Google DeepMind CEO, Demis Asabas, joins us to talk about the path from here to AGI,

when Google's AI glasses are coming and whether the pace of AI progress can keep up at this rate.

That's coming up right after this.

Welcome to a special edition of Big Technology podcast from Davos.

I'm Alex Cantorwitz, and I'm joined today by a special guest, Demis, the CEO of Google DeepMind Demis. Welcome back to the show.

Great to be here. A year ago, there were real questions about whether AI progress was tailing off. It was in fashion to ask whether LLMs were going to hit a wall. And those questions seem like they've been settled. There's been a tremendous amount of progress over the past year.

Could you tell us what specifically has happened that's gotten the AI industry from that moment of question last year to the point that it is today?

Well, for us internally, we were never questioning that.

Just to be clear, I think we've always been seeing great improvements.

So we were a bit puzzled by why there was this question in the air.

I mean, some of it was to do, people were worried about data running out.

And there is some truth in that is all the data had been used.

Can we create synthetic data that's going to be useful to learn from?

But actually it turns out you can ring more juice out of the existing architectures and data.

So there's plenty of room, I think, and we're still seeing that in both the pre-training,

the post-training and the thinking paradigms, and also the way that they all kind of fit together.

So I think there's still plenty of headroom there just with the techniques we already know about and tweaking and kind of innovating on top of that.

All right, here's what a skeptic would say.

Yeah.

That there have been a lot of tricks that have been put on top of LLMs.

I hear often about scaffolding and orchestration and AI that can use a tool to search the web,

but it won't remember what it learns. As soon as you close that session, it forgets.

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