Measuring AI’s capability to accelerate biological research

Accelerating scientific progress is one of the most valuable ways AI can benefit humanity. With GPT‑5, we’re beginning to see early signs⁠ of this—not only in helping researchers move faster through the scientific literature, but also in supporting new forms of scientific reasoning, such as surfacing unexpected connections, proposing proof strategies, or suggesting plausible mechanisms that experts can evaluate and test.

Progress to date has been most visible in fields like mathematics, theoretical physics, and theoretical computer science, where ideas can be rigorously checked without physical experiments. Biology is different: most advances depend on experimental execution, iteration, and empirical validation in the laboratory.

To help understand how frontier models behave in these settings, we worked with Red Queen Bio, a biosecurity start-up, to build an evaluation framework that tests how a model proposes, analyzes, and iterates on ideas in the wet lab. We set up a simple molecular biology experimental system and had GPT‑5 optimize a molecular cloning protocol for efficiency.

Over multiple rounds of experimentation, GPT‑5 introduced a novel mechanism that improved cloning efficiency by 79x. Cloning is a fundamental molecular biology tool. The efficiency of cloning methods is critical for creating large, complex libraries central to protein engineering⁠(opens in a new window), genetic screens⁠(opens in a new window), and organismal strain engineering⁠(opens in a new window). This project offers a glimpse of how AI could work side-by-side with biologists to speed up research. Improving experimental methods will help human researchers move faster, reduce costs, and translate discoveries into real-world impact.

Experimental results

An evolutionary framework for optimizing real-world protocols

Using standardized prompts with no human input, GPT5 improved end-to-end cloning efficiency 79-fold, confirmed across experimental replicates.