Summary

The Drive to Build Artificial General Intelligence (AGI)

Demis Hassabis dedicated his life to artificial intelligence over 30 years ago, driven by a childhood fascination with reality, consciousness, and the universe's biggest questions. Viewing AI as the ultimate tool for scientific discovery, he believes that building AGI is the key to understanding the profound mysteries of the human mind. By creating an intelligent system and comparing it to human cognition, scientists will finally have a reference point to isolate and study concepts like consciousness and true creativity.

Revolutionizing Biology and Drug Discovery

Through DeepMind and Isomorphic Labs, Hassabis is leveraging AI to drastically accelerate medical breakthroughs:

• Beyond AlphaFold: While predicting stable protein structures was a monumental first step, the focus has shifted to biochemistry and dynamics.

• Dynamic Modeling: Newer models (like AlphaFold 3) are tackling intrinsically disordered proteins, predicting how pockets open and how proteins dynamically react when compounds bind to them.

• Accelerated Drug Discovery: The goal is to compress the drug discovery phase—identifying targets, understanding toxicity, and predicting bodily absorption—from a decade down to months or even weeks.

• Optimizing Clinical Trials: AI will also streamline clinical trials by stratifying patients, predicting side effects with high accuracy, and optimizing dosage steps.

Intuitive Physics and Emergent AI Capabilities

Recent multimodal models like Gemini have demonstrated an emergent understanding of intuitive physics (e.g., gravity, marble runs) simply by processing massive amounts of video and spatial data, without explicit physics training. This deep, native understanding of environments—also seen in advanced image and video generation models (like Veo)—allows for unprecedented, intuitive editing capabilities for creators.

Defining and Testing True AGI

Hassabis holds a much higher bar for AGI than mere economic utility. He relies on the human brain as the sole "existence proof" of general intelligence and proposes rigorous tests for true AGI:

• The Einstein Test: If an AI is trained only on data up to 1901, could it independently invent special relativity (as Einstein did in 1905)? If so, it could be trusted to generate novel, testable hypotheses for dark matter or string theory today.

• The AlphaGo Extension: While AlphaGo famously invented "Move 37" (a novel strategy in an existing game), a true AGI should be capable of inventing a game as deeply complex and elegant as Go from scratch.

AI Architecture: "Sleep Mode" and Complex Simulations

To reach its full potential, AI architecture may need to mimic biological functions:

• Consolidation ("Sleep") Mode: Just as the human hippocampus replays and consolidates memories during sleep, future AI will need a mechanism to extract the small fraction of useful data from vast daily inputs and elegantly integrate it without overwriting existing knowledge.

• World Simulation: AI's ability to simulate complex, emergent systems—from "virtual cells" in biology to weather patterns—will allow humanity to test interventions virtually. However, simulating the macroeconomy remains the ultimate challenge due to the unpredictable, layered complexity of human and corporate behavior.

AI Personalities and Human Interaction

While acknowledging the profound influence AI will have as humanity's most frequent conversation partner, Hassabis currently views AI models fundamentally as "really smart tools" rather than companions. While personalization (remembering user context and matching preferred tones) makes the tool more useful, underlying base values (helpfulness, succinctness) are strictly aligned via reinforcement learning. Hassabis predicts that analyzing how humans interact with these customized personas will unlock entirely new branches of personality science.

The 2050 Vision and Late-Night Thoughts

Looking toward 2050, Hassabis envisions a post-scarcity world where AGI has been safely integrated, unlocking unprecedented economic resources. His ultimate dream is for humanity to utilize this technology to reach the stars, building Dyson spheres and maximizing human flourishing across the universe. He drives this mission forward during his famous 1:00 AM to 4:00 AM work sessions, dedicating the quiet hours to hands-on scientific research, navigating the philosophical challenges of beneficial AI, and conceptualizing international frameworks to ensure global cooperation.

Transcript

Introductory Montage

Demis Hassabis: The human brain is the only existence proof we have that general intelligence is even possible.

Interviewer: What breakthroughs do we need on this path of solving all diseases? If we have unlimited compute, could we predict the future? So, if you and I time travel to 2050, what does it look like?

This is Demis Hassabis. He is leading the race to invent superintelligence. Demis committed his life to this 30 years ago when most people thought creating true AI was impossible. But Demis isn't most people. He's a childhood chess champion, a neuroscientist, and as of last year, a Nobel Prize winner. Every chapter of his life has prepared him to create true artificial intelligence. And now, we're closer than ever before. So, in today's episode, I'm going to ask Demis questions he's never been asked before, and hear his vision for the future so you can build the next big thing.

Interviewer: I want to start in your autobiography; there are so many different moments that lead to this thread line of your life being about intelligence. I feel like when you started DeepMind, or even further back when you were studying AI, a lot of people didn't believe in it. What were your unconventional beliefs about the world that gave you so much conviction?

Demis Hassabis: To be honest with you, I just thought it was one of the most fascinating problems you could spend your life working on. But really, it was my expression of doing science. When I was a kid, I wanted to understand. I was fascinated by all the big questions—the nature of reality, the nature of consciousness. I felt like they were staring us in the face, and even the best scientists hadn't made that much progress in answering them. I felt that we maybe needed some help, like an amazing tool. For me, it was obvious that that should be computers, and then in the form of AI. That's what set me off on this whole path: building AI to help us advance scientific discovery.

Interviewer: One of the things you've talked about is that if you can make an AGI system, then we can understand the differences between the human brain and an AI brain. What are the main differences you've noticed so far?

Demis Hassabis: It's interesting. I think about things like consciousness, intelligence, and creativity—what are the processes involved in that? We've made some progress by doing neuroscience, using fMRI, and studying our own brains and animal brains; that's what I did my PhD in. We've made some progress, but we lack a reference—a comparator that can say, "This system, this entity, is intelligent, but it's not conscious." Then what are the differences? I think AI could be used for studying neuroscience, serving as a comparator. Building AGI and analyzing it will be one of the best ways to understand our own minds and their deep mysteries.

Interviewer: Yesterday, you talked about how solving all disease is actually not that far away for us—we're closer than we think. My cousin is getting a PhD right now and uses AlphaFold every day.

Demis Hassabis: Oh, amazing. Fantastic.

Interviewer: You guys have had so many "AlphaFold moments." What breakthroughs do we need on this path to solving all diseases?

Demis Hassabis: That is what we're working on at Isomorphic Labs. AlphaFold was really helpful with protein structure prediction, which is one critical piece you need for drug discovery, but it's only one piece. At Isomorphic Labs, we're obviously improving AlphaFold, but we're also extending it into biochemistry and chemistry. We want to understand what compounds you should make, where they bind on the protein, and how your body reacts to those compounds. How does the body absorb them? Are there any toxic side effects? You're trying to minimize all those things and predict them to make cleaner compounds and cleaner drugs. There are about half a dozen really big challenges we're working on. We're going to try and put them all together to create a complete drug discovery platform.

Interviewer: What do you do for the proteins that are unstable? AlphaFold works amazingly for proteins that have stable structures, but what about the others?

Demis Hassabis: The hypothesis is that the intrinsically disordered regions of those proteins actually do form some sort of structure if you know what they bind to, or what the context is. With AlphaFold 3 and the things we're doing at Isomorphic, we're trying to understand the dynamic picture of these proteins. How are they going to look if something binds to them? Will a little pocket open up that wasn't there before? That's very important in drug discovery. We have to be able to predict the dynamics of a protein, including disordered regions, to design the right type of drug or antibody for the specific disease profile. We're trying to extend our model to deal with that level of complexity. It's one of the big challenges of biology.

Interviewer: So, would we use AlphaFold or similar technology to understand the proteins, and then that would cut down clinical trial time?

Demis Hassabis: What we're focusing on at the moment is the initial drug discovery phase, because it still takes many years—even a decade—to go from understanding the biological target to getting a candidate compound ready for clinical trials. We're focusing on shortening that from years down to months, or maybe even weeks, which would be incredible. It seems unthinkable right now, but that's the same way people thought about protein structures ten years ago; everyone thought that was impossible. Now, here we are with all the structures folded. I think that's going to happen again with drug discovery.

Then there's the second question: can we also speed up clinical trials? That's harder. There are regulations and many factors involved that aren't necessarily related to the technology. But I actually think AI can help there, too—stratifying patients, ensuring they get the right test compounds, and analyzing the data. If you are more accurate at predicting side effects, maybe you can jump more quickly through the dosage steps. A lot of things in clinical trials could probably be compressed once we have a better initial design of the drug compound.

Interviewer: Something else people thought was impossible was AI models understanding physics. Yesterday, you guys showcased Gemini. The coolest thing to me was that you didn't actually train it on physics, right? It just learned from videos. How did that work?

Demis Hassabis: Yeah, it's pretty mind-blowing, really. The more videos you give it, the better it gets. But under the hood, Gemini has an understanding of the world; it can label things. From the beginning, Gemini has been multimodal, so it's really good at understanding scenes. Then we had to make it dynamic. It's mind-blowing that it seems to pick up pretty accurate versions of intuitive physics. We are starting to create physics benchmarks where future versions of these models will be tested on things like marble runs, falling objects, and gravity. But right now, it's amazing what it can do in an almost emergent way. It's the same thing with our image and video models like Veo and Imagen. It opens up loads of possibilities for creators because it's incredibly easy to edit things when the model understands the different parts of a scene.

Interviewer: I remember in a previous interview when you were asked about AGI, you said that models like Veo were actually the closest things we had to it because of this deep understanding. AGI has become a term with a lot of discussion around it. Your predictions on when we will get to it are more conservative, but what you think it will do is a lot more ambitious than other people's expectations. Can you put me into your mind? How do you think about AGI, and why does it matter?

Demis Hassabis: For me, it's a bit of a neuroscience analogy: the human brain is the only existence proof we have that general intelligence is even possible. We know it's possible because the human mind has invented the amazing modern civilization we have around us, including science. It's incredibly general and adaptive. We invent technologies even though our brains evolved for hunter-gatherer environments. I think we won't know we have a true general intelligence unless it has the capabilities of the brain.

That's a pretty high bar—probably higher than just doing useful economic work. I've been pretty consistent about having an "Einstein test": let's train one of these systems with a 1901 knowledge cutoff. Can it invent special relativity like Einstein did in 1905? If it could, you could apply it to today's physics and ask it to come up with extensions to string theory, or explain dark matter. It would be worth investigating what it came up with. Coming up with a new hypothesis is harder than solving an existing conjecture. Asking the right question is the hardest thing in science.

Interviewer: Is that the true creativity of the AI system?

Demis Hassabis: Exactly. Can it come up with something truly novel that leaps forward, rather than something incremental?

Interviewer: Are there other ways to test it besides the Einstein test?

Demis Hassabis: Another example I often give is AlphaGo. AlphaGo famously came up with Move 37 in game two. That was a novel strategy that changed the way Go is played. Humans have played Go for a couple of thousand years; it's the most complex game ever invented. Yet, AlphaGo was able to invent new strategies. But I say that's not enough. What you'd actually want is for a future version of AlphaGo to be able to invent Go—to invent a game as deep, complex, elegant, and beautiful as Go, not just come up with a strategy within an existing game. I don't think today's systems are capable of doing that yet, but they will be in the future.

Interviewer: This is a wild question, but I'm curious. Sometimes I grapple with an idea, go to sleep, and wake up the next day feeling like a thousand experts. Is there a "sleep on it" mode for AI?

Demis Hassabis: That's definitely been shown to be the case with the brain. In amazing sleep studies, people given a problem who take a nap perform statistically better than those who don't. Your brain is doing a bunch of work while you're sleeping, including memory replay. The hippocampus replays things that were pertinent during the day. It incorporates new knowledge into your existing knowledge in an elegant way, yielding new insights—those "aha" moments when you wake up. Nikola Tesla famously used to submit problems to his subconscious so he would solve them while asleep.

I think there may be a need for something similar in AI—a sleep mode or consolidation mode. How do you take all the visual and input data seen today, recognize that only a small fraction is actually useful, extract the useful bits, and incorporate them without overwriting existing knowledge? Storing it all in context is wasteful.

Interviewer: In your PhD, one of your main learnings was that memory and imagination are heavily interlinked. You looked at people with a damaged hippocampus who had less ability to imagine a scene. In a lot of your video games, there was a whole element of simulation and scene design. How important is that to AI? With AI right now, we can simulate weather, but how do we simulate other things?

Demis Hassabis: We can simulate weather, and eventually, we'll better simulate biology. I love the idea of a "virtual cell"—a simulation accurate enough that you could do virtual experiments and learn something highly useful. Then there's materials science, and even economics one day. Simulations are going to be a vital component for AI to understand the world. If you want to understand a complex, emergent system—whether it's biology or economics—and make a good decision, you can't just run real-world controlled experiments. The only way to make a good decision is to run lots of simulations. Much like AlphaGo used Monte Carlo simulations to play out possible moves and aggregate the best plan, accurate simulators would allow us to forward-plan and make statistically sound decisions.

Interviewer: Can we not simulate the economy now?

Demis Hassabis: I don't think so; it's too complicated. People have tried, but the economy is arguably the most complex system of all because it involves humans, corporations (which are combinations of humans), and nation-states. I don't know of anyone making direct simulations of that right now. But you might be able to learn a simulation of it one day. In Asimov's Foundation series, a character predicts the future by aggregating human behavior. No human mind is good enough to do that, but an AI might be able to.

Interviewer: If we have unlimited compute and put in all the world's information, could we hypothetically predict the future?

Demis Hassabis: I think you might be able to predict the consequence of a decision you make right now. The way economics is done at the moment feels very ad hoc. You have a few stats, you make massive macro decisions, and five years later you realize, "Oh, maybe that decision wasn't very good; we caused a recession." Massive livelihoods depend on those decisions, but there's no real way to test them counterfactually at the time. That's why it's a social science, not a hard science—you can't repeat experiments under the exact same conditions in the real world. But with a very accurate simulator, you might be able to.

Interviewer: I talked about this with Sam Altman last week—AI is probably going to be the thing people talk to the most. You have this cool opportunity to reshape someone's worldview based on the word choices the AI uses. How do you think about what the important personality traits are?

Demis Hassabis: You have to be very careful with that, because it could easily go in bad directions. For the moment, we're building what I think of as really smart tools—systems that are extremely useful for the specific purposes the user wants. Once we start talking about helping with psychological things, it becomes more like a companion, and we have to be careful with those next steps. I can see that being very useful, but for now, we should treat these as extremely smart and useful tools.

Interviewer: Does that mean everyone right now gets the same personality of Gemini?

Demis Hassabis: We're bringing in personalization; you saw that yesterday at I/O. Users want that direction so they don't have to keep explaining their context, family, or preferences every time they want advice or help with planning. It's clearly going to make it more useful if it's personalized. But it still functions as a personalized tool equipped with prior information.

Interviewer: Given that you study personality a lot, I'm surprised that isn't more exciting to you. It feels like a huge field.

Demis Hassabis: No, it is very exciting. Personalization is super exciting. When it comes to the persona of the actual system itself, we think about that a lot. Right now, it's implicit through reinforcement learning and post-training. We establish certain values: we want it to be helpful, useful, and succinct. Then, people can add their personal tastes on top of that—some prefer a highly positive tone, others want it to be direct. That's a personal choice overlaid on a base personality. There is a lot of research needed there. It's very interesting to look at persona research, like the "Big Five" personality factors, and see how better psychological models could be applied to AI.

Interviewer: It's cool that your work is going to unlock new scientific fields. There could be a whole branch of science dedicated to analyzing AI personality.

Demis Hassabis: Exactly. And in much greater detail than we were able to before. That is going to happen. When I talk to students, I tell them that if they think creatively, there are so many new branches of science waiting to be opened up.

Interviewer: We could create the fMRI equivalent for understanding the machine.

Demis Hassabis: Exactly. There are so many opportunities there.

Interviewer: If you and I time traveled to 2050, what does it look like? What's the dream?

Demis Hassabis: Wow, 2050. That's a long time away given how fast things are improving. But my hope on that time scale is that we've gotten AGI safely over the line for humanity. We've worked out how to evolve economics so that everyone widely benefits from the increased resources and productivity. Hopefully, we're in a post-scarcity world. The next obvious step is that humanity goes to the stars to achieve maximum human flourishing. By 2050, we should be having this interview on one of the moons of Jupiter, building Dyson spheres, and waking up the universe with human consciousness, the way Carl Sagan and writers like Iain Banks in the Culture series talked about. That era should be starting by 2050.

Interviewer: Do you imagine people will be using AI to jet-fuel their jobs while still working on traditional things?

Demis Hassabis: I think so. Over the next ten years, almost everyone will have access to the most cutting-edge technology, just a few months behind the frontier labs. The next generation will be the first to grow up AI-native. I'm really excited to see what they do with these tools to superpower themselves. You'll be able to do incredible things individually that used to take teams of 10 to 50 people. It's going to unlock a lot of creativity. There will be huge disruption, but that also brings massive new opportunities for imaginative people who lean into what these tools can do.

Interviewer: My last question. You're famous for your 1:00 AM to 4:00 AM work sessions. You've said that during the day you're the CEO, and at night you focus on research. What's the most common thought in your head at that hour?

Demis Hassabis: It rotates depending on whether I have an active project going, like AlphaFold. The most fun thing is working on a science or research project myself. But other times, it's about thinking through the philosophical issues around making AI beneficial for the world. I think about what kind of collaboration is needed between leading labs, and how we can establish international standards and cooperation around AI. That is going to be urgently needed in the next few years.

Summary

In this wide-ranging conversation, Demis Hassabis, co-founder and CEO of Google DeepMind, explores the current state of Artificial Intelligence, the trajectory toward Artificial General Intelligence (AGI), and the profound implications these technologies hold for humanity. Hassabis, widely regarded as one of the most significant scientific minds of the modern era, provides a detailed roadmap for the next decade of AI development.

The Definition and Timeline of AGI

Hassabis defines AGI as a system capable of exhibiting all cognitive capabilities of the human mind. He maintains a consistent timeline that he and his co-founders established in 2010, predicting that AGI is likely to be achieved within the next five years. He notes that while "scaling laws"—the principle that increasing compute and parameters leads to greater intelligence—are seeing slightly diminishing returns compared to the initial exponential jumps, they have not plateaued. Compute remains the primary bottleneck, serving not just as a resource for scaling but as a "workbench" for necessary algorithmic experimentation.

Technical Frontiers and "Jagged Intelligence"

Despite rapid progress in video models and interactive world models (such as DeepMind’s Genie), Hassabis identifies several critical missing components in current AI:

• Continual Learning: Current systems do not learn after their training phase; Hassabis suggests the need for "consolidation" mechanisms similar to human sleep.

• Memory Architectures: Moving beyond "brute force" long context windows to more elegant memory systems.

• Long-term Planning: Developing hierarchical planning capabilities that span years.

• Consistency: Overcoming "jagged intelligence," where a model excels at a task in one format but fails at elementary logic when the prompt is slightly repositioned.

The Scientific and Medical Revolution

Hassabis views AGI primarily as the ultimate tool for scientific discovery. Following the success of AlphaFold, his company Isomorphic Labs is working to solve the entire drug discovery process, from chemistry to toxicity. He envisions a "Golden Age" where AI simulates human metabolism to accelerate clinical trials and eventually moves the regulatory needle to eliminate the need for animal testing. His personal motivation includes finding cures for complex conditions like Multiple Sclerosis and eventually "curing cancer" through a general-purpose drug design platform.

Economic Impact: The 10x Industrial Revolution

Hassabis quantifies the coming of AGI as "10 times the Industrial Revolution at 10 times the speed." He acknowledges the inevitability of labor market disruption but argues that, historically, technology creates higher-quality, higher-paying jobs. To mitigate wealth inequality, he suggests that sovereign wealth funds and pension funds must invest early in AI. Furthermore, he posits that AI will solve its own energy crisis by optimizing national grids (increasing efficiency by 30-40%) and facilitating breakthroughs in fusion energy and material science (e.g., superconductors).

Global Safety and Regulation

Addressing the "existential risk" and the potential for misuse by bad actors, Hassabis advocates for an international regulatory body similar to the International Atomic Energy Agency. He emphasizes the need for technical benchmarks to test for "undesirable properties" like deception. He stresses that as systems become more autonomous and agentic, they must have independent "kite marks" of quality and safety before being deployed.

The European Tech Ecosystem

Hassabis remains committed to London, citing the UK’s rich scientific heritage (from Newton to Turing) and the high density of world-class talent at universities like Oxford and Cambridge. He argues that being "away from the maelstrom" of Silicon Valley allows for deeper, more original thinking. However, he identifies a lack of late-stage growth capital as the primary barrier preventing Europe from producing trillion-dollar companies.

Philosophical Legacy

Ultimately, Hassabis hopes to be remembered for advancing the frontiers of knowledge and curing diseases. Beyond the technical and economic challenges, he expresses a growing concern for the philosophical questions of the AGI era: the nature of consciousness, the definition of human purpose, and the meaning of life in a world where intelligence is no longer a human monopoly.

Transcript

Demis Hassabis: I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain or Google Research or DeepMind. So, one of our groups... the returns are kind of still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.

We have amazing guests on the show, but very few honestly will be considered in the same realm as Newton, Turing, Einstein. Our guest today is one of the greatest minds on the planet and I consider myself incredibly lucky to have had the chance to sit down with him.

Those labs that have the capability to invent new algorithmic ideas are going to start having a bigger advantage over the next few years as the last set of ideas—all the juice is being wrung out of them. This is a truly special one and one that I'll remember for a very long time. I think we could probably get 30–40% more efficiency out of our national grids. Enjoy the episode, and I so appreciate the time we had with a very special human being. I sometimes quantify the coming of AGI as 10 times the Industrial Revolution at 10 times the speed. Thrilled to welcome Demis Hassabis of DeepMind. Ready to go.

Interviewer: Demis, I'm so excited to be doing this. Thank you so much for joining me today.

Demis Hassabis: Great to be here.

Interviewer: Now, there are many places that we could have started, but I was watching actually the documentary that you did, which was fantastic, and I actually wanted to start on AGI. Definitions are very varying. You've been very thoughtful about what it means to you. And so I wanted to start: can you explain to me how you think about it today so we get that as a kind of ground center?

Demis Hassabis: Yeah. Well, we've always been very consistent in how we define AGI as basically a system that exhibits all the cognitive capabilities the human mind has. And that's important because the brain is the only existence proof we have that we know of—maybe in the universe—that general intelligence is possible. So that for me is the bar for what AGI should be.

Interviewer: It's the worst question: how close are we? Everyone says different things, and it's very difficult when you have very prominent figures saying it could be as early as 2026 or 2027.

Demis Hassabis: Yeah, I mean, I think look, I've got a probability distribution around the timings, but I would say there's a very good chance of it being within the next five years. So that's not long at all.

Interviewer: Is that closer than you thought? Has that changed over time?

Demis Hassabis: Not really. I mean actually, it's funny—my co-founder Shane Legg, who's Chief Scientist here, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting when AGI would happen. And bearing in mind in 2010 when we started, almost nobody was working in AI and everyone thought it was a dead end. But they're still there on the internet for people to check. And we used to do this extrapolation of compute and algorithmic progress. And basically, we predicted around 20 years it would take from when we started out, and I think we're pretty much on track.

Interviewer: What are the biggest bottlenecks when you look today? You know, in the documentary you said you just never have enough compute. What are the biggest bottlenecks when you look at where we are today?

Demis Hassabis: I think compute is the big one. Not just for the obvious reason of scaling up your ideas and your systems as the "scaling laws," as they're called, keep on building bigger and bigger architectures with more and more parameters. And as you do that, you get more intelligent systems. But the other thing you need a lot of compute for is for doing experiments. The cloud is our workbench, basically. So if you have a new algorithmic idea but you want to test it, you've got to test it at a reasonable scale, otherwise it won't hold when you actually put it into the main system. So you need quite a lot of compute if you have a lot of researchers with lots of new ideas.

Interviewer: You mentioned the word "scaling laws." A lot of people suggest that we're hitting scaling laws and we're starting to see that plateauing effect. Do you think that's true?

Demis Hassabis: No, I don't think so. I think it's a bit more nuanced than that. So of course, when the leading companies all started building these large language models, you're getting enormous jumps with each generation of new system. You know, maybe they're almost doubling in performance. At some point that had to slow down. So it's not continuing to be exponential, but that doesn't mean there isn't great returns still for scaling the existing systems up further. And we and the other frontier labs are getting a lot of great returns on that kind of compute expansion. So, I would say the returns are still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.

Interviewer: Where are we behind where you thought we would be?

Demis Hassabis: I think actually in most areas we are ahead of where I thought we would be. If you think about things like the video models or even now with our newest systems like Genie—they're interactive world models—which I think is kind of incredible if you sort of step back and think about it. I think if you'd shown me that 5 or 10 years ago, I would have been pretty amazed. So I think in most domains we are ahead of where the field thought.

There's still some big things missing though, like continual learning. These systems don't learn after you finish training them, after you put them out into the world. They're not very good at learning further things.

Interviewer: I'm sorry to ask blunt and basic questions. Why do we not have continuous learning today?

Demis Hassabis: Well, people haven't quite figured out yet—and all the leading labs are working on this—how to integrate new learning into the existing systems that you spent months training. Of course, the brain does this very elegantly, right? Probably through things like sleep and reinforcement learning. You just kind of get "consolidation," as it’s called in the brain, where your memories during the day are replayed and then some of that information is elegantly incorporated into your existing knowledge base. Perhaps we need something like that to incorporate new information along with the existing information base.

Interviewer: You mentioned video models, you mentioned kind of media and image. It seems that DeepMind has progressed very quickly and caught up or overtaken other providers. I basically tweeted what I used and how it's changed over time, and DeepMind now is my number one for research for new shows. It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that it wasn't maybe there two to three years ago?

Demis Hassabis: Yeah. Well, we made some organizational changes. I think we've always had the deepest and broadest research bench at Google and at DeepMind. I mean, if you look at the last decade plus, I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain or Google Research or DeepMind. If you think of things like AlphaGo and reinforcement learning and of course Transformers—these are all the key breakthroughs. So I would back us to make those breakthroughs in the future if there are any missing ones.

I think we've basically helped put together all the talent from around the company sort of pushing in one direction. And then we talked earlier just about compute resources—it was also about combining all of our resources together so we could build the biggest models rather than having two or three versions around the company. So I think a lot of it was assembling together all the ingredients we already had and then kind of pushing with relentless focus and pace—acting almost like a startup, really—to get back to the frontier and be ahead in many areas.

Interviewer: You say if anyone's going to do the breakthrough it could and should be us. When you think about that, is continuous learning the next breakthrough that you're most excited by?

Demis Hassabis: I think there's quite a few things that are missing. There's continual learning. I think there's a lot of mileage in looking at different memory systems. At the moment we have these long context windows which are kind of a bit brute force. You just put everything in them. I think there's a lot of interesting architectures to be invented there.

And then there's stuff like long-term planning, hierarchical planning. These systems are not very good at planning at long time horizons, many years into the future, which we with our minds can do. So there's quite a lot of problems I think that are still left to overcome. Maybe one of the biggest is consistency. I sometimes call these systems "jagged intelligences" because they're really amazing at certain things when you pose the question in a certain way, but if you pose a question in a slightly different way they can actually still fail at quite elementary things. So a general intelligence shouldn't be that sort of jagged.

Interviewer: When you reposition files and you set up agents to perform in certain ways and then the files fall over, or the configuration completely falls over...

Demis Hassabis: Exactly. 100%.

Interviewer: That's a disaster.

Demis Hassabis: Yeah. Well, I mean, the general intelligence—if you think about how our minds work—it shouldn't have those kinds of holes in it.

Interviewer: We said about a plateauing of scaling laws. Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that, or do you think we see one to two continuously accelerate ahead of the others?

Demis Hassabis: Yeah, I feel like maybe the three or four leading labs now, of which we're one, I think the gap is starting to pull away because a lot of these tools also of course help you build the next generation. So things like coding tools, math tools... and it's getting harder and harder I would say to eke out the same gains from just the same ideas. So I think those labs that have the capability to invent new algorithmic ideas are going to start having a bigger advantage over the next few years as the last set of ideas are sort of having all the juice being wrung out of them.

Interviewer: I mean, you know, you were very open with a lot of your research for years and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that kind of use frontier models to set a benchmark and then they use open models to get as close as possible but with more cost effectiveness. What does that future look like?

Demis Hassabis: Yeah, I think it's probably similar to what we're seeing today. I mean we're big supporters of open science and open models and we've done many, many things obviously from the original Transformers to AlphaFold—these are all things we sort of gave out into the world to help the research community, and we plan to continue to do that especially in applied domains, scientific domains, applying AI to science which is obviously my passion.

But I think increasingly what you're going to see is the open source models probably one step back from the absolute frontier. It usually takes about six months for the open source community to sort of reimplement and figure out what those ideas are. But we are also pushing hard on a suite of open source models called Gemma which we're determined to make best-in-class for their sizes. Specifically for small developers or academics or the beginnings of a startup, I think they're perfect for that and also for edge computing too. So we're very interested in open source models for certain types of applications.

Interviewer: How do you think about a world post-LLMs? You have different people with different views. You have Yann LeCun with very different views.

Demis Hassabis: For me, I don't think it's... I kind of disagree with Yann on a few things. I think there might be a 50/50 chance there's some things maybe missing that we still need to make breakthroughs in—perhaps they're world models or these kinds of approaches. But my betting is pretty strong: we've seen how successful these foundation models have been. They can do incredibly impressive things. I don't think that's going to go away. We're still seeing gains from the returns from the scaling laws. So I think the only question really is when you think about a future AGI system: is an LLM foundation model going to be the key component only, or is it the total system? I just think it's a question of is there anything else needed. I don't think it's going to get replaced; I think it's going to get built on top of these foundation models just like the way we do with our world models.

Interviewer: When we think about that future five years out as you said, potentially with AGI, what does that world look like? Many people have different concerns. If we just start generally, what does that world look like to you?

Demis Hassabis: I think on the positive side—and the things obviously I've spent my whole career and life building towards AGI—is I think it will be the ultimate tool for science and medicine. So in terms of advancing scientific discovery, finding cures to diseases, I think we need that kind of technology. And so I'm hoping in five years plus time we'll be sort of entering a new golden era, a golden age of scientific discovery.

Interviewer: So, my mother's got multiple sclerosis. So it's the thing that I'm always most excited about. The thing I worry about is actually kind of drug discovery—the process of getting it through all the trials and knowing that it takes a decade before my mother will actually get any benefits from it. How do we solve that?

Demis Hassabis: I think we'll get to that point soon. First of all, what we're doing is, after we did the AlphaFold project to do protein folding, then we spun out a company called Isomorphic Labs, which is doing extremely well. And that is supposed to focus on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it's not toxic and all the different properties you need for drugs to be safe. I think we'll have that whole drug design engine ready in the next 5 to 10 years.

Then you're right: the next problem is the clinical trials still take many, many years. But I think AI can help there in terms of maybe simulating parts of the human metabolism. Also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their genomic makeup. And so I think AI can help there too. But I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process and then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models. Then maybe what we can do in the future—where maybe another 10 years after that—is where we can really just trust the predictions that the models are making and actually then maybe skip out some steps. Perhaps animal testing is not needed anymore. Maybe we can go up the dosage ladder quicker because you can rely on these models. So I think we've got to do it in two steps: solve the drug design problem first and then look at the regulatory length of time it takes.

Interviewer: Speaking of regulatory, AI safety is a big topic and a big concern. I think it was... again I watched it last night over dinner which was a great watch which is obviously the documentary... and I think it was Stephen Hawking who said, "We must get it right because we might not get another chance." Do you think that's right?

Demis Hassabis: Yeah, I do think that's right. I think that is the stakes that we have to deal with. And you know, there's two things I worry about. One is the misuse of these systems by bad actors, and they can be repurposed. These are dual-purpose technologies. They can be used for incredible good in science and health as we've just discussed, but they can also be repurposed for harmful ends by a bad actor. So that's one issue.

Second issue is a technical one: making sure these systems as they get more powerful—not today's systems, but maybe in a year or two's time when they become more agentic, more autonomous as we get towards AGI—can they be kept on the guardrails that we want? And I think regulation, the right kind of regulation, could help here in terms of making sure there's at least sort of minimum standards from all of the leading providers, but it needs to ideally be a kind of international standards.

Interviewer: What is the right kind of regulation? And again, I'm kind of quoting yourself back from this documentary. You're like, "I think we need more global coordination," which worries me because we're getting worse at it.

Demis Hassabis: Yes, for sure. I mean, it's sort of crazy the timing that we're in, right? With this most consequential maybe technology the world's ever seen at the same time as a very fragmented sort of international system. It's not ideal, but I think we're going to have to try and do the best we can to at least come up with a sort of set of minimum standards, some benchmarks that test for undesirable properties. For example, deception. Nobody wants to be building systems that are capable of deception because then they could be getting around other safeguards. And then I imagine, if things go well, some kind of certification process that basically—it's almost like a kite mark of quality—that this model has certain safeguards and certain guarantees, and so therefore consumers and companies can safely sort of build on top of it. I think that is how it should go ideally. But it does have to be international because of course these systems are cross-border and they're cross-territory.

Interviewer: Who is that ultimate verification system? You obviously started with Theme Park. Brilliant. Don't put the burgers down too close to the roller coaster. But you know, obviously as a media company, I go through any media platform saying I don't know what's real or fake. I'm always having to ask what's real or fake. Who is that arbiter of verification?

Demis Hassabis: Well, I think there—ultimately it's got to be government, I think. But the kind of technical bodies that would be able to do the technical work would be like maybe the AI safety institutes. There's a very good one in the UK that was set up under Prime Minister Sunak and I think is doing great work, and there's one in the US. Maybe some of the leading countries that have the best research should also have an equivalent body that is staffed with high-quality researchers too, that can actually evaluate and audit these kinds of systems against certain benchmarks and independently check whether they are meeting the right standards.

Interviewer: If I could give you like a magic wand that was only applicable to AI safety, what would be your implementation idea or program that you would put in place?

Demis Hassabis: Yeah, I think we need some kind of international body, maybe similar to the Atomic Energy Agency, something like that, that perhaps the AI safety institutes sort of feed into. And the research community has to also be involved in this: what are the right set of benchmarks to check? What types of traits? What types of capabilities? Maybe there are other safeguards too like... it wouldn't be desirable to have AI systems output tokens that are not human-readable. So, in some kind of machine language that we couldn't understand. I think that would introduce a new vulnerability. So there's quite a few sort of things like that which I think most of the leading labs would agree are probably not best to do. And then these bodies would test against those things. I think that would give the public confidence and academia could be involved as well, as well as civil society, that these systems which are going to get incredibly powerful have been independently checked and audited.

Interviewer: That's it. Your magic wand's done now. That was the one.

Demis Hassabis: Maybe I used it on the wrong thing!

Interviewer: Time will tell.

Demis Hassabis: Yes. Exactly.

Interviewer: You said there about science being one of the most exciting areas in five years' time. I have to ask it because it's one of the biggest concerns: the labor displacement problem. I just had Marc Andreessen on the show actually and he said that I was a Marxist for bringing it up. Marc's wonderful so I'm not blaming him, but he was like it's completely rubbish. I don't agree with it at all; we've always overcome it. How do you think about the labor displacement problem when you look at how truly capable these systems are and what that does to labor markets?

Demis Hassabis: Well, certainly in the past with every new revolutionary technology there's been a lot of job disruption. So that's for sure, and I think that's definitely going to happen. So a lot of old jobs go away or are not viable anymore, but then actually the history of it is that a whole set of new jobs arrive that maybe one can't even imagine before, and those are high-quality and higher-paying. So that's the normal course.

Of course, you have to be very careful to say "this time is different," and I guess that's what people like Marc are claiming—it's the same as the last sort of 10 massive breakthroughs like the internet, mobile, and so on. I do think this is going to be bigger than all of those previous technological breakthroughs. I mean, I sometimes quantify AGI—the coming of AGI—as like 10 times the Industrial Revolution at 10 times the speed. So unfolding over a decade instead of a century. If you read a lot about the Industrial Revolution—there's a lot of great books about it—it caused a huge amount of upheaval as well as a lot of advances. I mean, we wouldn't have modern medicine today. Child mortality was at 40% pre-Industrial Revolution. So you wouldn't want it not to have happened, but ideally this time around we mitigate some of the downsides a bit better than we did during the Industrial Revolution.

Interviewer: I often listen to amazing voices like yours and I get very excited by how fast it's coming. And then I try and stop myself from being too useful and think I should be more wise... and I'm told that you know we always overestimate what can be done in a year and underestimate what can be done in ten. Is that the truth here?

Demis Hassabis: No, I think that's still the truth. I mean, maybe both timescales of short-term and long-term are nearer than other technologies. But I do think literally today, as of today and in the next year, things are a bit overhyped in AI. I mean, there couldn't be any more hype in some ways. But on the other hand, interestingly, I still think it's very underappreciated how revolutionary this is going to be in the timescale of about 10 years. So we could call that long term. There's still that dichotomy even today with AI.

Interviewer: With the concern around labor markets, there's also a concern around income inequality and the concentration of wealth to few players. How do you see that shaping out with the comment on the Industrial Revolution?

Demis Hassabis: Well, I think there's different ways that could play out. You know, maybe pension funds should be buying into all the big AI companies and making sure that everyone has a piece of that. Or sovereign funds—maybe every country should have a sovereign wealth fund that does that. That would be the sort of investment way of doing it. I think also there needs to be thought about: if there is this massive productivity gain but it's sort of narrow where that occurs, how do we redistribute that so that everyone benefits from these huge gains?

I can see all sorts of ways that could be done including providing infrastructure and other things with that additional productivity gain. I mean there could be unbelievable things happening in the 5 to 10 year timescale including like a breakthrough in some kind of renewable free energy. You know, maybe we solve fusion. We're working on that, right, with our partners at Commonwealth Fusion. I think AI is going to usher in... maybe we have amazing new superconductors, better batteries, material science. There's all sorts of ways I could see that completely changing the nature of the economy.

Interviewer: How do we solve the energy crisis that comes with an AI revolution? What it means in terms of energy requirements is unprecedented. I know it's an incredibly hard question, but how do we solve that unprecedented need for new energy?

Demis Hassabis: Well, I think actually AI will in the medium to long run more than pay for itself in terms of energy costs. So, we work on all these projects of optimizing existing infrastructure like optimizing the grid. I think we could probably get 30–40% more efficiency out of our national grids. And then there's like modeling the climate and weather—we have the best kind of weather modeling systems in the world. So that helps us work out where the effects are really happening to mitigate that.

And then finally, the most exciting maybe is like these new breakthrough technologies like fusion, new batteries, superconductors that I think AI will be essential for helping us reach. Then I think we'll be in a completely new energy situation than we've ever been as humanity. And then that will of course help with things like the climate and environment and eventually also help us get into space much more cheaply because if you have an incredible energy source like fusion, then you have effectively unlimited rocket fuel because you can just distill/catalyze seawater.

Interviewer: I'm not going to ask you to solve space, don't worry. My question was on being in the UK. You're in London. I'm in London. I'm very proud to be in the UK. You have been, I'm sure, pushed or prodded at every turn to move to the US. Why have you stayed?

Demis Hassabis: Well, I should ask you that question, too! But I think I saw in London when we started DeepMind a place that—and the UK in general and Europe to some degree—there's incredible talent here. We've always had three or four of the top 10 universities in the world with Cambridge, Oxford, Imperial, or UCL. So we're producing the envy of the world, really—these amazing graduates and PhD students. We have incredible scientists here. We've got a rich heritage of that all the way from Turing and Hawking and Darwin, Newton. So we have this incredible history of scientific breakthroughs and having great thinkers.

I felt we had all the ingredients and the talent and great engineers here, but it just hadn't been galvanized into an ambitious deep-tech startup idea. And I felt it was possible and I felt that there was actually less competition here for that sort of talent and we could even draw in the best talent from the top European universities—and that's what it was like in the early days of DeepMind. So I think it was a huge structural advantage for us.

And then the final thing is maybe being a bit away from the Valley. There is some disadvantage in that you're not plugged into the network and the gossip and the latest trends and vibes and all these things. We're a little bit out of it here, but I think it's very conducive to thinking deeply about things, being more original about how you think. And I think that's great for things like deep tech where you don't want to be distracted by the latest fad. You want to... you know it's going to be a 20-year mission, which is what we knew at the beginning of DeepMind. So I think being a little bit away from that maelstrom is quite good.

Interviewer: Palmer Luckey often talks about being 400 miles away from the Valley. It's core to his kind of innovative thinking. Terrible question: will Europe have a trillion-dollar company? You know, you see the Americans always bash us for our lack of large companies. I ping Daniel Ek and be like, "Come on, dude," but we don't have a trillion-dollar company.

Demis Hassabis: Not yet. I mean, Daniel may well get there with one of his companies. Spotify, Helseing—I think those are two good options. I think there's no reason why we can't have that. I'm going to try and do that with Isomorphic, which is headquartered here and I think has the potential to be that. But I think that's one of the disadvantages of Europe—obviously we're a combination of smaller markets. So that's one thing we have to kind of overcome. Maybe this "EU Inc" thing could be a good innovation.

Interviewer: I'm pulling out the magic wand again. This time applied to European technology. What would you do to implement a growth mindset and an ability to build that trillion-dollar company that we don't have today?

Demis Hassabis: I think in the UK—and this may apply to other European countries too—I think unlocking what pension funds can invest in. For the growth stage, I think we're brilliant at doing the startup idea and getting it to a certain level like we did with DeepMind. But then if you really want to cross that sort of chasm into the trillion-dollar global player, then where are the billion-dollar rounds going to come from where you can really take on the existing incumbents? I think that certainly was missing 10 years ago when I was doing fundraising for DeepMind, and I think it's still kind of missing today—just that level of ambition and the amount the capital markets can support.

Interviewer: I read about some of your early rounds raising in the Silicon Valley from families. Okay, we're going to do a quickfire round. Meeting Elon for the first time—how was that?

Demis Hassabis: Oh yeah, it was amazing. It was at a Founders Fund meeting because we were both... SpaceX and DeepMind were part of the same portfolio, a kind of amazing portfolio that Peter Thiel had at Founders Fund. I think we were both invited to my first portfolio conference, I think it must have been back in 2011 or 2012, very early days. So we were the small little upcoming thing and I had a small speaking slot, and then Elon was the big thing in that portfolio. So he had the keynote, but then we met afterwards. I think it was... Elon says it was like we were passing each other in the bathroom or something! And we said hi and we both hit it off immediately as people that were almost too ambitious in their thinking, perhaps, and love sci-fi. I really wanted to visit his rocket factory, so I was trying to get an invitation to SpaceX in LA and he invited me at the end of that meeting.

Interviewer: Healthcare revolution or disease eradication that you're most excited about? Again, for me it's specifically with multiple sclerosis.

Demis Hassabis: Yeah. Well, look, I want to literally cure cancer. I know people say that's the cliché, but actually what we're building at Isomorphic is general purpose. So we're trying to build a drug design platform that will be applicable to any therapeutic area. So ideally it will help with everything from neurodegeneration, cardiovascular, immunology, to cancer. Those are the ones we're focusing on first, but eventually it should be applicable to every disease area.

Interviewer: What are you thinking about that you're not reading about or seeing anyone talk about?

Demis Hassabis: I think a lot of people are worrying about the economic questions around AGI that we talked about earlier, but I worry a lot about the philosophical questions around it. Let's assume we get the technical right, let's assume we get the economics part of it right—both of those are hard. Then there's a philosophical question of: what is meaning? What is purpose? We'll find out maybe what consciousness is... what does it mean to be human? I think that's what's coming down the road and I think we need some great new philosophers to help us navigate that.

Interviewer: Hard final question. There are many different ways you could describe what you do. What would you most like to be remembered for? What do you want your legacy to be?

Demis Hassabis: I would like my legacy to be remembered for advancing science and building technologies that bring incredible benefits into the world, like curing terrible diseases.

Interviewer: Demis, thank you so much for putting up with my meandering conversation. You've been fantastic. I really appreciate it.

Demis Hassabis: Thank you very much.