This is the eighth part in a series on how AI is crossing out of software and into the physical world. This time, Jan Bosch looks at AI-driven discovery. Of everything in this series, the laboratory is where I would most like the optimistic story to be true. And it partly is. The best single piece of evidence I know comes from drug development, and it’s genuinely striking. A 2024 analysis in Drug Discovery Today by Jayatunga and colleagues looked at molecules discovered by AI-native biotech companies that had reached the clinic. Of 24 that completed Phase I, 21 succeeded – a rate of 80-90 percent, against a historical industry average somewhere between 40 and 65 percent. For an industry whose defining characteristic is failure, that’s an enormous result. Then look at Phase II. Of the ten molecules that had completed it, the success rate was around 40 percent. Which is to say, indistinguishable from the historical norm of 30-40 percent. I haven’t been able to stop pondering that pair of numbers, because I think they tell you precisely what AI has and hasn’t done to science. Phase I asks engineering questions: Did we make the molecule we intended? Does it behave in a body the way the design said it would? Is it tolerated? These are questions about whether your model of the molecule was right, and AI turns out to be very good at it. Phase II asks a scientific question: Is this target actually implicated in this disease in a way that matters? That’s a question about whether your model of the biology was right. The evidence so far suggests that AI hasn’t helped with it at all. The authors are careful, and so should we be: Ten molecules aren’t a dataset, the analysis covers only AI-native firms and excludes work done inside large pharma, and these numbers will move. But the shape of the finding is consistent with what I see elsewhere, and it generalizes well beyond medicine. AI has compressed the design step. It hasn’t touched the step where you find out whether the underlying idea was correct. Materials science makes the same point at a much larger scale and with less ambiguity. When Deepmind announced that its GNome system had predicted 2.2 million new crystal structures, it was received as one of the great results of AI-for-science. Then Anthony Cheetham and Ram Seshadri of UC Santa Barbara went through them and published their assessment in Chemistry of Materials. They reported finding “scant evidence for compounds that fulfill the trifecta of novelty, credibility and utility,” and made a distinction I think is worth carrying around: These were chemical compounds rather than materials, “because they have no demonstrated functionality or utility at this point.” Cheetham’s verdict on the practical value was blunter: not particularly useful to experimentalists. You can argue about how harsh that is. What you can’t argue with is the ratio. Two million predictions, a few hundred syntheses a year in a good lab – even if the hit rate were excellent, the queue to evaluate each prediction stretches past the end of everyone’s career. Which is the shape I promised at the top, and it’s the same one as in the previous part of this series. In energy, the digital part of the system outran the wires and the constraint moved from generating power to connecting it. In science, the digital part has outrun the bench and the constraint has moved from having ideas to testing them. Generation became nearly free. Verification didn’t, because verification is physical: It needs reagents, furnaces, cell lines, time and a person who notices that something has gone wrong. The self-driving lab is the serious attempt to widen that bottleneck. The deployments are real. Ginkgo’s cloud lab runs more than seventy instruments with reconfigurable automation carts. Alán Aspuru-Guzik’s Acceleration Consortium in Toronto is building toward a fleet of fifty autonomous robots on a 200-million-Canadian-dollar program. A Ginkgo collaboration with OpenAI cut the cost of ce