Name: The Man Behind Google's AI Machine | Demis Hassabis Interview
Uploaded: 2026-01-16T18:25:18.000Z
Duration: 52 min 36 s
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Hello, and welcome to the Tech Download, a new CNBC original podcast where we unpack the tech stories that matter most.

The superlative

Each season, we dive into one big theme and what it means for your money, with insights from the industry's most influential voices.

Future simple

I've always thought that in the end, it would be the most important technology we'll ever invent, and it's sort of the natural progression, really, of the computer age.

This season, we're looking at Google DeepMind, the powerhouse driving the tech giant's AI push.

We've been given rare access to key figures at the company, including our guest for this episode, DeepMind co founder and CEO Demis Hassabis.

I think that it's going to be like the Industrial revolution, but maybe 10 times bigger, 10 times faster.

So it's incredible amount of transformation, but also disruption that's going to happen.

Hey, everyone, and welcome to the Tech Download.

I'm Arjun Karpil, senior technology correspondent at cnbc, based in London, and I've got a very special new co host with me.

I mostly focus on Apple and Microsoft, but look, I've been covering the tech industry for over 15 years now.

I kind of have a good grasp on everything.

And I'm so excited to be here with you, Arjun, because I've just admired your work from across the ocean for so long, and now we actually get to kind of collaborate and do this thing together.

So between us, we think we've got nearly three decades of experience covering tech.

And the crazy thing is we've got so much to learn.

And I think over the course of us doing this podcast, we're going to learn so much, speak to so many interesting people.

I'm so excited that this first series, we're kicking off with an insight into Google DeepMind, one of the world's leading AI labs as well.

And just for our listeners and our viewers, a quick intro, I guess, to Google DeepMind.

It was a company founded in 2010 here in London, where I sit as well.

Very small company founded by three people.

Demis Hassabis, Shane Legg, and Mustafa Suleiman, who's at Microsoft now.

And in fact, I interviewed him nearly a year ago now.

He's basically doing what Demis is doing over at Google, and it's just kind of interesting to See how Google was like this incubator, so to speak, for all of this top AI talent around the world.

What I also think is really interesting, though, is just this AI moment Arjun, we've been living through for the last three years and how three years ago, ChatGPT comes on the scene and Google was kind of seen as under threat.

They had to go through a bunch of reorganizations internally.

Eventually demos came out on top as the leader of AI.

And guess what, 2025 was a really interesting year for AI over at Google.

They kind of caught up and in some ways even surpassed what ChatGPT was already doing.

And this is really interesting because the fundamental technology for all these large language models we've been talking about for so many years started at Google, and the perception was Google let ChatGPT kind of take that technology and run away with it.

But now, in my view at least, Gemini is pretty much on par, if not better than ChatGPT.

Google actually acquired DeepMind in 2014.

I was very new into my career as a tech reporter as well.

Google paid around 400 million pounds for DeepMind at the time.

It's a stake this day that could be worth tens of billions, maybe hundreds of billions of dollars, according to some estimates today.

And DeepMind really is very much responsible for Google's AI with Deep.

Talk about Gemini, the chatbot, the AI that Google's released to consumers.

This is powered so much by the technology coming out of DeepMind.

But even before all of this, DeepMind was having some big breakthroughs.

There was a big moment a few years ago when they released a system called AlphaGo.

This was the first computer program that was able to defeat a world champion in a game called Go.

This is a very complex game, and it was seen at the time as one of the grand challenges of AI because it was such a complex game with so many different combinations available.

The other big breakthrough, of course, was was something called AlphaFold.

This was another AI system developed at DeepMind that could accurately predict 3D models of protein structures.

And the idea is here is, if you could do that, this may lead to some medical breakthroughs.

So this advancement of science has been pretty core to what DeepMind's been up to, and clearly it was a significant bet for from Google more than 10 years ago because it's helped turn Google into an AI world leader today.

What really struck me about DeepMind, having watched them for so many years, is how rooted in science they were.

They weren't necessarily trying to build consumer products like they do now.

They were really trying to solve fundamental problems in science and really usher in this era of AI powered drug discovery, of other big complex problems like climate change.

I know Demis talks about that a lot and he's going to talk about that in your conversation.

Look, it's a great scene setter for DeepMind, so let's get into the conversation with its CEO Demis Asabis.

Demis, thanks for joining me on the tech download.

We're going to try to get through a lot in our time here, but I want to start first with the technology itself.

And we've been talking about AI and we've been talking about the capabilities and how they've been continuously improving as well now in the tech world.

And there's a lot of conversations about how good can these models get, how good can these systems get?

And there's a lot of debate around this idea of scaling laws for our, for our listeners.

You know, it's this idea of, of more compute, more data, bigger models eventually will lead to bigger systems as well.

You said we need to push scaling laws to the maximum.

Are we hitting any kind of wolves in terms of progress of those scaling laws, in terms of the ability for these models to get better?

And just from you know, what you've been developing here at D DeepMind, what are you seeing?

Well, look, I think scaling laws are going very well.

So we're definitely seeing increased capabilities by putting in more compute, more data and making these models generally larger.

So that trend's continuing maybe not as fast as it was a couple of years ago.

So there's some talk of diminishing returns, but there's a big difference between sort of no returns and exponential.

And I think we're somewhere in the middle where there's very good returns and that's worth doing.

On top of that, if I, you know, in terms of like getting all the way to AGI, artificial general intelligence, you know, it may be that there's one or two big innovations still needed as well and maybe missing in addition to the scaling up of kind of the existing ideas.

Well, if you look at, I mean we've all, you know, played around with different chatbots and you can see that, you know, they can do very impressive things in some dimensions, but they're kind of like jagged intelligences I like calling them, in the sense of like they're very good at certain things, but there are other things that they don't do, they're not capable of at all.

And if you pose a question in a certain way, you know, you find that they're flawed and they can't do some relatively simple things.

And so for a true general intelligence, you shouldn't see that inconsistency should be consistent across the board.

And also there are things like it can't continually learn, it can't learn new things online, it can't truly create original things.

So there's quite a few capabilities that you would like to see and you would need for general intelligence that are missing from today's systems.

So what would be the sort of unlock to get to those intelligent systems?

I just want to quickly discuss a conversation I had with Thomas Wolf, who's the co founder over at Hugging Face.

He was talking to me a few months back about his view on LLMs in particular large language language models and just saying they're really great.

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The Man Behind Google's AI Machine | Demis Hassabis Interview

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