Demis Hassabis

We have a lot of systems that do the hill climbing of the S-curve that you're currently on.

Yeah, I think it would be a leap, something like that.

We certainly feel there's a lot more room just in the scaling.

So actually all steps, pre-training, post-training, and scaling,

inference time.

So there's sort of three scalings that are happening concurrently.

And we, again, there, it's about how innovative you can be.

And we, you know, we pride ourselves on having the broadest and deepest research bench.

We have amazing, you know, incredible researchers and people like Noam Shazia, who, you know, came up with Transformers and Dave Silver, you know, who led the AlphaGo project and so on.

And, um, it's, it's, it's that research base means that if some new, new breakthrough is required, like an alpha go or transformers, uh, I would back us to be the place that does that.

So I'm actually quite like it when the terrain gets harder, right?

Because then it veers more from just engineering to performance.

to true research and, you know, research plus engineering.

And that's our sweet spot.

And I think that's harder.

It's harder to invent things than to, you know, fast follow.

And so, you know, we don't know.

I would say it's kind of 50-50 whether new things are needed or whether the scaling the existing stuff is going to be enough.

And so in true kind of empirical fashion, we're pushing both of those as hard as possible.

The new blue sky ideas and, you know, maybe about half our resources are on that.