Eric Schmidt argues AI-for-science needs reasoning agents, not more AlphaFolds — because most fields have no Protein Data Bank
Eric Schmidt and Suhas Mahesh (AI for Science lead at the Schmidt Sciences AI Center) make the case that the AlphaFold template does not generalize: the Protein Data Bank took 53 years, roughly $21 billion in experimental work and underpinned 25+ Nobel Prizes to assemble ~170,000 validated structures, and almost no other discipline has an equivalent. Their alternative is iterative reasoning agents that work through uncertainty the way human researchers do rather than requiring a massive pre-existing dataset, citing Google's AI Co-Scientist matching conclusions Imperial College London researchers spent a decade validating in wet-lab work on antibiotic resistance. The builder-relevant framing is throughput: an agent that can 'read a thousand papers in an hour, design 500 molecules' and learn from failed tests.
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