"I've Been Driven My Whole Life to Build AI as the Ultimate Tool"

Demis Hassabis, a 2024 Nobel laureate, is one of the most important AI scientists in the world. Here, he explains what is next for AI – and what he thinks of DeepSeek.

Interview: Jochen Wegner und Uwe Jean Heuser

Aus der ZEIT Nr. 05/2025

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In 2024, AI researcher Demis Hassabis was awarded the Nobel Prize in Chemistry. His company DeepMind, now integrated into Google, first attracted attention by designing a program that defeated the best Go player in the world. His ultimate goal, however, is the theory of everything. A conversation about realistic dreams and their risks

DIE ZEIT: Mr. Hassabis, what is your personal definition of intelligence?

Demis Hassabis: If you mean artificial general intelligence or AGI, it is a system that is able to learn for itself how to accomplish tasks, a system that exhibits all the cognitive capabilities that humans possess as well. And that is important, because the only existing proof of it is the human mind.

ZEIT: The human brain developed consciousness. Will machines do the same?

Demis Hassabis, 48

is a computer scientist and neuroscientist in addition to being a software and game developer – and a leading scientist in the field of artificial intelligence. In 2010, the London-based researcher founded DeepMind Technologies, which was acquired by Google in 2014. In 2020, Hassabis and a U.S. chemist were able to use AI software to predict the structure of the resulting proteins from gene data, which can accelerate the production of drugs, among other things. They were awarded the Nobel Prize in chemistry in 2024.

Hassabis: My guess is that you can have intelligent systems that are not conscious, and vice versa. A lot of animals dream and probably have some form of consciousness, but they're not that smart. I have always thought that this journey we are on to build AGI would tell us a lot about consciousness, because you would be building an intelligent artifact, and we would then be able to compare it to the human brain and see the differences in a controlled way. If there's a choice, I would recommend that we first build intelligent machines that are not conscious, because consciousness comes with moral problems and other risks – autonomous systems that want to do their own thing. But it may turn out that you cannot build intelligent systems of that level without some form of consciousness.

ZEIT: How close is AGI to becoming reality?

Hassabis: I would say there is a 50 percent chance in the next five years. I would be pretty surprised if it took more than 10 years.

ZEIT: How will it be possible to know when AGI has been achieved?

Hassabis: One way would be to perform tens of thousands of cognitive tests, from psychology, from neuroscience, things that humans can do and that involve different skills or different capabilities. If you have a consistent level across the board, then the more tests you add, the more certain you can be that you have general intelligence.

Demis Hassabis: "I've Been Driven My Whole Life to Build AI as the Ultimate Tool"

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ZEIT: Late last year, progress was made towards AGI in the form of the o3 model of Open AI which called it the "next phase". It did very well with a formal AGI test, the ARC, which is about abstract reasoning. Is that a breakthrough?

Hassabis: First off, it was not an AGI test. It is a pretty big misnomer.

ZEIT: That's what it is called.

Hassabis: Sure. I can just write something on a piece of paper and call it AGI. Does that make it an AGI test? No. I do not know why they did that. It is good that people are building benchmarks. But this is a rather mundane puzzle, nothing more. Great, but then to muddy the waters by adding AGI was mocking it, I felt. That does not have a real belonging in rigorous science. Then journalists get confused, the general public gets confused. And second, we just released a new version of our thinking model that is very competitive with o3. You should know that we pioneered that line of work.

ZEIT: Is language alone sufficient for an AI to develop an understanding of our world, or do we need to incorporate many other forms of data?

Hassabis: If we were talking five years ago, I would have told you: There are a lot of theories in neuroscience, in which I did my PhD, which hold that you need to act in the world in order to perceive it. There's a whole branch of neuroscience called Action in Perception: It's very hard to understand the notion of weight by just looking at something, you normally need to pick it up and feel it. But it turns out there is actually a lot more – we call it "grounding" – in words and language than even linguists thought. It just turns out that our language is so rich. If you read the entire internet and all of Wikipedia, you will actually get quite a lot of grounding about the real world. The other source of grounding, which we didn't really foresee, is the feedback loop. When you train a model you test it on people and they give a thumbs up or a thumbs down, and then you retrain to get a more useful answer. We are all grounded agents because we are embodied intelligence systems in the real world. By giving feedback to a model, you're giving some of that groundedness to the system. Finally, we have now begun working with multimodal models for videos and images and other things. These systems have to understand videos. You need a real understanding of the world to do that.

Demis Hassabis: Demis Hassabis in his lab in London 2016

Demis Hassabis in his lab in London 2016 © Immo Klink

ZEIT: In hindsight, was the massive reaction to the first version of ChatGPT back in 2022 just hype?

Hassabis: No, I think it was an important threshold moment. It was surprising to all of us, even to OpenAI. They would have called it something catchier if they had known it was going to go viral. We at Google and DeepMind had similar systems. We had our own language models. And when you were using them, before they had been fully post-trained, they were not that interesting to talk to. The other thing is: We thought they hallucinated a lot, 10 percent of the time. How useful is that? Surely, we need to get that down to 0.1 percent. What was surprising about that release was that even then, people found it valuable for certain things like summarizing information or writing mundane letters. How surprising that it was good enough to be of interest to the general public. It was a threshold moment for AI crossing over into the mainstream.

ZEIT: With ChatGPT, did OpenAI publish an immature model? Or was Google simply being too late – or too responsible?

Hassabis: I wasn't involved in that decision because it was made on the Google side. We were just doing research on large language models on the DeepMind side. But I can see why they had faced a dilemma. Google is known for super accurate information, that is the whole point of Google. Having something that hallucinates a bit is not great.

ZEIT: Is this a race for a monopoly or will we see many competitors?

Hassabis: There are lots of competitors for the base models right now. I think there are at least five or six leading players, perhaps three at the top – ourselves, Anthropic and Open AI. Then there's the Chinese: DeepSeek just came out, a surprisingly impressive model. I don't know how much it relied on Western models, but even still, it’s very impressive. They're definitely the best team I have seen coming out of China. So, I think it is going to be multipolar. The real race is for AGI: Can you get to the next level? And then everyone's trying to explore the kinds of products that can be built on that. I would point you to Google’s project NotebookLM. It generates a podcast out of any information you put in. It is pretty fascinating that you can do that and it is a new way of interacting with information. I would have thought the priority would be to have an AI system summarize loads of documents in an area and then just read the summary to you. But it turns out, it's much more interesting if there are two AIs arguing and critiquing just like in a podcast.

ZEIT: We’re slowly starting to worry about our job security as journalists. Does this mark the real step from the industrial age to the intelligent age? It wasn't the computer, it wasn't the internet, it is AI?

Hassabis: Yes, I think so. Of course it needed all of those things. It needed the computer and the internet, especially given the way we ended up training these systems.

ZEIT: So right now this is the big leap?

Hassabis: Yes, and in my mind, it always was. I somehow intuited this when I was a kid – maybe too much science fiction. I knew this was going to be the biggest thing, which is why I have spent my whole life on it. And even though I have spent so much time with it, I still find it surprising that it has all turned out this way.

Demis Hassabis: Demis Hassabis founded DeepMind in 2010. The company was acquired by Google in 2014.

Demis Hassabis founded DeepMind in 2010. The company was acquired by Google in 2014. © Carlotta Cardana

ZEIT: In 2023, you and many other leading researchers, including OpenAI’s CEO Sam Altman, signed an open letter warning of the danger that humanity could go extinct in the AI age. Is that really a possibility?

Hassabis: I signed that because I wanted to make sure that people who were worried about such an eventuality had a time window to discuss it. There are a lot of other people who say there is nothing to worry about – which I think is kind of crazy when you are confronted with a massively transformative technology that is bigger than the industrial revolution and we don't have a full understanding of these systems. There is risk in that. It may turn out that we are lucky and it is easy to fix the technical issues, but we don't know.

ZEIT: Which risks do you see?

Hassabis: I think there are two big risks. One is bad actors repurposing these general technologies for harmful acts. And those bad actors can be individuals or rogue nations. How do you accelerate all the benefits with open source – but at the same time restrict access to would-be harmful actors? That’s an unresolved question. Second is the technical risk inherent in AGI. As these systems become more self-improving, we will see wonderfully powerful systems for good things such as curing diseases and helping with the climate. I think we will get all the things we want as a society from these technologies. But they are inherently risky. What if there are unintended consequences like they change their goals? Can they be controlled if they are smarter than us? There is a technical component to that. Can you build the right types of designs? Can you build the right monitoring tools? That is hard enough. But even harder, I think, is the international or geopolitical environment we're in, where you need cooperation. It's no use if some countries or some companies build safe architectures, but then others build unsafe ones. Because all these systems will affect the globe.

ZEIT: Do you build kill switches into your AIs?

Hassabis: As science fiction authors have written, kill switches are not a solution. The whole point of AGI systems is they could get around it, or they would resist it.

ZEIT: We could burn the racks full of the NVIDIA hardware necessary for AI.

Hassabis (laughs): You could. But if it was sufficiently smart, it would predict ahead of time what you were going to do and would protect itself.

ZEIT: Should we stop developing these things?

Hassabis: We should not do that. There are many economic reasons why people develop these systems. And the history of science shows that if something is possible, humans will build it. We're insatiably curious. And we want all the positive aspects of it. We want to cure diseases. We want help with the climate.

ZEIT: You built AlphaGo, which was the first program able to defeat the best human player in a game far more complex than chess. The follow-up, AlphaGo Zero, taught itself to play Go from scratch and was even better than AlphaGo. And it no longer required human training data. Do you think it’s possible to eliminate the need for training data in other fields as well? Hassabis: Potentially. We're experimenting with synthetic data creation. Obviously, AlphaGo Zero was our first attempt, a very successful attempt. I think there's a lot of potential, like self-improvement and synthetic data generation. But I think it is going to be a lot harder with world models.

ZEIT: Couldn’t we just run the basic laws of physics on a quantum computer one day and have an artificial universe?

Hassabis: Potentially. I said this in my Nobel acceptance speech, actually: It may turn out that most of the things in nature, even the quantum systems, are modellable on a classical AI system, which would be quite intriguing. It’s possible we could simulate some physics on it, but you might even be able to do that with an AI system, just like we did with protein folding. In effect, this is also about solving a quantum problem. Proteins at the atomic level have quantum effects, and somehow, we can still predict the structure.

Demis Hassabis: Chemistry Laureates Demis Hassabis, David Baker and John Jumpe arriving at the banquet for Nobel laureates, hosted by the king at the Royal Palace in Sweden, in November 2024.

Chemistry Laureates Demis Hassabis, David Baker and John Jumpe arriving at the banquet for Nobel laureates, hosted by the king at the Royal Palace in Sweden, in November 2024. © Henrik Montgomery/TT/imago images

ZEIT: Last year you received the Nobel Prize for the prediction of protein structures with AI. Are the life sciences turning into computer science?

Hassabis: In a big way. My wife is an Alzheimer's researcher, so she's the real biologist in the family. We talk a lot about this.

ZEIT: What a family!

Hassabis (laughs): If you take physics, mathematics is the perfect descriptive language for [it]. Newton's laws of motion, Kepler's laws, general relativity: All of these things are beautifully described in elegant equations. But biology is an interactive emergent system, far too complex to describe with a few equations. AI is actually the right analysis approach for such a system – messy, interactive, lots of weak signals, and then something emergent happens out of it. I think AI is the perfect descriptive language for biology.

ZEIT: What other scientific fields do you think AI will revolutionize?

Hassabis: It would be shorter to list the ones that won’t be. Even at DeepMind we're working on many fields: quantum chemistry, material science, plasma fusion reactors. And then there is weather prediction. We do a lot of work in mathematics, proving interesting conjectures. I think we are close to solving a Millenium Prize Problem –  we'll see that in the next year or year and a half. (Editor’s note: The Millennium Prize Problems are seven complex mathematical problems selected by the Clay Mathematics Institute in 2000. A prize of one million US dollars is offered for each solution.) And we have now extended our efforts into drug discovery with our Isomorphic Labs. This involves not just the protein structure, but if you can design compounds that bind to the right part of the protein but don't bind to anything else, you won’t have any side effects.

ZEIT: In the tournament where AlphaGo defeated the best Go player in the world, there was the legendary move 37 …

Hassabis: … yes, in the second game …

ZEIT: … which defied human wisdom. Will AI help us to ask radically new questions in other fields?

Hassabis: Yes, move 37 was maybe the first example of that ever in AI. It wasn't just that we won that match. It was because AlphaGo made these creative moves. That was, for me, the most surprising and beautiful thing about it. I was very excited to see that because that kind of new discovery in science would equate to a huge breakthrough. And for Go, of course, it was revolutionary. This game has been played for thousands of years, and no one had ever thought of something like that. It was beautiful. It showed that these systems are capable of discovering new knowledge.

ZEIT: The famous physicist Stephen Hawking once warned of the threats posed by AI,  as have you. What is your red line that you would not cross with Google?

Hassabis: It is not just about Google which is responsible maybe to a fault in terms of slowing down releases. One issue is market dynamics and competitive imaginations which create unhelpful incentives – obviously a case for government to help out, but it needs to be international. Right now, the systems are not very dangerous in my view because they are not active in the world. They are passive. You put your intent in the question, and it gives you an answer. So the threshold for me has always been advanced, generalized agents that could operate through world models, not just games. And maybe we are only two, three years from that kind of system. If that started coming on the horizon, things would start becoming very serious.

ZEIT: What are the unresolved questions that keep you awake at night?

Hassabis: There are so many. I’ve been driven my whole life to build AI as the ultimate tool for sciences because I want to understand the deepest mysteries of physics, of the universe. That is what has always obsessed me, intrigued me, maybe even haunted me. We don't understand anything about the nature of reality really. We have some approximations, but they have holes in them. Quantum mechanics is a little bit strange in many ways. We don't know what the nature of time is. I don't understand why people aren’t more troubled by this.

ZEIT: Do you believe AI will provide us the elusive Grand Unified Theory of physics one day?

Hassabis: I think so. When I have an AGI, that is what I want to do with it.

ZEIT: And it will bring us back to before the beginning of time?

Hassabis: Perhaps. Maybe we’ll start to ‘understand the nature of reality’. My Twitter bio says this for a reason. I really mean it.

ZEIT: Will most of the future Nobel prizes be won by AI projects?

Hassabis: I think AI will be more involved. I think for the next decade or more, human ingenuity will still be required to come up with the theory. And then very smart people will use AI to help them solve it. I think that's going to be the next phase. I hope we'll look back in 10 years and see this next decade as a golden era of new discovery, in part powered by AI.

ZEIT: Your first startup was a gaming company, and you are an international chess master. Do you still play games?

Hassabis: Only with my kids. We used to play many German board games, like Settlers. But they are teenagers now, so we play more computer games.

English editing: Charles Hawley, Angela Holz

This interview has been edited and condensed for length and clarity.