At the same time, these models raise important dual-use considerations: enabling scientific advancement while maintaining the barrier to harmful information. The same underlying capabilities driving progress, such as reasoning over biological data, predicting chemical reactions, or guiding lab experiments, could also potentially be misused to help people with minimal expertise to recreate biological threats or assist highly skilled actors in creating bioweapons. Physical access to labs and sensitive materials remains a barrier—however those barriers are not absolute.

We expect that upcoming AI models will reach ‘High’ levels of capability in biology, as measured by our Preparedness Framework⁠(opens in a new window)*, and we’re taking a multi-pronged approach to put mitigations in place. In this post, we cover:

We need to act responsibly amid this uncertainty. That’s why we’re leaning in on advancing AI integration for positive use cases like biomedical research and biodefense, while at the same time focusing on limiting access to harmful capabilities. Our approach is focused on prevention—we don’t think it’s acceptable to wait and see whether a bio threat event occurs before deciding on a sufficient level of safeguards.

The future will require deeper expert and government collaboration to strengthen the broader ecosystem and help surface issues that no single organization could catch alone. We’ve consulted with external experts at every stage of this work. Early on, we worked with leading experts on biosecurity, bioweapons, and bioterrorism, as well as academic researchers, to shape our biosecurity threat model, capability assessments, and model and usage policies. As we designed mitigations, human trainers with master’s and PhDs in biology helped create and validate our evaluation data. And now, we’re actively engaging with domain-expert red teamers to test how well our safeguards hold up in practice under high fidelity scenarios.