When AI Improves Itself | Richard Socher (Recursive)
About this episode
Richard Socher joins Matt Turck to discuss the implications of AI improving itself and its potential impact on scientific discovery.
What happens when AI begins improving itself, and then turns that intelligence toward science? Richard Socher, pioneering AI researcher and CEO and co-founder of Recursive, joins Matt Turck to explore the vision behind his new book, The Eureka Machine. They discuss why scientific progress may be slowing, how large language models can learn the hidden languages of proteins and biology, and why simulations, verifiers and autonomous experiments could unlock superhuman AI capabilities. The conversation covers recursive self-improvement, AI drug discovery and cancer research, hallucination as creativity, virtual cells, self-driving laboratories, agent swarms, the AI Economist, Recursive’s plans, and the compute and data needed to build an AI scientist that never stops learning—and may eventually discover what humans cannot. (00:00) Intro: AI That Improves Itself (00:55) Why Scientific Progress Is Slowing (03:08) The Labyrinth of Human Knowledge (05:59) Can AI Put Science Back Together? (07:57) How LLMs Learn Biology and Proteins (10:56) Next-Token Prediction as a World Model (16:44) Can AI Generate Truly Original Ideas? (17:32) Simulations, Verifiers and Superhuman AI (22:18) The Path…
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