Working with Ginkgo Bioworks, we created an AI-driven autonomous lab and achieved a 40% reduction in protein production cost.

We’ve seen rapid progress from AI in fields like math and physics, where ideas can often be evaluated without touching the physical world. Biology is different. Progress runs through the lab, where scientists run experiments that take time and money.

That’s starting to change. Frontier models can now connect directly to lab automation, propose experiments, run them at scale, learn from the results, and decide what to do next. In much of life science, the bottleneck is iteration, and autonomous labs are built to remove that constraint.

In earlier work, we showed that GPT‑5 could improve wet-lab protocols through closed-loop experimentation. Here, we show that the same approach can reduce the cost of protein production.

We partnered with Ginkgo Bioworks ⁠ (opens in a new window) to connect GPT‑5 to a cloud laboratory—an automated wet lab run remotely through software, where robots execute experiments and return data—and used that lab-in-the-loop setup to optimize a widely used biological process: cell-free protein synthesis (CFPS). Over six rounds of closed-loop experimentation, the system tested more than 36,000 unique CFPS reaction compositions across 580 automated plates. After being provided access to a computer, a web browser, and access to relevant papers, GPT‑5 took three rounds of experimentation to establish a new state of the art in low-cost CFPS, achieving a 40% reduction in protein production cost (and a 57% improvement in the cost of reagents), including novel reaction compositions that are more robust to reaction conditions common in autonomous labs.

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