Podcast guest: Robotic labs could accelerate scientific AI

In a discussion about applying AI to biology, chemistry and drug discovery, participants said progress in these fields has not yet matched what they have seen in software engineering. In their view, robotic labs matter both for increasing the volume of experiments and for generating data that can support the next generation of scientific AI models. They also connected a faster “model — experiment — data” loop with the possibility of faster progress.

One speaker suggested that shortening experiment cycles while applying models more often could have a multiplicative effect. Another said that if AI designs more drugs, more candidates will need to be tested, which will likely require physical infrastructure. Experiments in that infrastructure would also generate valuable data, the speaker said. They argued that labs should be built from scratch: in their view, existing CROs are difficult to refit after decades of operating differently, and many do not hire AI researchers or robotics specialists. The discussion also linked the need to develop robotic labs with reshoring this capacity from China.