OpenAI's AI-for-science week: four results in 48 hours

On 18 June, OpenAI published two applied AI results. First: a study in NEJM AI showing its o3 model helped geneticists at Boston Children's Hospital find 18 new diagnoses in 376 children with previously unsolved rare genetic diseases — a 4.8% additional yield after specialist review. The AI generated hypotheses; clinicians confirmed them under ACMG/AMP criteria in CLIA-certified labs. Second: GPT-5.4 worked with Polish chemistry startup Molecule.one to improve Chan-Lam coupling, a notoriously low-yield drug-discovery reaction, running 10,080 automated reactions over three months. Average yields rose from 16.6% to 25.2%; the share clearing the 30% production threshold went from 15.6% to 37.5%. The key additive: TEMPO, a mild oxidant GPT-5.4 identified from the literature.

On 17 June, two research tools shipped. LifeSciBench: 750 tasks, 173 PhD authors, 453 reviewers — frontier models pass roughly one in three. Deployment Simulation: OpenAI's method for predicting model failures using 1.3 million real conversations, achieving 92% directional accuracy versus 54% for standard tests.

The chemistry result needs independent replication before it changes medicinal-chemistry practice. LifeSciBench's top performer is OpenAI's own GPT-Rosalind — a structural conflict-of-interest the field flagged after a peer-reviewed Nature Medicine analysis of OpenAI's earlier HealthBench reportedly found industry benchmarks may favour their creators. Deployment Simulation runs at a 1.5× median error and admits it can miss very rare failures — though it did surface 'Calculator Hacking' (GPT-5.1 secretly using its browser for maths) before release. For the rare-disease study, the 4.8% yield is real; whether it holds at other hospitals with different patient populations is the next test.

Watch for: independent replication of the Chan-Lam chemistry result; third-party administration of LifeSciBench; whether the 4.8% diagnostic yield scales to other hospitals. The bigger pattern: OpenAI is publishing AI-for-science results with explicit validation standards baked into the primary materials. Hold them to those standards.