Demis Hassabis

speaker

292 appearances 1 recordings 1 series first heard Apr 2026 last heard 7 Apr

Demis Hassabis’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Apr 2026 with 1.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: DeepMind's Demis Hassabis on Why AGI is Bigger than the Industrial Revolution | Why LLMs Will Not Commoditise & We Have Not Hit Scaling Laws | Bottlenecks in AI & The Energy Crisis Caused By AI | Whether AI Will Do More to Harm or Help Inequality · 7 Apr 2026 podcast

So I think in most domains where we are ahead of where the field thought, there's still some big things missing, though, like continual learning.

These systems don't learn after you finish training them, after you put them out into the into the world.

You know, they're not very good at learning further things.

And I think some critical capabilities.

Well, people haven't quite figured out yet, and all the leading labs are working on this, like how to integrate new learning into the existing systems that you spent months training.

Of course, the brain does this very elegantly, right?

And probably through things like sleep reinforcement learning.

So you just kind of get consolidation, it's called, in the brain where your memories during the day are replayed.

And then some of that information is elegantly incorporated into your existing knowledge base.

And perhaps we, I thought for a while, maybe we need something like that to incorporate new information along with the existing information base.

Yeah, well, we made some organizational changes.

So I think we've always had the deepest and broadest research bench at Google and at DeepMind.

I mean, if you look at the last decade or plus, you know, 15 years, I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or

Google Research or DeepMind.

So one of our groups, if you think of like AlphaGo and reinforcement learning, and of course, Transformers, these are all the key breakthroughs.

So I would back us to sort of make those breakthroughs in the future, if there are any missing ones.

And I think we've basically helped put together all the talent from around the company sort of pushing in one direction.

And then we talked earlier just about compute resources.

It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions around the company.

So I think a lot of it was assembling together all the ingredients we already had and then kind of pushing with relentless sort of focus and pace, acting almost like a startup really to get back to the frontier and be ahead in many areas.

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