GPT-5 Just Ran 36,000 Biology Experiments. The Governance Is From 1975
In February, OpenAI and Ginkgo Bioworks announced that GPT-5 had autonomously designed and executed 36,000 biological experiments through robotic cloud labs — humans set the goal, machines did the rest, cutting the cost of producing a target protein by 40%. This is programmable biology closing the loop: design, build, test, learn, repeat, at a pace no human team can match. A Scale AI and SecureBio study found novices given access to LLMs completed biosecurity-relevant tasks with four times greater accuracy than unaided — and around 90% reported little difficulty getting models to provide detailed pathogen instructions despite built-in safety filters.
The governance response is not matched to the capability. The Biden biosecurity executive order that addressed AI risk: revoked. DNA synthesis screening: mostly voluntary. The 1975 Biological Weapons Convention: no provisions for AI. The dual-use problem isn't theoretical — the same tools accelerating drug discovery to a truly exponential pace can optimise how effectively a virus spreads, and researchers have already demonstrated this. For universities running biology departments and deploying frontier models across research infrastructure, the question of what responsible AI use looks like when self-regulation is the primary framework is not one that can wait for a policy cycle.