LEX. to what degree do you think V3 understands our world? (15:18)
DEMIS. “I think to the extent that it can predict the next frames you know in a coherent way that’s some that is a form you know of understanding right not in the anthropomorphic version of you know it’s not some kind of deep philosophical understanding of what’s going on I don’t think these systems have that but they they certainly have uh modeled enough of the dynamics you know put it that way that they can pretty accurately generate whatever it is 8 seconds of consistent video that by eye at least you know at a glance is quite hard to distinguish what the issues are and imagine that in two or three more years time. That’s the thing I’m thinking about and how incredible that there will look uh given where we’ve come from, you know, the early versions of that uh one or two years ago. And so, um the rate of progress is incredible. And I think um I’m like you is like a lot of people love all of the the the the standup comedians and the the that actually captures a lot of human dynamics very well and and body language, but actually the thing I’m most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids. And it’s pretty amazing that it can do that. And I think that shows that it has some notion of at least intuitive physics, right? um how things are supposed to work uh intuitively maybe the way that uh a human child would understand physics right as opposed to a you know a PhD student really uh being able to unpack all the equations it’s more of an intuitive physics understanding.”
Imagine if every pattern shaped by nature – like a protein’s fold or cosmic phenomena – is inherently learnable by AI.@DemisHassabis shares with @lexfridman that if AI can learn these natural patterns, we could open doors to new eras of scientific discovery.
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— Google DeepMind (@GoogleDeepMind) July 23, 2025
Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475
OUTLINE: 0:00 – Episode highlight 1:21 – Introduction 2:06 – Learnable patterns in nature 5:48 – Computation and P vs NP 14:26 – Veo 3 and understanding reality 18:50 – Video games 30:52 – AlphaEvolve 36:53 – AI research 41:17 – Simulating a biological organism 46:00 – Origin of life 52:15 – Path to AGI 1:03:01 – Scaling laws 1:06:17 – Compute 1:09:04 – Future of energy 1:13:00 – Human nature 1:17:54 – Google and the race to AGI 1:35:53 – Competition and AI talent 1:42:27 – Future of programming 1:48:53 – John von Neumann 1:58:07 – p(doom) 2:02:50 – Humanity 2:05:56 – Consciousness and quantum computation 2:12:06 – David Foster Wallace 2:19:20 – Education and research