According to Adaption co-founder and CEO Sara Hooker, who previously worked as VP of AI research at Cohere, AutoScientist represents a new way to approach the AI training process. “What’s super exciting about it is that it co-optimizes both the data and the model, and learns the best way to basically learn any capability,” Hooker told TechCrunch. “It suggests we can finally allow for successful frontier AI trainings outside of these labs.”

AutoScientist builds on the company’s existing data offering, Adaptive Data, which aims to make it easier to build high-quality datasets over time. AutoScientist, meanwhile, is designed to turn those continuously improving datasets into continuously improving AI models. “Our view at Adaption is that the whole stack should be completely adaptable, and should basically optimize on the fly to whatever task you have,” Hooker says.

Of course, that approach will only be as good as the results. In its launch materials, Adaption boasts that AutoScientist has more than doubled win rates across different models — impressive numbers, but difficult to put into context. Since the system is built to adapt models to specific tasks, conventional benchmarks like SWE-Bench or ARC-AGI aren’t applicable.

Still, Adaption is confident that users will see the difference once they try AutoScientist — so confident that the lab is making the tool free to use for the first 30 days after its release.