Anthropic Co-Founder Jack Clark: AI Can Now Do Frontier Science Across Disciplines, and the Researcher Bottleneck May Be Breaking
In a speech at the IMF annual meetings, Jack Clark argued that AI systems have demonstrated breakthrough capability across mathematics, biology, and robotics, and that the training cycle for human researchers—roughly 40 years in his own case—is the fundamental bottleneck to innovation speed. He proposed that AI can be replicated instantly and in arbitrary numbers, enabling unprecedented 'force multiplication' of human scientists, potentially reversing the half-century trend of declining research productivity. He called on countries to build laboratory and robotic infrastructure now while emphasizing the need for careful governance of AI-driven scientific research.
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On October 17, 2026, Anthropic co-founder Jack Clark delivered the Per Jacobsson lecture at the International Monetary Fund annual meetings in Bangkok, Thailand. His core argument: AI has already made substantive contributions at multiple scientific frontiers, and the training cycle for human researchers—he cited his own nearly 40 years of experience as an illustration—is the fundamental bottleneck to innovation speed. If AI can be replicated instantly and in arbitrary numbers, this bottleneck may be broken.
Clark illustrated this with a story from TheoPhysEval, a paper published just the previous week. The paper tests how well AI systems can solve unpublished problems in theoretical physics; its 50 questions were compiled by physicists around the world. Indian physicist Suvrat Raju, commenting on the results, wrote: 'I don't know of any individual who can solve any significant fraction of our final collection of problems without AI assistance.' More surprisingly, when the research team began investigating mistakes the models might have made, the models—especially GPT-6-Astra—insisted their answers were correct and that the expert-supplied answers were erroneous. When they went back to the experts, it turned out that in multiple instances the experts had indeed found subtle errors buried deep inside their own calculations. After revising their answers, Astra's score climbed to 98% (49/50). Clark characterized this not merely as a high score but as AI correcting human experts' errors.