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Training Data · 3 min · Apr 29, 2026

DeepMind bet on combining deep learning and reinforcement learning

Demis Hassabis explains why DeepMind saw potential in combining deep learning, reinforcement learning and GPUs. The team pursued AGI despite skepticism because it offered a new way to fail—or succeed.

Demis Hassabis

Demis Hassabis is cofounder and CEO of Google DeepMind.

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    Konstantine Buhler, Host · Partner, Sequoia

About this segment

Demis Hassabis explains why DeepMind saw potential in combining deep learning, reinforcement learning and GPUs. The team pursued AGI despite skepticism because it offered a new way to fail—or succeed.

From “Demis Hassabis: We're Three Quarters of the Way to AGI”, Apr 29, 2026. Watch the full episode

“deep learning had just been invented by Jeff Hinton and colleagues in academia, but almost no one had really realized it was a big deal.”
Demis Hassabisat 5:13 · Training Data5:13

Takeaways

  1. Deep learning and reinforcement learning were siloed fields.
  2. GPUs looked like a key ingredient for scaling AI.
  3. The team saw value in taking a different path to AGI.

In this episode

Full episode

  1. 4:35DeepMind bet on combining deep learning and reinforcement learning2:51

  2. 9:01AI could shrink drug discovery from years to weeks3:46Up next
  3. 12:40AI simulations could make social sciences more experimental4:41
  4. 3 more moments in the full episode

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 4:39

    All right, so speaking of not being too far ahead of your time, it was 2009, and you decided there would be AGI. Mm-hmm. Yes. Maybe it was only 10 years ahead of our time that time. Better than 50 years. So tell us about, again, room full of founders here. Tell us about 09. How did you convince the first few brilliant talents? You pulled in really high caliber employees, early team members. How do you convince them to believe in what seemed like total sci-fi at the time?

  2. 5:09

    Well, there was some interesting threads that I think we picked up on. I think we thought we were five years ahead, but maybe we were more like 10. But it was deep learning had just been invented by Jeff Hinton and colleagues in academia, but almost no one had really realized it was a big deal. We knew a lot about reinforcement learning, And we felt there was huge progress to be made by combining those two techniques, which almost had never been mixed together, really.

  3. 5:35

    Certainly not at any kind of anything other than toy problems in the academic subjects. They were two quite siloed parts of AI. Then we could see the GPUs at the time were going to be really useful. Of course, we use TPUs now. But the accelerated computing industry was going to be very helpful. and then we also felt at the end of my PhD in postdoc and some of the other people I got together were computational neuroscientists that we had enough ideas and principles from the brain that could be useful including the idea that reinforcement learning could eventually scale to AGI so we felt we had these ingredients and we almost felt like we were keepers of a secret because no one either in academia or industry really believed that any big progress was possible In fact, a lot of the people in academia used to roll out, you know, literally roll their eyes up at us when we were sort of suggested we would work on AGI or strong AI.

  4. 6:32

    It was sometimes called at the time because it was like, well, we know this doesn't work. So, you know, everyone tried it in the 90s. And I did my postdoc at MIT, which was the which is the sort of center point for expert systems. And first of all, the logic language systems. I mean, it seems amazing to think that now. but I was already feeling like that was a caic then but they they you know that's still how it was done both in Cambridge in the UK and also in MIT these big centers of traditional AI and it felt like but actually that convinced me even more that we were onto something because at least if we were going to fail we would fail in a different way than people had failed you know to get to AGI in the 90s so that felt like it was worth doing no matter what even if obviously it was research

  5. 7:18

    We didn't know for sure it would be successful. But at least we would fail in an original way if it didn't work.