Ginkgo Bioworks (NYSE:DNA) saw its stock rise about 6% on Thursday afternoon after the company reported that an autonomous laboratory, driven by OpenAI's GPT-5, delivered a 40% improvement over existing benchmarks in cell-free protein synthesis costs.
According to Ginkgo, the GPT-5-driven autonomous lab carried out in excess of 36,000 experiments across six iterative cycles. The system's work drove down reaction costs for protein production to $422 per gram, compared with the prior benchmark of $698 per gram, the company said. The autonomous platform was responsible for designing, executing, and analyzing the biological experiments with minimal human intervention.
Ginkgo emphasized the commercial implications of the result by noting that it has begun offering the improved reaction mix for sale through its reagents store. The company said the autonomous workflow generated nearly 150,000 experimental data points while human involvement was largely confined to reagent preparation and system oversight.
Reshma Shetty, co-founder of Ginkgo Bioworks and a co-author of the study, commented on the outcome. She said, "By pairing a frontier large language model with an autonomous lab, we found reaction compositions that are notably cheaper than prior state of the art. We expect more and more experiments to be run on autonomous labs where reagent and consumables costs dominate the cost of an experiment. Lower cost reagents for protein production enable more data generation and thus more scientific progress per dollar spent."
In addition to commercializing the improved reagent mix, Ginkgo announced it will make the Pydantic model used to validate experiments available as open source. The company described this step as a way to broaden access to the technology for the wider scientific community.
The company framed the finding as a demonstration of how artificial intelligence, when coupled with laboratory automation, can materially change the cost structure for laboratory experiments. The autonomous lab's output of nearly 150,000 data points supports Ginkgo's contention that reagent and consumables costs can be a decisive factor in scientific throughput and cost-efficiency.
Context and limitations
The announcement highlights a specific cost reduction achieved under the conditions described. The company has reported the start of commercial sales for the improved reaction mix and the open sourcing of a validation model, but broader, long-term commercial outcomes and adoption timelines were not detailed in the company's statement.