[0:00:00] In 10, 15 years' time, we'll be in a kind of new golden era of discovery. That's why I hope a kind of new renaissance. We're in the thick of the AI revolution, but we might look back on January 2014 as one of the most pivotal moments in business history. That was the month that Demis Hassabis sold his AI company, DeepMind, to Google. He rebuffed a higher offer from Meta's Mark Zuckerberg, and the acquisition scared Elon Musk so much that he decided to launch a rival company with Sam Altman, now called OpenAI.
[0:00:29] Fast forward to today, and Demis is still the one to beat. He runs all of Google's AI initiatives, including Gemini, which is quickly eating away at OpenAI's user base. In his spare time, Demis won a Nobel Prize, and he runs a startup called Isomorphic that wants to solve all disease with AI. I sat down with Demis at the World Economic Forum in Davos to learn where he thinks the future is heading. I'm Alison Chantel, and this is Fortune 500, Titans and Disruptors of Industry.
[0:00:58] We'll be back with Demis after a quick message from our sponsor.
[0:01:33] ripe for automation and use of this technology to create efficiencies as well as innovation. And over time, AI agents will be also in customer-facing and growth-oriented domains. In our case, Deloitte, we're using it within our finance organization, looking at very mundane processes like expense management and working capital management. We're seeing other organizations use it in call centers and with software development that can be automated.
[0:02:01] What are the key elements for successfully implementing AI agents? It comes down to intentionality. And so I think that intentionality in going functional area by functional area in concert with business and IT leadership in an enterprise, it needs to be a mainstream business planning effort that's budgeted, that's KPIs are developed, and there's real accountability for actual business outcomes and impact because of agentic capabilities.
[0:02:30] Demis, we're here at Davos. Thank you for making the time to do this with us. It's great to be here. You've had a huge 2025. It sounds like you're gearing up for a great 2026. But before we get into both of those things, I want to just take a step back so people can get to know you a little bit better. One of the things you love is chess. Yes. You're a chess master. Yes. You also love astronomy. Yes. And I'm curious how both of those things took you into AI or shape how you think about AI. Yeah.
[0:02:59] I've always been interested in things like astronomy, cosmology, physics as a kid because I've always been interested in the big questions. So, you know, what's actually happening here in the universe, consciousness, natural consciousness, all of these types of things. So you get sort of drawn to physics if you're interested in the big questions. And then for me, for chess, I also love games, love strategy. I ended up training my own mind by playing chess as a kid very seriously. And then that got me thinking about thinking and how does the brain work. And then I've combined all that together.
[0:03:29] That sort of led me to AI and computers and AI being a way to understand our own minds, but also a perfect tool for science and understand the universe out there. Equipped with a degree in computer science and a PhD in cognitive neuroscience, Hassabis co-founded DeepMind in 2010. The company launched with an ambitious goal to solve intelligence. Under Hassabis' leadership, the DeepMind team made significant strides in its artificial intelligence models.
[0:03:54] And just four years later, Google purchased the research startup for hundreds of millions of dollars. You first started DeepMind, you co-founded it a number of years ago. And about 2014, you sold it to Google for about $500 million at the time. It was a hot deal. I know Meta wanted it too. And from my perspective, I think we're going to look back on that moment as one of the most transformative moments in business history.
[0:04:21] Google, the foundation of which to build an incredible AI machine and really take it into the future. When you look back on that, how do you feel about that moment?
[0:04:30] How did you make that decision? Did you know it was going to be such a big moment at the time? We did, actually. Those of us who were involved in the science. So it's interesting. We started DeepMind in 2010, which was 15 years plus ago now, and nobody was talking about AI. But we knew and we set out with the mission of solving intelligence and then using it to solve everything else. So we wanted to be the first company to build artificial general intelligence. And the main thing we wanted to apply it to was solving scientific problems.
[0:04:57] So when Google came along in 2014, and it was actually driven by Larry at the time, Larry Page, who was the CEO.
[0:05:03] We knew that in some ways we were sort of underselling. But on the other hand, what mattered to me was not the money. It was being able to, it was the mission and be able to accelerate our progress towards artificial general intelligence and answering these scientific questions that we were trying to solve. And I felt that teaming up with Google would accelerate that, mostly because they had obviously enormous compute power. And we see today that how important that is for developing intelligence. So at the time, I...
[0:05:32] I did mention to Larry and also the head of search at the time who was driving the deal that this would turn out to be, although it didn't look like it now, it might turn out to be the most important acquisition Google has ever done, which is saying something because they acquired YouTube and Android. They've got a good history of buying important things. And now if you go back.
[0:05:50] And you look at the origins of OpenAI, if Elon and Sam got together because they were afraid that Google might now have a monopoly in the AI space with the DeepMind acquisition. So really, that also kind of created a mega competitor at the time.
[0:06:04] Yeah, I guess there's all these sort of butterfly effects that happen. And I think partly also it was the success of things like AlphaGo, the first program to beat the world champion at the game of Go, using these kinds of learning systems that we're familiar with today, you know, reinforcement learning, deep learning at the heart of it. I think that was a big watershed moment as well in 2016. It's actually like the 10-year anniversary of that breakthrough this year. And I think that really started the starting gun for the modern AI era.
[0:06:30] Including things like, you know, OpenAI, I know the founders of that watched that match and wanted a piece of that action. Following Google's DeepMind acquisition, the company repeatedly made headlines for its accomplishments in AI. In 2015, the company's AI model, AlphaGo, became the first computer to defeat a champion Go player. And later on, it defeated top players in Chess, Stratego, and StarCraft II, which is a popular real-time strategy computer game. In 2020, Google's DeepMind AlphaFold 2 solved the protein folding problem.
[0:07:00] For decades, scientists had struggled to predict how protein sequence forms in its final structure. The model accomplished this with remarkable accuracy. The team has since scaled its process to predict over 200 million structures, all of which are now available in an online database. Hisabas received the Nobel Prize in Chemistry and a knighthood in 2024 in part for this achievement.
[0:07:21] Under Google and Alphabet, you've been able to have a lot of moonshots, take risks, try things that haven't necessarily led to money immediately, but have been profound breakthroughs. Yes. And one of them, you won a Nobel Prize. Yeah. So I was wondering, congratulations. Thank you. It's incredible. I was wondering if you could just tell me a little bit more about AlphaFold and why that's such a big deal in terms of how we could be looking at solving diseases moving ahead.
[0:07:44] Yeah, I think this is one of the benefits of being in as part of Google and Alphabet was having the resources and the time to really go after these sort of deep scientific problems. And AlphaFold, I think, is the best example of that. It's basically a solution to a 50-year-old grand challenge in biology of can you determine the 3D structure of a protein just from its amino acid sequence, basically from its genetic sequence. And this is incredibly important because proteins basically do everything in your body from...
[0:08:12] Muscles to neurons firing, everything depends on proteins. And if you know the 3D structure of a protein, what it looks like in your body, then you kind of partially know what the function it does, what it supports. Obviously, it's important also for disease because things can go wrong with proteins. They can fold in the wrong way, like in something like Alzheimer's, and then that can create a disease.
[0:08:31] So really important for drug discovery as well as fundamental biology. And AlphaFold, it was the solution to this problem that was posed 50 years ago by another Nobel Prize winner, actually, Christian Anfinsen, that this should be possible and to go directly from a kind of one-dimensional string of amino acid sequence to this 3D structure, this kind of how does it scrunch up into a ball. And AlphaFold was the solution and it was so efficient, not only is it accurate, we folded all 200 million proteins known to science and then we put that on a huge database.
[0:09:00] with the European Bioinformatics Institute and for free into the world for everyone to use. So now over 3 million researchers around the world make use of AlphaVault every day. Wow. And it's turned into, you're using some of it, I believe, for Isomorphic, which is a startup that you have.
[0:09:16] I want to say on the side. Yes. You're doing two huge jobs at once here. You've raised hundreds of millions of dollars and Google, of course, is a backer for isomorphic. Can you just explain the mission there? And you have some lofty goals. Like you say, we're going to solve all disease. Yes. You don't say cure. Yes. You say solve. Yes.
[0:09:33] Also walk me through how hard it is to get a drug to trial because that's historically been very difficult. So that was always the idea behind AlphaFold. So obviously there's a lot of fundamental science that can be done if you understand the structures of proteins, including designing new proteins that do new things. So you can sort of use AlphaFold in reverse to sort of go, okay, I want this particular shape. How do I get it from a genetic sequence?
[0:09:55] But to do drug discovery, knowing the structure of protein is only one small part of that whole process. And usually it takes like on average, you know, 10 years to go from understanding a target, you know, for a disease to all the way to a drug that's ready for in the market. So it's an enormous amount of time and cost, you know, billions of dollars, a decade or more. And most drugs, you know, fail along the way. It's only like a kind of 10% success rate. So it's just incredibly inefficient because biology is so complicated.
[0:10:23] So what I've always dreamed about doing and was the first thing I wanted to apply AI to was human health, improving human health. What could be more important use of AI? And AlphaFold was the proof point of that this could be possible. And then Isomorphic, we spun that out after AlphaFold was done. So three, four years ago to develop additional AlphaFold level.
[0:10:44] breakthroughs surrounding AlphaFold. So you can think more in the chemistry space. So if you now know the structure of a protein, you need to know where the chemical compound you're designing, the drug basically, where it's going to bind to the protein and what is it going to do. And so you need to build other AI systems that can predict all of that. So that's what we've been doing in Isomorphic. It's going incredibly well. We have great partners with Eli Lilly and Novartis, the best farmers in the world. We have like a 17 drug program.
[0:11:13] And we plan to go to, you know, eventually be hundreds. And I think this is the way to make real step change progress in human health is you basically do your search and your hypothesis searching in silico. And that's, you know, hundreds, thousands of times more efficient than doing it in a wet lab. And you save the wet lab part just for the validation step.
[0:11:35] Of course, eventually you have to test it in trials, human trials and all those types of things to make sure everything's safe. But you can do all of your search and design or almost all of it in Silico. That's the plan. And so 2026, you mentioned, is going to be a big year, I imagine, for both Google and for Isomorphic. Do you anticipate in early 2026, this could be the moment that you get the first drug to trial?
[0:11:59] Cancer? Yes. So we're working on actually several spaces, cancer, cardiovascular, immunology, and then eventually we'd like to branch out to all therapeutic areas. We're building a general drug discovery engine platform, you can think. And we are already in preclinical trials, very early stage for some cancer drugs. And then hopefully by the end of the year, if those are successful, we'll start going towards clinical trials.
[0:12:23] How do you manage yourself and your time and your teams? Because you're achieving really, really difficult things, whether it's the launch of Gemini 3, which was very successful and well-received, or it's getting drugs to trial. These are two sounding like very different things, two different teams to run. You can't be in two places at once. How are you doing this? How are you running two companies? One of my skills is bringing together amazing world-class interdisciplinary teams.
[0:12:50] I've loved managing those teams. I love composing those management teams together. And I've got incredible teams, both at Google DeepMind and at Isomorphic. And if we take Isomorphic, for example, we've blended...
[0:13:02] Top biologists and chemists, along with top machine learning and engineering. And I think there's a lot of magic happens when you have these kind of interdisciplinary groups. And then if we think about on the Google DeepMind side, there we've tried to blend together the best of the startup worlds, like what we were doing at DeepMind originally. And then at scale, you know, in a kind of multinational scale, with all the advantages of having these amazing product surfaces that we can immediately deploy, you know, technologies like Gemini 3.2.
[0:13:31] Great feedback from users and also help in everyday lives of billions of users. So it's amazingly exciting and motivating, actually. And in terms of...
[0:13:42] The way I manage my time is, you know, I don't sleep very much. Like a couple hours? Yeah, well, if you know a bit more than that, that would be bad for the brain. So I do try and get six. But I have unusual, you know, sleeping habits. I sort of manage during the day and do try and pack my day in the office with as many meetings as possible, back to back, almost no time, no break between. Then I get home, spend a little bit of time with family, have dinner, and then I sort of start a second day of work about 10 p.m. and go to 4 a.m. where I do my thinking and kind of more creative.
[0:14:11] In 2023, Google was facing increasing competition from other rapidly growing search engines and from the launch of ChatGPT. In the same year, Google pivoted to merge two of its AI teams, DeepMind and Brain, under Hasadas' leadership. And the goal was clear, jumpstart the next generation of AI.
[0:14:41] You're clearly good at motivating teams to do hard things. I know in 2023, a decision was made at Google to put two different AI teams under you. How did you work out management kinks there and get the team shipping again? Because there was this feeling.
[0:14:56] Google was a little bit asleep at the wheel for AI. And I'm curious if you think that's true and how you got them to wake up. Yeah, well, we had two world-class groups in original DeepMind and Google Brain. And actually, I think often as a collector, we don't get enough credit for the fact that, you know, I think about 90% of the modern AI industry is built on technology or discoveries made by one of those two groups, from Transformers to AlphaGo and Deep Reinforcer Learning. So we have, and we still have, I think, the deepest and...
[0:15:25] So we have an incredible talent, I think, better than anywhere else in the world by a long way.
[0:15:33] It was getting complicated having two groups, especially given the amount of compute needed in this scaling era. So that was really why we had to put the two groups together so we could pull all of the talents together working on a single project in Gemini. But also even someone like Google didn't have enough compute to have two frontier projects under one house. So we needed to combine all of our resources together. I'm a very collaborative person. I'm very open-minded about different ways of working and try to...
[0:16:02] I'm always looking to improve as well. Like one of my watch words I live by is this Japanese word kaizen that I love, which is sort of striving for continual self-improvement. And that's what I always try and do. I'm always in learning mode.
[0:16:13] Maybe perhaps that's why I like building learning machines because I like learning. And there's always something you can learn no matter how expert you are at what you do. And bringing the two groups together and trying to combine the best of both cultures has been great. And I think we're reaping the rewards of that now. And now Google DeepMind is really, the way we think about it is like the engine room of Google. So we're sort of...
[0:16:34] Powering is like the nuclear power plant that's plugged into the rest of this amazing company in Google. And I think one of the things we did is one of the things I'm very proud of is getting the shipping culture going and sort of rediscovering, I guess, the golden era of Google back 10, 15 years ago and taking risk, calculated risk, shipping things fast and being innovative.
[0:16:54] And I think that's all working out really well now, whilst at the same time being thoughtful and scientific about and rigorous about what we put out in the world, whether that's engineering or scientifically. And I think and I hope, you know, we're getting that balance right. Yeah, and you mentioned kind of going back to the golden era of Google, so much so that...
[0:17:11] The founders, at least Sergey, it seems like he's back involved. How is it like working with him on AI and doing it in Google? It's been great. And Larry is too in different ways. Larry more strategically. Sergey's been in the weeds programming away on things like Gemini. And it's been fantastic seeing them engage. Are you putting him to work or are you like, Sergey, I need this code right now? No, it's more like he chooses what to work on. But it's great seeing him in the office and pushing things in certain directions. And it's easier if the founders are kind of heavily involved. And I still act.
[0:17:40] As well, like as a co-founder of Google DeepMind, of like as a kind of founder or two, right? In terms of like what we've got to do and strategically what we pick to do. And that's something I think I've learned to do well over the last 10, 15 years is.
[0:17:53] When you have some ambitious goal like solve all disease or build AGI, what are the intermediate goals that are also very ambitious, but that are kind of waypoints? What are the right ones to pick? And I think we've done that historically pretty well with most of the alpha projects, AlphaGo, AlphaFold, and so on, and then now Gemini. And I think that's really critical, actually, for any very ambitious scientific and engineering project is breaking it down into manageable steps so that you can see you're on the right direction.
[0:18:22] And I think that we very clearly are, I think, with the technology that we're building. And it's been an incredible couple of years for us. And I think we're getting into our groove, I would say. And I think other people and the external world are starting to feel that, you know, including things like Wall Street and the share price. In 2005, any questions about whether Google was facing an innovator's dilemma when it came to AI and search were answered. After the first quarter, its share skyrocketed.
[0:18:46] Alphabet shares rose about 65% by the end of the year, marking its best performance since 2009 and making it the top performing stock among the Magnificent Seven.
[0:19:04] It definitely seems like there must have been some sort of KPI measurement charge ahead unifying moment because, I mean, the launch of Gemini 3, much fanfare among other launches that caused OpenAI to go to a code red, which they claim happens all the time.
[0:19:17] Okay, sure. And then, you know, you have this huge monster deal with Apple that is monumental, I think, for the industry. So I'm curious, what happened internally behind the scenes? How did you set those KPIs for the team? And then how are you setting them to keep the momentum in 2026? Well, look, I think for me, it always starts with the research, like having the best models in this case, and obviously fundamental research feeding into that.
[0:19:40] And I always believe you then need to reflect that, obviously, as quickly as possible in your products. And then you've got to get your marketing distribution right. But none of it matters if your models aren't best in class and state of the art.
[0:19:52] And so that's what we focused on first with the Gemini models, but also our other models, things like Nanobanana, our image model, which went super viral. And that was a big part of their success last year. Our video model, VO, our world models. So there's more than just large language models. And we're kind of, you know, state of the art on all of those. And then it was about sorting things out internally, almost rebuilding the infrastructure in some way of Google so that you could reflect.
[0:20:16] Very quickly, the power of the latest models into the Lighthouse products, including Search, YouTube, and Chrome, all these amazing surfaces that we have, as well, of course, as the Gemini app. It was new for everyone in the industry. And I think it takes a little while to kind of re-architect things around that. And very much, again, this idea of Google DeepMind being the engine room, providing the engine for the rest of the organization to use. And I think that took...
[0:20:43] A year, 18 months to get right. But I think we're seeing the results of that now. And I think there's still more to go, by the way. And we can have even faster velocity. And I think the other thing is just also instilling this culture of intensity and pace and focus and really focusing only on the things that matter and kind of cutting out distractions.
[0:21:03] And then maybe the final thing I would say is I think there's a lot to say, especially in today's very noisy world, to just consistently deliver good decisions, good rational decisions, and over time, minimal drama. And then I think it's just amazing how much that compounds over time. And, you know, I think we're building a lot of momentum now. And I think hopefully we'll see that even more this year. Sort of like we mentioned before, the decision to sell DeepMind to Google was a monumental moment, transformational moment in business.
[0:21:32] If you're successful now, I think that will be perhaps the biggest transformation in business. How does that weigh on you to make sure that you as a leader are driving this in a direction that's good for society, good for the workforce, good for Google? Because it is a little bit of an innovator's dilemma where this is the search king, huge business model based on ads. If you're successful, I don't know.
[0:21:57] Sure. Well, look, I mean, it is a classic innovator dilemma. I think we've navigated it pretty well so far and search is more successful than ever. But also there's this aspect of like, if we don't disrupt ourselves, someone else will. So you're better off sort of being ahead of that, I think, and kind of doing it on your terms. And so I think that's what we found. In terms of responsibility, I feel that, you know, I've felt that way since.
[0:22:23] Not just at Google, but before that at DeepMind and before that even in my academic career, because if we, you know, myself and Shane, especially our chief scientists, you know, when we started DeepMind, it seemed like a fanciful idea, but we really believed that it would be possible to create artificial general intelligence. And we understood.
[0:22:40] What I think more and more people are understanding now is how transformative to the world that would be. But also, of course, amazing for things like science and human health and maybe helping with energy and so on. But also there are risks. It's a dual purpose technology. You know, harmful actors, you know, bad actors could use it for harmful ends. And eventually as AI technology becomes more autonomous, more agentic, and we get towards AGI, there's technological risk too.
[0:23:06] And so I worry a lot about all of those things. And I also, of course, we have to make sure that the engine and the economic engine works as well. So we have enough money to fund our research and fund things like AlphaFold and give it to the world for free. That's not easy. It costs a lot of money to create something like and hire the researchers to create something like AlphaFold. But we do a lot of things like that. And I want to do more things like that for the world. But that requires us to be successful also on the commercial side. So I think there's a balance to be had there. But the responsibility...
[0:23:35] I think part comes as well, and I feel like we can do this at Google as well, is we have the platform to show how AI can be deployed in a responsible way and a beneficial way for all of society.
[0:23:48] And all of us who are frontier labs producing AI, we have choices about what should we use AI for. Are we going to use it for things like medicine and for alleviating administration and helping with things like poverty? Or are we going to use it for exploitative things? And I think that we're going to try and be a role model for all the good things that can come with AI. It doesn't mean we won't make any mistakes. We will do because it's such a nascent and complex technology. But we'll try and be as thoughtful as possible as we can.
[0:24:17] I think it's going to matter here a lot. I mean, it's a scientific endeavor in the end. And then, you know, I hope that what we...
[0:24:30] The kind of reliability and security and safety that we like to work on will come through in our products. And then I think the market will reward that because if you think about enterprises that use these technologies as they get more sophisticated, they're going to want to know if you're a big bank or insurance company, whatever, a health company, a medical company, that you have some guarantees about what your AI systems that you're bringing in are going to do.
[0:24:59] So I think that could be a good aspect of AI becoming very commercial is that there'll be sort of commercial incentives to be robust and reliable and secure and all the things that you'd want actually in preparation for AGI coming into the world.
[0:25:14] So when you look at the year ahead, what do you think the story of AI will be? What will we achieve? Well, I think this year, I mean, I say this every year, every year is pretty pivotal in AI. And it feels like, at least for those of us inside, working at the coalface, you know, like 10 years almost happens every year. And I think this year will be no different. It's very intense. But you've also got to kind of every now and again look up at the strategic picture. I think that...
[0:25:40] At least for us with Gemini 3, we crossed the watershed moment, in my opinion. And hopefully those of you who've used it will feel that in that it's very capable now. And I'm certainly using it in my everyday life to help me with my research and summarizing things and doing some coding. So I think that these systems are now ready to maybe build agents. The whole industry has talked a lot about agents and more autonomous systems and delegating whole tasks to them. But I think maybe by the end of this year, we'll really start seeing that.
[0:26:09] I'm very excited about assistance coming into the real world with you, maybe on glasses. We have a big project on smart glasses. I think that the AI technology is only just about there to make that actually viable. And I think that could be a kind of killer app for glasses. You know, I think that...
[0:26:27] Bringing that into the world. Also robotics, I think is going to, I still think there's more research to be done on robotics, but I think over the next 18 months or so, I think we're going to see kind of breakthrough moment in robotics too. So all of these areas we're pushing very hard on as well as of course, improving Gemini itself.
[0:26:42] Those aren't the glasses, right? No, they're not. I will buy those if they were. I was going to ask you about the future form of how computers were not built for AI and all the things that AI can do. What do you think is the future for us? Sounds like glasses. I think glasses would just be one of the solutions. I have this side notion of, we talk about this notion internally of a universal assistant. And what we mean by that is an assistant that's super helpful in everyday life, recommending new things, enriching your life, and dealing with admin.
[0:27:12] All of these types of things. But it goes across all the surfaces. So it's on your computer, on your browser, on your phone. And then I think there'll be new devices too, like glasses. And it will be the same assistant that kind of understands your context across the different conversations you've had, whether that's in your car or in your office. And if you want it to, you know, that can all be integrated together. And I think help you, you know, improve your life across all those different aspects of your life. Maybe for Christmas next year or the holidays.
[0:27:41] Next year, we can all get our Google glasses. That's the idea. You were just way too early, I think, before when they kind of looked a little ugly. I think, you know, and like a lot of things we've done at Google, we maybe were, you know, we pioneered all these spaces, perhaps a little bit too early in hindsight with glasses, both the technology of making them not too chunky and things, but also I think it was missing the killer app. And I think an AI digital assistant could be that.
[0:28:04] Yeah, amazing. Well, just one last question for you. I want to ask your biggest, boldest prediction for how AI will transform the world. When you look ahead, I know you said 10 years is one year now. Yes. It's true. Yes. But when you're looking ahead, are you...
[0:28:18] Like the abundance world where AI can solve all of our problems. Like what does it look like? I think done right, we will be in an incredible, you know, in 10, 15 years time, we'll be in a kind of new golden era of discovery. That's why I hope a kind of new renaissance. And I think human health will be revolutionized. It won't, medicine won't look like it does today. I think that personalized medicine, for example, will be a real, a reality. And I think we'll have solved, used these AI technologies to solve many big problems in science and things like new material.
[0:28:47] Maybe help with fusion or solar or optimal batteries, some way of solving the energy crisis. And then I think we'll be in a world of radical abundance where we can use those energy sources to travel the stars and explore the galaxy. That's what I think our destiny is going to be. Amazing. Well, thank you. I hope that that's what you build and thank you for all your efforts on it. Great to talk to you. Bye-bye.