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An unusually candid 18-minute map of the AI data market that most vendors would rather you not hear. Cai explains why most benchmark results are 'quietly fake,' introduces Verifier's Law to predict which domains (coding, then finance, then healthcare) AI conquers first, and shows how lab data-spending patterns predict product launches months ahead. If you're building in a vertical, this is a useful lens for timing.

• Type 1 data (real workflow captures like GitHub commits, Slack/Jira logs) is far more valuable than Type 2 (contrived expert-generated examples) — but most vendors sell Type 2 marketed as Type 1.
• Verifier's Law: domains mature in order of how cheaply and reliably outputs can be verified — coding won because GitHub provided verification at scale; robotics lags because the modality isn't settled.
• Most benchmarks test single isolated questions instead of sustained reasoning across dependent episodes, and cross-harness differences hide high false-positive/negative rates behind aggregate stats.
• Data market spending is a leading indicator: lab purchases of cybersecurity and biology data preceded the corresponding model launches by months.

Parte de Edición Nº 003: Evals Are the New PRDs: How Anthropic, Bridgewater, and SonderMind Actually Ship AI