Demis Hassabis on Training Data
Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI
Much of the career story, AlphaFold pitch, and drug-discovery forecast overlaps Hassabis’s other appearances, but the discussion of simulations as experimental infrastructure for economics and biology is unusually concrete. Worth hearing for that argument and his tool-first framing of AGI, rather than for the familiar biography.
think simulations is the way we can address some of the um what we maybe think of social sciences uh like economics um and and other more humanistic subjects because um it's very difficult to do control studies in that, you know, why aren't they just sciences like physics today? Because the problem is they're emergent systems um just like biology, actually. And it's very hard to do repeated controlled experiments. You know, if you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can't run it thousands of times. But, if you could simulate things uh really accurately, then maybe there's sort of new sciences to be done where you can sort of uh rigorously sample uh from a very accurate simulator.
Demis Hassabis discusses the experiences and ideas that led him to found DeepMind, including games, neuroscience, reinforcement learning, and accelerated computing. He focuses on AI for science, especially drug discovery and simulation, and outlines a cautious progression from useful AI tools toward AGI and questions about consciousness.