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.

Because advances in biological reasoning carry biosecurity implications, we conducted this work in a tightly controlled setting—using a benign experimental system, limiting the scope of the task, and evaluating model behavior to inform our biosecurity risk assessments and the development of model- and system-level safeguards, as outlined in our Preparedness Framework⁠(opens in a new window).