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

Absolutely.

I think there's quite a few things that are missing.

There's continual learning.

I think there's a lot of mileage in looking at different memory systems.

At the moment, we have these long context windows, which are kind of a bit brute force.

You just put everything in them.

I think there's a lot of interesting, probably, architectures to be invented there.

Then there's stuff like long-term planning, hierarchical planning.

These systems are not very good at planning at long time horizons, many years into the future, which with our minds we can do.

There's quite a lot of problems I think that are still left to overcome.

Maybe one of the biggest is consistency.

I sometimes call these systems jagged intelligences because they're really amazing at certain things.

when you pose the question in a certain way.

But if you pose a question in a slightly different way, they can actually still fail at quite elementary things.

So a general intelligence shouldn't be that sort of jagged.

Yeah.

Well, I mean, the general intelligence, you know, if you think about how our minds work, it shouldn't have those kinds of holes in it.

Yeah, I feel like maybe, you know, the three or four leading labs now, which we're one, I think the gap is sort of starting to pull away because a lot of these tools also, of course, help you build the next generation.

So things like coding tools, math tools, and it's getting harder and harder, I would say, to kind of eke out the same gains from just the same ideas.

So I think those labs that have capability to, you know, invent new algorithmic ideas are

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