Episode
The Man Who Turned Down $1.3 Million to Stay in School — And Changed Medicine Forever
- Published
- Jun 25, 2026
- Duration seconds
- 4829
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Summary
What does it actually take to solve a scientific puzzle that stumped researchers for fifty years — and why are the same AI tools that cracked it still getting basic financial math catastrophically wrong? In 2024, Demis Hassabis won the Nobel Prize in Chemistry for something that sounds almost too ambitious to be real: teaching artificial intelligence to predict the three-dimensional shape of any protein, a problem that had resisted traditional science for half a century. The breakthrough, called AlphaFold, has already mapped over 200 million proteins, a feat with profound implications for how we understand and treat disease. In this episode, Larry Kotlikoff sits down with Sebastian Mallaby , the New York Times bestselling author and veteran journalist behind The Infinity Machine , his deeply reported new biography of Hassabis and the rise of DeepMind. Mallaby walks through the improbable arc of Hassabis's career - from chess prodigy to video game programmer to the architect of some of the most consequential AI breakthroughs of the last decade. Then turns to a sharper, more urgent question: what can AI actually be trusted to get right, and where does it quietly, confidently fail? What You'll Learn: [00:32:01] The $1.3 million check Demis Hassabis turned down — and why he chose to study computer science instead of cashing in on his teenage video game fortune [00:33:00] Why AI was considered a dead field as recently as 2012 — and how Hassabis convinced Peter Thiel to fund a company built on a technology that, at the time, couldn't do much of anything [00:34:05] What "agentic AI" actually means — and how DeepMind's early systems learned to master Atari games through pure trial and error, years before "agentic" became a buzzword [00:35:08] Reinforcement…