Demis Hassabis

And then on top of that, there's the thinking systems, the new paradigm of the last year where they get smarter the longer amount of inference time you give them at test time.

So all of those things need a lot of compute.

And I don't really see that slowing down.

And as AI systems become better, they'll become more useful and there'll be more demand for them.

So both from the training side, the training side actually is only just one part of that.

It may even become the smaller part of what's needed in the overall compute that's required.

Yeah, yeah, exactly.

We did a little video of the servers frying eggs and things, and that's right.

And we're going to have to figure out how to do that.

There's a lot of interesting hardware innovations that we do.

As you know, we have our own TPU line, and we're looking at, like, inference-only things, inference-only chips, and how we can make those more efficient.

We're also very interested in building AI systems, and we have done the help with energy usage.

So help data center energy, like for the cooling systems, be efficient, grid optimization, and then eventually things like helping with plasma containment fusion reactors.

We've done lots of work on that with Commonwealth Fusion, and also one could imagine reactor design.

And then material design, I think, is one of the most exciting new types of

Solar material, solar panel material, room temperature superconductors has always been on my list of dream breakthroughs and optimal batteries.

And I think a solution to any, you know, one of those things would be absolutely revolutionary for, you know, climate and energy usage.

And we're probably close, you know, and again, in the next five years to having AI systems that can materially help with those problems.

I think fusion and solar are the two that I would bet on.

Solar, I mean, you know, it's the fusion reactor in the sky, of course.