-gram / early neural LMs) through the transformer era, still active.","13-17":"8-15 years of personal language-modeling research or systems work.","8-12":"3-8 years with a real record.","3-7":"Under 3 years, or adjacent (general ML with no language-modeling record).","0-2":"Nothing verifiable."},"lm_domain_breadth":{"18-20":"Four or more distinct language-modeling domains each with a real hands-on record (papers, patents or shipped systems), at least two of them outside natural-language text (for example biological AND financial).","13-17":"Three domains, or two domains each with a deep multi-year record.","8-12":"Two domains with a verifiable record.","3-7":"A single domain (natural-language text only), or domain applications of someone else's models with no modeling work.","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 — or three or more such companies founded in that role across 10+ years.","13-17":"8-15 years in that role, or two such companies.","8-12":"3-8 years as a verifiable technical founder of one company.","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","lm_domain_breadth"],"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 + lm_domain_breadth) / 120 + 30 * (hands_on_engineering + industry_impact + scientific_founder) / 60)","formula_v3":"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","frontier_founder":"Frontier Founder","lm_domain_depth":"Deep Knowledge Domain Expert","scientific_founder":"Scientific & Technical Founder"},"added_dimensions_scored_by_delta":["frontier_founder","lm_domain_depth","lm_domain_breadth","scientific_founder"]}}