Davos 2026: Google DeepMind CEO Demis Hassabis 1/24/26

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0:57these LLMs, these foundation models like Gemini, they're getting better and better with each iteration and we see no end to that. But on the other hand, to get to full AGI, there's some missing capabilities still. Demis Hasebis, CEO of Google DeepMind at the World Economic Forum in Davos, Switzerland. He runs Google's AI Research Lab, which put out, for all of us, Gemini. Different models are getting good at different things, maybe like Claude Fist Co specifically, things like Gemini are amazing for multimodal understanding and, you know, image generation, things like that. Hasebis says there is still a lot left to innovate.

1:35They don't do continual learning. They don't have true creativity yet. They don't do long-term planning and reasoning. Plus, how hyped is too hyped. AI is all the rage for investors, but part of the industry could be in a bubble. If you look at things like the new hot startups that are raising billions of dollars in a seed round with no product or technology yet. That seems a little bit frothy to me. I'm CNBC producer Cameron Costa. Squawk Pod reports from Davos 2026. Google's DeepMind CEO Demis Hasebis begins right now.

2:16The World Economic Forum in Davos is a marathon few days. Joe Kernan, Becky Quick and Andrew Ross Sorkin are meeting with and interviewing some of the world's most influential leaders. And this year, few are as influential as those leaders in AI. You're about to hear from Demis Hassabis. He co-founded an AI research lab called DeepMind, which Google acquired about 12 years ago. And just about a decade ago, DeepMind blew every other mind when its AlphaGo AI model beat the world's best human Go player. Now, that story is completely amazing and thrilling even 10 years later. I highly recommend you go check it out on YouTube.

3:05Nowadays, Hasabees is heading up Google's AI strategy at DeepMind. He pushed out Gemini, the latest version of which recently won a deal to run Apple's Siri intelligence. He is a visionary, a longtime builder of the AI that's now ubiquitous in 2026. And he sat down with Andrew Ross Sorgan outside, of course, at the World Economic Forum in Switzerland.

3:31Andrew Ross Sorkin:For the last year and a half, there's been this battle playing out between OpenAI and Anthropic and you. And I think there was a view that you thought you needed to catch up. I think behind the scenes, you thought you were going to get there. But something happened this year? What do you think it was? Because I think at this moment, your model may very well be at the top of the charts, if you will. I think it's, look, it's been a kind of long journey last couple of years to kind of corral together all of the assets that we have as Google and DeepMind. Incredible research bench, our TPUs, and all of the kind of research we've been doing over the last decade plus, really, that underpins a lot of the AI industry.

4:11So we always had all the ingredients, amazing product surfaces to plug AI into. And I think in the last year, what people are feeling is we've kind of organized that all in a really efficient way.

4:22Andrew Ross Sorkin:Okay. So where are you in the journey though, in terms of what comes next about sort of productizing all of this, really getting it into all of these different surfaces? When am I going to see it in Gmail, for example? Yeah. So look, everything starts first of all with the research and the model quality. So as you mentioned, we're very happy with how our latest Gemini model, Gemini 3, is working. We think it's topping most of the leaderboards on most of the benchmarks. And the other thing we worked hard on in the last year is accelerating the infrastructure and actually doing a lot of rewriting of the infrastructure to get the model quality as quickly as possible into our product services.

4:57So you're starting to see that in, we're simultaneously shipping in search, obviously in the Gemini app. And then what you'll see this year is appearing in more places across all of our product services, especially things like Gmail.

5:09Andrew Ross Sorkin:So Apple has now chosen to work with you. How transformational is that for Gemini and for what your work is? Well, I think it's a massive sort of vote of confidence in the quality of our model. So we're very pleased with that partnership. Obviously, it's an amazing partnership for us, very important one. Apple ran a very rigorous evaluation process and Gemini came top of that and I think that's that's testament to the work that we've we're surprised that they decided they wanted to work with you as opposed to did you think that they would ultimately try to create their own model well you know creating models costs cost a lot of a huge amount of resources and money and also a certain type of research base you know in terms of like the research teams and so on so I think it makes sense now these much these models are mature there's a kind of ferocious battle going on on the frontier to make if your product company to make use of those models and and then downstream do amazing things with it used to use the word interesting word mature that the models are mature when people talk about getting to AGI and sort of how quickly all of this can scale there has been there's sort of two views one is if you just throw more compute processing power at this we can get there there's another view that there needs to be some kind of really breakthrough scientific research shift.

6:30Which is it? Well, actually, I have a kind of in-between view, which is that I think it's an empirical question. So I feel like we're still getting lots of amazing gains out pushing the existing paradigms, these LLMs, these foundation models like Gemini. They're getting better and better with each iteration, and we see no end to that. But on the other hand, to get to full AGI, there's some missing capabilities still. They don't do continual learning. They don't have true creativity yet. They don't do long-term planning and reasoning. So it's a question mark about a couple more, maybe a handful, two or three new big breakthroughs needed in order to get all the way to AGI or scaling up the existing techniques be enough.

7:10And we're doing both. So we have the luxury of basically having the deepest and broadest research bench and we can push both to the maximum.

7:18Andrew Ross Sorkin:How much do you worry that all these large language models ultimately converge, meaning they become very similar in terms of what they can all do, and then what differentiates them? Yeah, I don't think we're seeing that. I think if you look at the last year, models are actually getting good, different models are getting good at different things, you know, maybe like Claude's code specifically, things like Gemini are amazing for multimodal understanding and, you know, image generation, things like that. So actually, I think you're seeing quite a lot of differentiation at the frontier. And my expectation is that will actually increase the gaps in that and the differences between the models this year.

7:52Andrew Ross Sorkin:There's a big question about whether there's an AI bubble. You've heard that over and over again. Is there a bubble? Well, I don't think it's a binary yes or no. I think some, the industry is big now and there's many parts to it. I think some parts of the AI field may be in a bubble. I mean, if you look at things like the new hot startups that are raising billions of dollars in a seed round with no product or technology yet, that seems a little bit frothy to me and perhaps unsustainable. But on the other hand, there are lots of amazing use cases going on and products being used. Clearly, Alphabet and Google have remarkable financial backing, but there's a lot of even big independents, and I'm thinking of Anthropoc, I'm thinking of OpenAI, that have to continue to get new capital to really meet some of the spending that's necessary to get to the next place.

8:47Andrew Ross Sorkin:Do you worry about whether they'll be able to get there? Well, I think, I mean, I mostly worry about what we need to do. And going back to your bubble question, I think my job is, as head of Google DeepMind, is to make sure whatever happens, whether there is a bubble and it bursts or whether the trajectory continues, the bull case continues from here, we're in a winning position and doing really well. And I think because of our balance sheet and all of the existing amazing products we have that are natural fits for AI. You mentioned, you know, email and there's Chrome and there's, you know, search, of course.

9:18So there's all these amazing products billions of people use every day that I think AI can enhance. And, you know, we're only scratching the surface, I think, of what we can do there. And there's going to be a lot more, I think, amazing kind of product features this year.

9:30Andrew Ross Sorkin:In terms of spending, people talk a lot about spending on data centers and the need for new chips. when you think about sort of what comes next do you think we could have a deep seek like mind deep seek like moment rather where there's some kind of major technological revolution where people say oh maybe we actually don't need all this processing power yeah I think it's very unlikely and I also think the deep seek moment was was a little bit overblown in my opinion like they had to utilize some of the western models in order to kind of fine-tune against and to train from. So it wasn't, you know, what they reported, this very small training number wasn't quite the full picture.

10:06It is possible that one of, you know, there could be a left field breakthrough that does increase the efficiency of things, maybe things like self-improvement, where you could imagine there's some cycle which requires less compute power. But for now, you know, we think that more compute, you need compute for lots of things for training, for serving and for exploring new ideas. But of course, we're also, all of us are trying to make our models, our leading models as efficient as possible. So in fact, of the Gemini 3 models, our flash model, which is sort of the workhorse model, may be the most important because we can deploy that everywhere.

10:37Andrew Ross Sorkin:There's also a big question about the depreciation schedule of these chips, meaning do they really last for four years? Do they last for seven years? And it's not so much that they last. Are they useful in the same way that you probably buy a new phone every year or two? People are going to want to buy and need the next cutting edge chip. And so are we laying down you know railroad tracks or are we laying down railroad tracks that need to be replaced every couple years yeah well I think that's one of the advantage we have is we're full stack so I think we're the only organization really that has a frontier lab and our own chips and TPUs and our cloud business so we have a lot of sort of ways of utilizing any spec compute anywhere on our on our you know on our on our systems and data centers and maybe as compute gets older the older generations, you start moving them towards serving or maybe like labeling data for you.

11:28So you can always utilize even quite older sets of generations of chips for useful work.

11:35Andrew Ross Sorkin:We've mentioned OpenAI. We've mentioned Anthropic. We have not mentioned Meta, and we have not mentioned Elon Musk and XAI. Where do they sit in this sort of competitive stack for you right now? Well, obviously, they're extraordinary companies and they're led by very ambitious and aggressive leaders. And I think they're working on very interesting things. And we'll see what comes out of that this year, especially on Meta. I guess they've rebooted their sort of research division. And we're all waiting to see what they're going to come up with next. But do you think that ultimately there's going to be one or two or three big models?

12:12Andrew Ross Sorkin:And that's how it's going to be. If we're sitting here together five years from now, what does this look like to you? I think there's two pictures from here. I think there could be room. AI is going to be so transformative, and I think it's going to create so much new opportunities. I think there is plenty of room, especially on the enterprise side, for maybe two, three, four winners. But I think each year it's getting harder because the pace is ferocious. These are very capable companies and organizations and research groups that are pushing the frontier, and we're all pushing that as hard and as fast as possible.

12:42I work 100-hour weeks. I've been doing that for the last three years, and I see no end to that. And I think all the leading labs are doing that. And so for anyone to catch up to the frontier, it's progressively a harder, harder problem.

12:54Andrew Ross Sorkin:What's the defensive moat around your business? And the reason I ask is I always thought that persistent memory, the idea that you would know me potentially better than somebody else, would be the thing that would keep me using one model from switching to another. But I just recently learned that actually all of this is actually quite portable. So you could actually take whatever persistent memory OpenAI has about me and actually hand it to Gemini. Yeah, so look, I think that... And vice versa. Vice versa, absolutely. I think the key is going to be the quality of the models and the capabilities.

13:26I think people can feel that when there's a differential there and it's suddenly more useful for whatever use case they have. So I think that's going to continue. Then there's the product features. But also, I do think memory and personalization is going to be somewhat sticky. and we launched our first version of personal intelligence a couple of weeks ago. And, of course, we have a whole ecosystem that you're already using, you know, Google Workspace and email and, of course, search, that we can kind of try and plug into, obviously, with the user's permission, if they want a more useful personalized assistant experience.

14:00Andrew Ross Sorkin:A lot of hand-wringing around here about jobs and what ultimately happens 10 years from now. Maybe it's five years from now. I don't know if you think, even when you look at employment numbers today, that to the extent that there's any softness, it's a function of AI and productivity being more successful. Yeah, I think it's early days. I don't think there's any real evidence yet of any change in the job market, perhaps a little bit on the entry level, maybe jobs and internships. But I think in the next five years, at least, it's gonna be more than made up for with extraordinary new opportunities these tools are gonna deliver, especially for individual creators, artists and creators and game designers, for example, that can create whole apps almost on their own.

14:42So that should, I think, if I was to advise the youth of today and graduates, Yes, what do you tell the kids? is get unbelievably proficient with the new tools. Immerse yourself in it, become native with it, and then leapfrog whatever professional ladder you're trying to get onto, leapfrog the incumbent people on that with these new skills, which are going to change the workplace. And I think there's going to be plenty of new opportunities any time there's a lot of disruption. Demis, thank you. Thank you very much. That was great.

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17:10Thank you for listening to this special Squawk Pod reports from Davos. This is only one of many, many iconic interviews from the World Economic Forum this year. I promise it's worth following Squawk Pod wherever you're listening now to catch each episode. Squawk Box is hosted by Joe Kernan, Becky Quick, and Andrew Ross Sorkin. Squawk Pod is produced by me, Cameron Costa, and Zach Valisi. Katie Kramer has been with the anchors in Davos this week. And stateside with us is Julie Trass, our editor. Have a great day.

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