Ginkgo Bioworks completed a six‑month program that used its Reconfigurable Automation Cart (RAC) platform and Catalyst software in partnership with OpenAI’s GPT‑5 to design, run, and learn from 36,000 cell‑free protein synthesis experiments. The AI‑driven workflow generated 580 384‑well plates and produced nearly 150,000 data points, proving that large‑scale, fully autonomous experimentation is feasible.
The result was a 40% reduction in the cost of producing superfolder green fluorescent protein, bringing the component cost from $698 per gram to $422 per gram. Lower reagent costs translate directly into a more attractive price point for academic researchers and small biotech firms, while the expanded data set feeds Ginkgo’s Datapoints AI service, strengthening its recurring‑revenue engine.
"This is AI doing real experimental science: designing experiments, running them, and learning from the results," said CEO Jason Kelly. "AI combined with autonomous labs is needed to keep the United States competitive in science worldwide. The results with OpenAI show this approach is working." Co‑founder Reshma Shetty added, "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. Lower cost reagents for protein production enable more data generation and thus more scientific progress per dollar spent."
Investors reacted positively to the announcement, reflecting confidence in Ginkgo’s ability to leverage AI and automation to drive cost efficiencies and broaden its customer base. The achievement also signals a tangible step toward the company’s goal of scaling autonomous experimentation and strengthening its competitive position against rivals such as Twist Bioscience and AbCellera.
The milestone aligns with Ginkgo’s strategic shift from a milestone‑based R&D services model to a platform‑centric, recurring‑revenue business. By demonstrating that AI can optimize reaction compositions at scale, the company is positioning itself to capture a larger share of the synthetic biology market and to accelerate the deployment of its Datapoints AI services. The preprint describing the work has not yet undergone peer review, but the data provide a compelling proof of concept for future cost reductions and broader application of autonomous labs across Ginkgo’s product portfolio.
The content on EveryTicker is for informational purposes only and should not be construed as financial or investment advice. We are not financial advisors. Consult with a qualified professional before making any investment decisions. Any actions you take based on information from this site are solely at your own risk.