Demis Hassabis

Look, interestingly, again, I think both cases are true in the sense that, especially us at Google and at DeepMind, we focus a lot on very efficient models.

that are powerful.

Because we have our own internal use cases, of course, where we need to serve, say, AI overviews to billions of users every day.

And it has to be extremely efficient, extremely low latency, and very cheap to serve.

And so we've kind of pioneered many techniques that allow us to do that, like distillation, where you sort of have a bigger model internally that trains the smaller model.

So you train the smaller model to mimic the bigger model.

And over time, if you look at the progress of the last two years, the model efficiencies are like 10x, even 100x better for the same performance.

Now, the reason that that isn't reducing demand is because we're still not got to AGI yet.

So also the frontier models, you keep wanting to train and experiment with new ideas at larger and larger scale, whilst at the same time, at the serving side, things are getting more and more efficient.

So both things are true.

And in the end, I think that from the energy perspective,

I think AI systems will give back a lot more to energy and climate change and these kind of things than they take in terms of efficiency of grid systems and electrical systems, material design, new types of properties, new energy sources.

I think AI will help with all of that over the next 10 years that will far outweigh the energy that it uses today.

Wow, okay.

Well, I mean, 10 years, even 10 weeks is a lifetime in AI.

The Brownian field of 10 years for you.

But I do feel like if we will have AGI in the next 10 years, full AGI, and I think that will usher in a new golden era of science, so a kind of new renaissance.

And I think we'll see the benefits of that right across from energy to human health.

Sure.

Well, look, I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little bit provocative.