Demis Hassabis has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI.
On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve and what the next big scientific breakthroughs might be.
Chapters:
00:00 — Intro
00:46 — Demis Hassabis: From Chess Prodigy to DeepMind
01:48 — What’s Missing Before We Get To AGI?
03:36 — Why Memory Is Still Unsolved
06:14 — How AlphaGo Shaped Gemini
08:06 — Why Smaller Models Are Getting So Powerful
10:46 — The 1000x Engineer
12:40 — Continual Learning and the Future of Agents
13:32 — Why AI Still Fails at Basic Reasoning
15:33 — Are Agents Overhyped or Just Getting Started?
18:31 — Can AI Become Truly Creative?
20:26 — Open Models, Gemma, and Local AI
22:26 — Why Gemini Was Built Multimodal
24:08 — What Happens When Inference Gets Cheap?
25:24 — From AlphaFold to the Virtual Cells
28:24 — AI as the Ultimate Tool for Science
30:43 — Advice for Founders
33:30 — The AlphaFold Breakthrough Pattern
35:20 — Can AI Make Real Scientific Discoveries?
37:59 — What to Build Before AGI Arrives
