23 Jul 2025 · 2h 34m · Lex Fridman, Demis Hassabis · 26 quoted facts
Demis Hassabis argues that many natural systems, such as protein folding, weather and fluid physics, have hidden structure because they were shaped by evolution or other long selection processes. That structure lets neural networks running on ordinary computers learn them efficiently. He treats AlphaGo, AlphaFold and Veo 3 as evidence, and sees AGI as the tool to push the idea further.
The central tension is between this optimism and what AI still cannot do. Hassabis says today's systems climb existing hills well but cannot yet pick a great conjecture or make a big conceptual leap. He puts AGI at roughly even odds by 2030, expects change far larger and faster than the industrial revolution, and calls for caution, more safety research and international cooperation.
Hassabis argues that things shaped by evolution or long selection, such as proteins, mountains and orbits, have structure a neural network can learn. That is why AlphaGo and AlphaFold could avoid brute-force search over astronomically many options.
Hassabis expected that understanding intuitive physics would require acting in the world, perhaps through a robot. Veo 3 renders liquids and lighting convincingly from watching video, which surprised him.
Hassabis says picking a good conjecture is harder than solving it. Current systems can hill-climb well-specified problems, like faster matrix multiplication, but cannot yet make Einstein-style leaps.
Hassabis calls today's AI 'jagged': brilliant at some tasks and flawed at others. He would test a candidate AGI with thousands of cognitive tasks and let top experts hunt for flaws for a month or two.
Hassabis predicts AI's impact will be about ten times that of the industrial revolution, arriving about ten times faster. He urges economists and political thinkers to plan now, including ideas like universal basic provision.
He proposes that any pattern found in nature can be efficiently discovered by a classical learning algorithm, meaning a neural network on an ordinary computer. His reason is that nature's patterns were shaped by evolution and selection, so they have learnable structure.
He gives roughly a 50% chance of AGI within about five years, which he puts at around 2030. He sets a high bar: matching the brain's general cognitive abilities consistently, not just excelling at some tasks.
Veo is Google's video generator, and it renders realistic liquids, lighting and materials. Hassabis says this shows a system can pick up an intuitive grasp of physics just by watching video, without a body or robot.
He refuses to give a single 'P(doom)' number because that would claim false precision. He says the risk is non-zero and not negligible, and that the sensible response is cautious optimism, with about ten times more safety research.
He expects the best programmers to become far more productive by using AI tools well, while coding becomes accessible to more creative people. He warns that the wider disruption will be fast, so people may need to adapt their skills.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Demis Hassabis says proteins fold in milliseconds in the body, a problem DeepMind's AlphaFold has now solved computationally.
“You know, proteins fold in milliseconds in our bodies, so somehow physics solves this problem that we've now also solved computationally.”
Hassabis says Google's Veo video model appears to learn how materials behave just from watching videos, whereas he found hand-writing physics engines painstakingly hard.
“yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure around how these materials behave.”
Hassabis describes Veo 3 as generating about eight seconds of consistent video that is hard to fault at a glance.
“Whatever it is, eight seconds of consistent video that, by eye, at least, you know, at a glance, is quite hard to distinguish what the issues are.”
Hassabis worked on the game Theme Park, an open-world simulation where each player's experience was unique.
“So games like Theme Park that I worked on, where everybody's game experience would be unique to them, right?”
Hassabis says he worked on the early stages of the game Black & White, which contained what he considers still probably the best learning AI in a game.
“Black and White, uh, was the game that I worked on. Early stages of that that had the still probably the best AI learning AI in it.”
Hassabis grew up with a Spectrum and then a Commodore Amiga 500, which he calls his favourite computer and where he learned programming.
“I had a Spectrum and then a a Commodore, Amiga 500, which was my my favorite computer ever. And that's why I learned all my programming.”
Hassabis describes AlphaEvolve as combining large language models that propose solutions with evolutionary computing that searches for novel ones.
“So LLMs are kind of proposing some possible solutions, and then you do. You use evolutionary computing on top to to to find some novel part of the of the search space.”
Hassabis says AlphaGo used Monte Carlo tree search, which found the novel strategy known as Move 37 in Go.
“AlphaGo, we just used Monte Carlo tree search, right? And that's what found Move 37, the new, uh, kind of never seen before strategy in Go.”
Hassabis says DeepMind's AlphaProof system earned a silver-medal level result on maths Olympiad problems the previous year.
“Alpha proof last year, our system got, you know, silver medal in that.”
Hassabis says biologist Paul Nurse, a mentor of his, founded the Crick Institute and won the Nobel Prize in 2001.
“He runs the the, you know, he founded the Crick Institute and and won the Nobel Prize in in 2001.”
Hassabis says he has wanted to build a virtual simulation of a cell for about 25 years.
“I've had this idea, you know, of wanting to do that for maybe more like 25 years.”
Hassabis says he would start a virtual cell project with yeast, a well-understood single-celled organism that Paul Nurse studied.
“And I'd probably start with a yeast cell, and partly that's what Paul Nurse studied, because a yeast cell is like a full organism, that's a single cell, right?”
Hassabis says Google DeepMind's neural-network weather systems outperform traditional fluid-dynamics weather models that run on supercomputers.
“We've created the the best weather prediction systems in the world, and they're better than traditional fluid dynamics sort of systems”
Hassabis estimates roughly a 50% chance of AGI within five years, by about 2030.
“My estimate is sort of 50 chance by in the next five years, so, you know, by 2030, let's say.”
Hassabis proposes testing an AI by giving it all knowledge up to 1900 and seeing whether it could derive special and general relativity like Einstein.
“have a cutoff of knowledge cutoff of 1900, and then give the system everything that was, you know, that was written up to 1900.”
Hassabis says Google DeepMind has done work with Commonwealth Fusion on AI for plasma containment in fusion reactors.
“We've done lots of work on that with Commonwealth Fusion and also, uh, one could imagine reactor design”
Hassabis argues that cheap clean energy would solve water access through desalination, which he says currently is too expensive for most countries.
“the water access problem goes away because you can just use desalination. We have the technology, it's just too expensive.”
Hassabis says a new major Gemini version takes roughly six months for a full training run and productization.
“maybe it takes, you know, roughly six months or something to to do a new kind of full run and the full productization of a new version”
Hassabis says the smaller Gemini models, Flash and Flashlight, are often distilled from the largest Pro model.
“we also have different sizes pro and flash and flashlight that are often distilled from the biggest ones, you know, the flash model from the pro model.”
Hassabis says Isomorphic Labs was spun out of the AlphaFold work to do drug discovery.
“we spun out companies like Isomorphic off the back of AlphaFold to do drug discovery, and it's going really well.”
Hassabis says that when DeepMind started in 2010 he did not pay himself for a couple of years because they could not raise money.
“I remember when we were starting out back in 2010, you know, I didn't even pay myself for a couple of years”
Hassabis predicts AI's impact will be around ten times that of the industrial revolution and arrive roughly ten times faster.
“I think what we're gonna see is something like probably 10 times the impact the industrial revolution had, and but 10 times faster as well, right?”
Hassabis did a neuroscience PhD studying how the brain works, focusing on imagination and memory in the hippocampus.
“to look at how the brain works, especially imagination and memory. I focused on the hippocampus”
Hassabis says Roger Penrose worked with neuroscientists to seek quantum mechanical effects in the brain, which Hassabis says have not been convincingly found.
“He collaborated with a lot of good neuroscientists to see if he could find mechanisms for quantum mechanics behavior in the brain.”
Hassabis says Google built a version of its Gemma model, called DolphinGemma, trained on dolphin and whale sounds.
“A version of our system was trained on dolphin and whale sounds.”
Lex Fridman says he has held a paid research position at MIT since 2015.
“I have been at MIT for over ten years in a paid research position from 2015 to today.”
Hassabis explains his Nobel lecture conjecture: systems shaped by selection have structure that neural networks can model, avoiding impossible brute-force search.
“natural systems have structure because they were subject to evolutionary processes”
Video generation suggests passive observation can teach a model how liquids, light and materials behave, challenging the belief that embodiment is required.
“you can understand it through passive observation, which is pretty surprising to me”
Hassabis draws on his game-design past to imagine open-world games that adapt to each player, and links games to his interest in simulation and reality.
“it's like the ultimate choose your own adventure sort of game”
LLMs paired with evolutionary search can find new solutions, but choosing a great conjecture remains the hardest problem for AI.
“it's harder to come up with a conjecture, a really good conjecture”
Hassabis lays out a staged path from AlphaFold toward simulating a whole yeast cell, which could speed up biology experiments.
“a yeast cell is like a full organism, that's a single cell”
He gives a roughly 50% chance of AGI by 2030 and proposes broad testing plus 'lighthouse' moments like inventing a Go-level game.
“My estimate is sort of 50 chance by in the next five years”
He says it is about fifty-fifty whether scaling alone suffices or new ideas are needed, and DeepMind is pushing both.
“whether new things are needed or whether the scaling”
Hassabis bets on fusion and solar, and imagines cheap energy easing water, space and resource conflicts.
“fusion and solar are the two that I I would bet on”
He expects the AI transition to be about ten times the industrial revolution's impact, only much faster, and calls for new governance.
“something like probably 10 times the impact the industrial revolution had”
Hassabis explains why he gives no P(doom) number and why he favours more safety research and international standards.
“the only rational, sensible approach is to proceed with cautious optimism”
The following is a conversation with Demis Hassabis, his 2nd time on the podcast. He is the leader of Google DeepMind and is now a Nobel Prize winner. Demis is one of the most brilliant and fascinating minds in the world today, working on understanding and building intelligence and exploring the big mysteries of our universe. This was truly an honor and a pleasure for me. And now a quick few second mention of each sponsor. Check them out in the description or at lexfridman.
Com. Slash sponsors. It's the best way to support this podcast. We've got Hampton for connecting with founders and CEOs, Fin for AI customer service, Shopify for building e commerce businesses, Element for daily electrolytes, and AG1 for your health. Choose wisely, my friends. And now on to the full. I do try to make them interesting, but if you must skip friends, please do check out our sponsors. I enjoy their stuff, maybe you will, too. And also to get in touch with me, for whatever reason, go to lexfridman.
Com. Slash contact. All right, let's go. This episode is brought to you by Hampton, a private community for high growth founders and CEOs. That's the interesting thing about starting a company and running a company, especially one that's growing really quickly. Has to hire a lot, has to scale a lot. It's perhaps a little bit counterintuitive, but for the founder, it can be deeply lonely. I suppose that's one of the reasons they recommend to have a co founder.
But even outside of that, there's just a deep loneliness with putting it all on the line, risking everything, knowing that the chances of success are low. But if you do succeed, the gains are huge. And you have your heart in it, you have your dreams in it, you believe in it. But also there's a constant rollercoaster of fear and doubt and hope and moments of triumph and moments of failure. All those go back and forth and just this constant psychological turmoil anyway, through all that.
It's just nice to connect with other people that are going through the same thing, and that's what Hampton is about. It does a thing where every month, eight founders face to face, have real conversations about their daily struggles. Groups are forming in a bunch of places, New York City, Austin, San Francisco, L. A. Miami, Denver, and so on. If you are a founder who's tired of carrying it all alone, visit join Hamptons. com Lex to see if it's a fit for you.
That's join Hamptons. com Lex. This episode is also brought to you by Fin. It's an AI agent for customer service, so they are focused, laser focused on the customer service application, and they wanna do that better than anybody else in the world. In fact, if you measure by the metric of the number of resolutions, so when you have the agent resolve the customer service issue, that's resolution. They have a fifty nine average resolution rate, which makes it the highest performing customer service agent on the market.
It's uh trusted by over five zero customer service leaders, and even uh top AI companies, including Anthropic. The way they design the system is, uh, it can continuously improve from the interactions, so you can, uh, continuously analyze, train, test and deploy. Also, probably important to say, they, uh, give you a ninety day money back guarantee. Go to fin. ai Lex to learn more about transforming your customer service and scaling your support team. That's fin. ai Lex.
This episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store. Even I figured out how to create an online store at lexfridman. Com. Store and put up a few shirts. Haven't done anything with it since because I'm not a serious person. There's a lot of serious people that build real businesses on top of Shopify. It's a platform that connects you with millions of people that wanna buy stuff and gives you all the tools you need and all the integrations you need to do just that at scale.
As we talked about with D. H. H. about the incredible beauty and power of Ruby on Rails that Shopify is powered by. I have not yet built a serious sort of medium scale project on, uh, rails. I need to. It's just I need to actually find things that I need to do web dev type of stuff with to inspire myself, uh, to build something useful. I don't wanna build some weird variant of a to do list, especially now with, uh, the help of LLMs.
You can generate so much of the code, so I need to figure out how to learn a new framework, a new programming language, is when LLMs can generate so much of it. And I don't wanna do it exclusively by vibe coding, because I feel like that's not a way to learn fully a thing, but vibe coding does remove some of the friction of learning. So balancing that out is a tricky thing to do anyway. That's about the programming language and the framework that power Shopify, but Shopify itself connects buyers and sellers in an incredible scale.
That's awe inspiring. Sign up for a one per month trial period at Shopify. Com. Slash Lex. That's all lowercase. Go to Shopify. Com slash Lex to take your business to the next level today. This episode is also brought to you by Element, my daily zero sugar and delicious electrolyte mix. I've been traveling recently, and I have a lot of element packets with me, and I bring that, and I bring, uh, bands, uh, whatever you call them.
I don't know what they're called. They're like rubber bands for, like, um, basic shoulder exercises. So if I have to do a lot of either heavy lifting or, uh, heavy jujitsu training, I like to warm up the shoulders really well. Cause, probably cause I have issues with shoulders from many years of playing tennis and many years of doing stupidly bench press anyway. I think of element as a critical component of my workout routine. Hydrate before, rehydrate after, fully embrace the deliciousness of watermelon salt flavor, the flavor of champions, the one I recommend.
It's been quite a while since I tried the others, they're all good. But for me, I'm a man of focus and dedication, and I'm dedicated to watermelon salt. I think they have actually, uh, I saw a lemonade flavor. I think a lot of people love lemonade, so maybe that's your thing. For me, I'm sticking to watermelon salt. Get a free eight count sample pack with any purchase. Try it to drink element. Com. This episode is also brought to you by AG1, an all in one daily drink to support better health and peak performance.
I travel with it. It makes me feel like I take a little piece of home with me. I drink it at least once a day, very often twice a day. And they keep innovating, they keep improving it. They recently introduced the AG1 next gen, improving every aspect, more vitamins and minerals and upgraded probiotics. It's funny how a morning routine can be the source of peace and happiness. Because I find that if I check my phone at all in the first couple hours of the day, I get this weird anxiety that ultimately morphs into unhappiness.
And if I don't, I'm much more likely to sort of maintain that deep focus. And a part of that early in the morning is some coffee or caffeinated drink, and then a few hours on is AG1, and it's just many hours of deep focus in between. It makes me feel happy, makes me feel at one with the universe, and it helps me get shit done. Anyway, they'll give you one month supply of fish oil when you sign up at drink a g1.
This is a Lex Fridman podcast. To support it, please check out our sponsors in the description or at lexfridman. Com. Slash sponsors, and consider subscribing, commenting and sharing the podcast with folks who might find it interesting. I promise to work extremely hard to always bring you nuanced and long form conversations with a wide variety of interesting people from all walks of life. And now, dear friends, here's Demis Hassabis. In your Nobel Prize lecture, you proposed what I think is a super interesting conjecture that quote.
Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm. What kind of patterns of systems might be included in that biology, chemistry, physics, maybe cosmology, neuroscience? What are we talking about?
well, look, I, I felt that it's sort of a tradition, I think, of Nobel Prize lectures, that you're supposed to be a little bit provocative, and I wanted to follow that tradition. What I was talking about there is if you take a step back and you look at, um, all the work that we've done, especially with the Alpha X projects, so I'm thinking AlphaGo, of course, AlphaFold. What they really are is we're building models of very combinatorially high dimensional spaces.
That, you know, if you tried to brute force a solution, find the best move in Go, or find the the exact shape of a protein. And if you enumerated all the possibilities, you there wouldn't be enough time in the in the, you know, the time of the universe. So you have to do something much smarter. And what we did in both cases was build models of those environments, um, and that guided the search in a in a smart way, and that makes it tractable.
So if you think about protein folding, which is obviously a natural system, you know, why should that be possible? How does physics do that? You know, proteins fold in milliseconds in our bodies, so somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that that shape them. And if that's true, then you can maybe learn, uh, uh, what that structure is.
So this perspective, I think, is a really interesting one. You've hinted it at it, which is almost like, uh, crudely stated, anything that can be evolved can be efficiently modeled. Do you think there's some truth to that?
Yeah, I sometimes call it survival of the stabilist or something like that, because, uh, you know, it's. It's. Of course, there's evolution for life, uh, living things, but there's also, you know, if you think about geological time. So the shape of mountains, that's been shaped by weathering processes right over thousands of years, but then you can even take it cosmological, the orbits of planets, the um, shapes of asteroids. These have all been survived kind of processes that have acted on them many, many times.
So if that's true, then there should be some sort of pattern, um, that you can kind of reverse learn. And, uh, a kind of manifold, really, that helps you, uh uh, search to the right solution, to the right shape, um, and actually allow you to predict things about it, uh, in an efficient way, because it's not a random pattern, right? So, um, it may not be possible for for man made things or abstract things like factorizing large numbers.
Because unless there's patterns in the number space, which there might be, but if there's not and it's uniform, then there's no pattern to learn. There's no model to learn that will help you search, so you have to do brute force. So in that case, you, you know, you maybe need a quantum computer, something like this. But in most things in nature that we're interested in, uh, are not like that. They have structure, um, that evolved for a reason and survived over time.
And if that's true, I think that's potentially learnable by a neural network.
It's like nature's doing a search process, and it's so fascinating that it's in that search process is creating systems that could be efficiently modeled. It's so interesting.
so they can be efficiently rediscovered or recovered. Um, because nature's not random, right? These th. Uh. Everything that we see around us, including like the elements that are more stable, all of those things, they're subject to, um, some kind of selection process, pressure.
Do you think, because you're also a fan of theoretical computer science and complexity? Do you think we can come up with a kinda complexity class, like a complexity zoo type of class? Where maybe it's the set of learnable systems, the set of learnable natural systems, LNS. This is a Demis Hassabis a new class, new class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be, uh, modeled efficiently.
yeah, I mean, I've. I've always been fascinated by the P equals NP question and what is modelable by classical systems, i. e. non quantum systems, you know, Turing machines in effect. And that's exactly what I'm working on, actually, in kind of my few moments of spare time with a few colleagues about, is should there be, you know, maybe a new class of problem that is solvable by this type of neural network process? And kind of mapped onto these natural systems.
So, you know, the things that exist in physics and have structure, so I think that could be a very interesting, uh, new way of thinking about it. And it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary, information is the most sort of fundamental unit of the universe, more fundamental than energy and matter. I think they can all be converted into each other, but I think of the universe as a kind of informational system.
So when you think of the the universe as an informational system, then the P equals NP question is a is a physics question. That's right, It is a question that can help us actually solve the entirety of this whole thing going on.
yeah, I think it's one of the most, uh, fundamental questions, actually, if you think of physics as informational, uh, and and the answer to that, I think, is gonna be, you know, very enlightening.
More specific to the P and NP question, this again, some of the stuff we're saying is kind of crazy right now. Just like the Christian Anthony Nobel Prize speech, controversial thing that he said sounded crazy. And then you went and got a Nobel Prize for this with John Jumper, solved the problem. So let me let me just stick to the P equals NP. Do you think there's something in this thing we're talking about that could be shown?
If you? C can do something like, uh, polynomial time or constant time compute ahead of time and construct this gigantic model, then you can solve some of these extremely difficult problems in a theoretical computer science kind of way.
Yeah, I think that there are actually a huge class of problems that could be couched in this way. The way we did AlphaGo and the way we did AlphaFold, where you know, you, you model what the dynamics of the system is, the the properties of that system, the environment that you're trying to understand. And then that makes the search for the solution or the prediction of the next step efficient, basically polynomial time, so tractable by a, uh, classical system, uh, which a neural network is.
It runs on normal computers, right? Classical computers, uh, Turing machines, in effect. And um, I think it's one of the most interesting questions there is is how far can that paradigm go? You know, I think we've proven, uh, and the AI community in general, that classical systems, Turing machines, can go a lot further than we previously thought. You know, they can do things like model the structures of proteins and play Go to better than world champion level.
And, uh, you know, a lot of people would've thought maybe 1020 years ago that was decades away. Or maybe you would need some sort of quantum machines to to quantum systems to be able to do things like protein folding. And so I think we haven't really, uh, even sort of scratched the surface yet of what, uh, classical systems, so called uh, uh, could do. And, of course, AGI being built on a on a neural network system, on top of a neural network system, on top of a classical computer would be the ultimate expression of that.
And I think the limit, the, you know, the the what, what, the bounds of that kind of system, what it can do. It's a very interesting question, and and and directly speaks to the P equals NP question.
What, what do you think again? Hypothetical might be outside of this, maybe emergent phenomena, like if you look at cellular automata, some of the you have extremely simple systems, and then some complexity emerges. Maybe that would be outside, or even would you guess even that might be amenable to efficient modeling by a classical?
yeah. I think those systems would be right on the boundary, right? So, um, I think most emergent systems, cellular automata, things like that could be modelable by a classical system. You just sort of do a forward simulation of it, and it'd probably be efficient enough. Um, of course, there's the question of things like chaotic systems, where the initial conditions really matter, and then you get to some, you know, uncorrelated end state. No, those could be difficult to model.
So I think these are kind of the open questions. But I think when you step back and look at what we've done with the systems and the and the problems that we've solved. And then you look at things like Veo 3 on, like video generation, sort of rendering, physics and lighting and things like that, you know, really, core fundamental things in physics. Um, it's pretty interesting. I think it's telling us something quite fundamental about how the universe is structured, in my opinion.
Um, so, you know, in in a way, that's what I want to build AGI for is to help, uh us, uh, as scientists, answer these questions, uh, like P equals NP.
Yeah, I think we might be continuously surprised about what is modelable by classical computers. I mean, AlphaFold three on the interaction side is surprising that you can make any kind of progress on that direction. Alpha genome is surprising that you can map the genetic code to the function. Kind of playing with the emergent kind of phenomena. You think there's so many combinatorial options that, and then here you go, you can find the kernel that is efficiently modelable.
yes, because th th. There's some structure, there's some landscape, you know, in the energy landscape, or whatever it is that you can follow some gradient you can follow. And of course, what neural networks are very good at is following gradients. And so if there's one to follow, an object, and you can specify the objective function correctly, you know, you don't have to deal with all that complexity. Which I think is how we maybe have naively thought about it for decades, those problems.
If you just enumerate all the possibilities, it looks totally intractable, and there's many, many problems like that. And then you think, well, it's like ten to the 300 po possible protein structures, uh, ten to the hundred and, you know, seventy possible go positions. All of these are way more than atoms in the universe. So how could one possibly find the the right solution or predict the next step? And and it? But it turns out that it is possible, and of course, reality, nature does do it right, proteins do fold.
So that that gives you confidence that there must be, if we understood how physics was doing that, uh, in a sense, uh, then, and we could mimic that process, I. e. model that process, uh, it should be possible. On our classical systems, is is is basically what the conjecture is about.
And, of course, there's nonlinear dynamical systems, highly nonlinear dynamical systems, everything involving fluid, you know, I. I recently had a conversation with Terrence Tao, who mathematically, uh, it contends with a very difficult aspect of systems that have some singularities in them that break the mathematics. And it's just hard for us humans to make any kind of clean predictions about highly nonlinear dynamical systems. But again, to your point, we may be very surprised what classical learning systems might be able to do about even fluid.
yes, exactly. I mean, fluid dynamics, Navier Stokes equations. These are traditionally thought of as very, very difficult, intractable kind of problems to do on classical systems. They take enormous amounts of compute, you know, weather prediction systems, you know, these kind of things all involve fluid dynamics calculations. And um, but again, if you look at something like VO, our video generation model, it can model liquids quite well, surprisingly well, and materials, specular lighting. I love the ones where you know there's there's people who've generated videos where there's like clear liquids going through hydraulic presses and then being squeezed out.
I, I used to write, uh, physics engines and graphics engines in in my early days in gaming, and I know, uh, it's just so painstakingly hard to build programs that can do that. And yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure around how these materials behave. So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully understood what's going on under the hood.
That's maybe, you know, maybe true of most of reality.
Yeah, I've been continuously, precisely by this aspect of Veo 3. I think a lot of people highlight different aspects, including the comedic and the meme and all that kind of stuff. And then the ultra realistic ability to capture humans in a really nice way. That's compelling and g feels close to reality. And then combine that with native audio. All of those are marvelous things about Veo 3, but the exactly the thing you're mentioning, which is the physics, is not perfect, but it's pretty damn good.
And then the the really interesting scientific question is, what is it understanding about our world in order to be able to do that? Because of the cynical take with the diffusion models, there's no way it understands anything, but it seem, I mean, I don't think you can generate that kind of video without understanding. And then our own philosophical notion of what it means to understand then is, like, brought to the surface, like, do to what degree do you think Veo 3 understands our world?
I think to the extent that it can predict the next frames, you know, in a coherent way, that's some that is a form, you know, of understanding, right? Not in the anthropomorphic version of, you know, it's not some kind of deep philosophical understanding of what's going on. I don't think these systems have that, but they they certainly have, uh, modeled enough of the dynamics, you know, put it that way, that they can pretty accurately generate. Whatever it is, eight seconds of consistent video that, by eye, at least, you know, at a glance, is quite hard to distinguish what the issues are.
And imagine that in two or three more years time, that's the thing I'm thinking about and how incredible that will they will look, uh, given where we've come from, you know, the early versions of that, uh uh, one or two years ago. And so, um, the rate of progress is incredible. And I think, um, I'm like you. It's like a lot of people love all of the the stand up comedians and the the. That actually captures a lot of human dynamics very well and and body language.
But actually, the thing I'm most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids, and it's pretty amazing that it can do that. And I think that shows it that it has some notion of at least intuitive physics, right? Um, how things are supposed to work, uh, intuitively, maybe the way that, uh, a human child would understand physics, right? As opposed to a, you know, a PhD. student, really, uh, being able to unpack all the equations.
It's more of an intuitive physics understanding.
Well, that intuitive physics understanding, that's the base layer, that's the thing people sometimes call like common sense, like it, it really understands something. I think that really surprised a lot of people. It blows my mind that I just didn't think it would be possible to generate that level of realism without understanding. There's this notion that you can only understand the physical world by having an embodied AI system, a robot that interacts with that world. That's the only way to construct an understanding of that world.
But Veo 3 is directly challenging that. It feels like,
yes, and it's very interesting, you know? Even, uh, if we, if you were to ask me five, ten years ago, I would've said, even though I was immersed in all of this, I would've said, well, yeah, you probably need to understand intuitive physics, you know. Like, if I push this off the table, this glass, it will maybe shatter, you know, um, and the and the liquid will spill out, right? So we know all of these things, but I thought that, you know.
And there's a lot of theories in neuroscience. It's called action in perception, where you know you, you need to act in the world to really, truly perceive it in a deep way. And there was a lot of theories about you'd need embodied intelligence or robotics or something, or maybe at least simulated action, uh, so that you would understand things like intuitive physics. But it seems like, um, you can understand it through passive observation, which is pretty surprising to me.
And and again, I think hints at something underlying about the nature of, uh, reality in in, in my opinion, beyond, um, just the, you know, the cool videos that it generates, um, and and of course, there's next stages. Is maybe even making those videos interactive, so, uh, one can actually step into them and move around them. Um, which would be really mind blowing, especially given my games background, so you can imagine, uh, and then and then, I think, you know, you're.
We're starting to get towards what I would call a world model, a model of how the world works, the mechanics of the world, the physics of the world, and the things in that world. And of course, that's what you would need for a true AGI system.
I have to talk to you about video games. So you, you're being a bit trolly, I I think you're, you're having more and more fun on Twitter on X, which is great to see. So a guy named Jimmy Apples tweeted, Let me play a video game of my Veo 3 videos already. Uh, Google cooked so good playable world models. When spelled W E N question mark, Uh, and then you quote tweeted that with now, wouldn't that be something?
So how, how hard is it to build game worlds with AI? Maybe can you look out into the future, uh, of video games five, ten years out? What do you think that looks like?
Well, games were my first love, really, and doing AI for games was the first thing I did professionally in in my teenage years. And and was the first, uh, major AI systems that I built. And, uh, I always wanna I have, I wanna scratch that itch one day and come back to that. So, you know, and I will do, I think. And um, I think I sort of dream about, you know, what would I have done back in the nineties if I'd had access to the kind of AI systems we have today?
And I think you could build absolutely mind blowing games. Um, and I think the next stage is I always used to love making. All the games I've made are open world games, so they're games where there's a simulation and then there's AI characters. And then the player, uh, interacts with that simulation, and the simulation adapts to the way the player plays. And I always thought they were the coolest games because, uh, so games like Theme Park that I worked on, where everybody's game experience would be unique to them, right?
Because you're kinda co creating the game, right? Uh, we set up the parameters, we set up initial conditions, and then you as the player immersed in it, and then you are co creating it with the with the simulation. But of course, it's very hard to program open world games. You know, you've gotta be able to create, uh, content whichever direction the player goes in, and you want it to be compelling no matter what the player chooses.
Um, and so it was always quite difficult to build, uh, things like cellular automata, actually, type of those kind of classical systems, which created some emergent behavior, um, but they're always a little bit fragile, a little bit limited. Now we're maybe on the cusp in the next few years, five, ten years of having AI systems that can truly create around your imagination. Um, can n sort of dynamically change the story and storytell the narrative around, uh, and make it dramatic, no matter what you end up choosing.
So it's like the ultimate choose your own adventure sort of game. And, uh, you know, I think maybe we're within reach. If you think of a kind of interactive version of VO, uh, and then wind that forward five to ten years. And, um, you know, imagine how good it's gonna be.
yeah, so you said a lot of super interesting stuff there. So one, the open world built into that is a deep personalization, the way you've described it. So it's not just that it's open world, like you can open any door and there'll be something there. It's that the choice of which door you open in an unconstrained way defines the worlds you see. So some games try to do that, they give you choice, but it's really just an illusion of choice.
Because you only, uh, uh, like, like Stanley Parable, this game I recently played, it's, it's, it's really, there's a couple doors, and it really just takes you down a narrative. Stanley Parable is a great video game. I recommend people play that kind of, uh, in a meta way, uh, mocks the illusion of choice, and there's philosophical notions of free will and so on. But, uh, I do, like one of my favorite games of Elder Scrolls is Daggerfall.
I believe that they really played with the like random generation of the dungeons of if you can step in. And it gives you this feeling of an open world. And there you mentioned interactivity, you don't need to interact, that that's the 1st step, cause you don't need to interact that much. You just when you open the door, whatever you see is randomly generated for you. And that's already an incredible experience, cause you might be the only person to ever see that.
yeah, exactly, and and so. But what you'd like is a little bit better than just sort of a random generation, right? So you'd like, uh, and and also better than a simple AB hard coded choice, right? That's not really, uh, open world, right? Like, as you say, it's just giving you the illusion of choice. What you want to be able to do is a is potentially anything in that game environment. Um, and I think the only way you can do that is to have, uh, generated system systems that, uh, will generate that on the fly.
Of course, you can't create infinite amounts of game assets, right? It's expensive enough already how AAA games are made today. And that was obvious to to us back in the nineties when I was working on all these games. I think maybe Black and White, uh, was the game that I worked on. Early stages of that that had the still probably the best AI learning AI in it. It was an early reinforcement learning system that you, you know, you were.
You were looking after this mythical creature and growing it and nurturing it. And depending how you treated it, it would treat the villagers in that world in the same way. So if you were mean to it, it would be mean, if you were good, it would be protective. And so it was really a reflection of the way you played it. So actually, all of the, uh. I've been working on sort of simulations and AI f uh uh through the medium of games at the beginning of my career.
And and really, the whole of what I do today is still a follow on from, uh, those early, more hard coded ways of doing the AI. To now, you know, fully general learning systems that that are trying to achieve the same thing.
Yeah, it's been, uh, interesting, hilarious and, uh, fun to watch you and Elon, obviously itching to create games, cause you're both gamers and one of the sad aspects of your, uh, incredible success in so many domains of science, like serious adult stuff that you might not have time to really create a game. You might end up creating the tooling that others would create the game, and you have to watch other others create the thing you've always dreamed of.
Do you think it's possible you can, somehow, in your extremely busy schedule, actually find time to create something like black and white? Some some, an actual video game where, like you could, uh, make the childhood dream yeah, well, become become reality?
you know, uh, there's two things where I think about that is maybe that with vibe coding as it gets better, and there's possibility that I could, you know, one could do that actually in in your spare time. So I'm quite excited about that as a as. That would be my project if if I got the time to do some vibe coding. Um, I'm itching to do that. And then the other thing is, you know, maybe it's a sabbatical.
After AGI has been safely stewarded into the world and delivered into the world, you know that, and then working on my physics theory. As we talked about at the beginning, those would be the two, my my two post AGI projects, let's call it that way, I, I would love yeah, to see which the ultimate game.
post post AGI, which you choose. Solving, uh, the the problem that some of the smartest people in human history contended with, so P equals NP or creating a cool video
Yeah, well, they, but they might. game. But in my world, they'd be related because it would be an open world simulated game, uh, as realistic as possible. So, you know what, what is, what is the universe? That's that's that's speaking to the same question, right? And P equals NP. I think all these things are related, at least in my mind.
I mean, in a really serious way, it's like V. Video games sometimes are looked down upon as just this fun side activity. But especially as, uh, AI does more and more of, um, the difficult, uh, boring tasks, something we in in modern world call work, you know, video games is the thing in which we may find meaning, in which we may find, like, what to do with our time. You could create incredibly rich, meaningful experiences, like that's what human life is, and then in video games, you can create more sophisticated, more diverse ways of living, right?
That's I think the whole idea.
so. I mean, those of us who love games, and I still do, is is, is, um, you know, it's almost can let your imagination run wild, right? Like I, I used to love games, um, and working on games so much. Because it's the fusion, especially in the nineties and two early 2000s, the sort of golden era, and maybe the eighties of of of game, of the games industry. And it was all being discovered, new genres are being discovered.
We weren't just making games, we felt we were. We were creating a new entertainment medium that never existed before. Uh, especially with these open world games and simulation games, where you were co create, you as the player were co creating the story. There's no other media, uh, entertainment media, where you do that, where you, as the audience, actually co create the the story. And, of course, now with multiplayer games as well, it can be a s very social activity and can explore kinds of interesting worlds in that.
But on the other hand, you know, it's very important to, um, also enjoy and experience, uh, the physical world. But the question is, then, you know, I think we're gonna have to call. Or confront the question again of what is the fundamental nat nature of reality? Uh, what is gonna be the difference between these increasingly realistic simulations and, uh, multiplayer ones? And em emergent, um, and what we do in the real world?
Yeah, there's clearly a huge amount of value to experiencing the real world nature, there's also a huge amount of value in experiencing other humans directly in person, the way we're sitting here today. But we need to really scientifically rigorously answer the question why, and which aspect of that can be mapped into the virtual world. exactly, and And it's not. It's not enough to say, yeah, you should go touch grass and hang out in nature. It's like, why exactly is that valuable?
yes. And I guess that's maybe the thing that's been, uh, haunting me or obsessing me from the beginning of my career. If you think about all the different things I've done, that's they're all related in that way. The simulation, nature of reality, and what is the bounds of, you know, what can be modeled?
Sorry for the ridiculous question, but so far, what is the greatest video game of all time? What's up there? What Well, my favorite makes it?
one of all time is Civilization. I, I have to say that that was the the the Civilization I and Civilization II, my favorite games of all time. Um,
I can only assume you've avoided the most recent one because it would probably you would d that would be your sabbatical, that would you would disappear.
yes, exactly. They take a lot of time, these civilization games, so, uh, I gotta be careful with them.
Fun question You and Elon seem to be somehow solid gamers. Uh, is there a connection between being great at gaming and and, uh, being great leaders of AI companies?
I don't know I. It's an interesting one. I mean, uh, we both love games, and, uh, it's interesting. He wrote games as well. To start off with, it's probably especially in the era I grew up in, where home computers were just became a thing, you know, in the late 80s and 90s, especially in the UK. I had a Spectrum and then a a Commodore, Amiga 500, which was my my favorite computer ever. And that's why I learned all my programming.
And of course, it's a very fun thing, uh, to program is to program games, so I think it's a great way to learn programming, probably still is. And um, and then, of course, I immediately took it in directions of AI and simulations, which so I maybe was able to express my interest in in games, uh, and my sort of wider scientific interests all together. And then the final thing I think that's great about games is it fuses, um, artistic design, you know, art with the the the most cutting edge programming.
Um, so again, in the 90s, all of the most interesting, uh, technical advances were happening in gaming, whether that was AI, graphics, physics engines, uh, hardware, even GPUs, of course, were designed for gaming originally. Um, so e. Everything that was pushing computing forward in the in the 90s was due to gaming. So I, interestingly, that was where the forefront of research was going on, and it was this incredible fusion with with art. Um, y, you know, graphics, but also music and just the whole new media of storytelling, and I love that.
For me, it's this sort of multidisciplinary kind of effort is again something I've enjoyed my whole, my whole life.
I have to ask you, I almost forgot about. W. One of the many, and I would say one of the most incredible things recently, uh, that somehow didn't yet get enough attention is AlphaEvolve. We talked about evolution a little bit, but it's the Google DeepMind system that evolves algorithms. Are these kinds of evolution like techniques promising as a component in a future superintelligence system? So for people who don't know, it's kind of, um, I don't know if it's fair to say, it's LLM guided evolution search, so it's d.
Evolutionary algorithms are doing the search, and LLMs are telling you where.
yes, exactly. So LLMs are kind of proposing some possible solutions, and then you do. You use evolutionary computing on top to to to find some novel part of the of the search space. So actually, I think it's an example of very promising directions where you combine LLMs or foundation models with other computational techniques. Evolutionary methods is one, but you could also imagine Monte Carlo tree search, basically many types of search algorithms or reasoning algorithms, sort of on top of or using the foundation models as a basis.
So I actually think there's quite a lot of interesting, uh, things to be discovered, probably with these sort of hybrid systems, let's call them.
But not to romanticize evolution. I'm only human, but you, you think there's some value in whatever that mechanism is, cause we already talked about natural systems. Do you think we're? There's a lot of low hanging fruit of us understanding, being M, being able to model, uh, being able to simulate evolution, and then using that, whatever we understand about that nature inspired mechanism, to to then do search better and better and better.
yes, so if you think about, uh, again, br, uh, b, uh. Breaking down the sys sort of systems we've built, uh, to their really fundamental core, you've got, like the model of the of the underlying dynamics of the system, uh. And then if you wanna discover something new, something novel that hasn't been seen before, um, then you need some kind of search process on top to take you to a novel region of the of the of the search space.
And um, you can do that in a number of ways. Evolutionary computing is one. Um, with AlphaGo, we just used Monte Carlo tree search, right? And that's what found Move 37, the new, uh, kind of never seen before strategy in Go. And so that's how you can go beyond potentially what is already known, so the model can model everything that you currently know about, right, all the data that you currently have. But then how do you go beyond that?
So that starts to speak about the ideas of creativity. How can these systems create something new, find, discover something new? Obviously, this is super relevant for scientific discovery or pushing met science, medicine forward, which we wanna do with these systems. And you can actually bolt on some, uh, fairly simple search systems on top of these models and get you into a new region of space. Of course, you also have to, um, make sure that, uh, you're not searching that space totally randomly.
It would be too big, so you have to have some objective function that you're trying to optimize and hill climb towards, and that guides that search.
But there's some mechanism of evolution that are interesting, maybe in the space of programs, but then the space of programs is an extremely important space, cause you can probably generalize to, uh, to everything. You know, but, you know, for example, mutation, so it's not just Monte Carlo tree search, where it's like a search. You could every once in a while
Combine things. alter, combine Yeah, yeah,
things, alter like sub like a components of a thing. So then, you know, what evolution is really good at is not just the natural selection, it's combining things and building increasingly complex h hierarchical systems. So that component is super interesting, especially like with Alpha evolving the space of programs.
yeah, exactly. So there's a. You can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about, right? Of course, like happened with life, interestingly, with naive, uh, sort of traditional evolution computing methods, without LLMs and the modern AI. The problem with them, well, I think there was a. They were very well studied in the 90s and and and early 2000s, and some promising results. But the problem was they could never work out how to evolve new properties, new emergent properties.
You always had a sort of subset of the properties that you put into the system. But maybe if we combine them with these foundation models, perhaps we can overcome that limitation. Obviously, uh, natural evolution clearly did, cause it ev, it did evolve new capabilities, right? So, uh, bacteria to where we are now. So clearly that it must be possible with evolutionary systems to generate, uh, new patterns, you know, going back to the 1st thing we talked about, and, uh, new capabilities and emergent properties.
And maybe we're on the cusp of discovering how to do that.
yeah, listen, uh, AlphaFold is one of the coolest things I've ever seen. I've, I've, uh, uh, on my desk at home. You know, most of my time is spent behind the computer, just programming. And next to the the three screens is a s skull of a, uh, Tiktaalik, which is one of the early organisms that crawled out of the water onto land. And I just kind of watch that little guy. It's like, you, the the whatever the computation mechanism of evolution is, is quite incredible, it's truly, truly incredible now.
Whether that's exactly the thing we need to do to do our search, but never, never, uh, dismiss the power of nature, what, what it did here.
yeah, and it's amazing, um, whi, which is a relatively simple algorithm, right, effectively, and it can generate all of this immense complexity, emerges, obviously running over, you know, 4 billion years of time. But but it's, it's, it's, you know, you can think about that as again, a a a pro, a search process that ran over the physics substrate of the universe for, uh, a long amount of computational time. But then it generated all this incredible, uh, rich diversity.
so, uh, so many questions I wanna ask you. So one, you do have a dream, one of the natural systems you want to, uh, try to model is, uh, is a cell, that's a beautiful dream. Uh, I could ask you about that. I also just for that purpose, on the AI scientist front, just broadly. So there's a essay, uh, from Daniel Coquillette, Scott Alexander and others that outline steps along the way to get to ASI. And has a lot of interesting ideas in it, one of which is, uh, including a superhuman coder and a superhuman AI researcher.
And in that, there's a term of research taste that's really interesting. So in everything you've seen, do you think it's possible for AI systems to have research taste? To help you in the way that AI co scientist does to help steer human, um, human, brilliant scientists, and then p potentially by itself, to figure out what are the directions? Uh, where you want to gen generate truly novel ideas, cause that seems to be like a really important component of how to do great science
yeah, I think that's gonna be one of the hardest things to to, uh, mimic or model, is is this this idea of taste or or judgment? I think that's what separates the, you know, the the great scientists from the good scientists. Like all all professional scientists are good technically, right? Otherwise they wouldn't have made it, uh, that far in in academia and things like that. But then do you have the taste to sort of sniff out what the right direction is, what the right experiment is, what the right question is?
So the it's the it's picking the right question is is the hardest part of science, um, and and making the right hypothesis, and um, that's what, you know, today's systems definitely they can't do. So, you know, I often say it's harder to come up with a conjecture, a really good conjecture, than it is to solve it. So we may have systems soon that can solve pretty hard conjectures. Um, you know, I, I, um, maths Olympiad problems, where we we, you know, Alpha proof last year, our system got, you know, silver medal in that.
Really hard problems. Maybe eventually we'll be able to solve a millennium prize kind of problem. But could a system come up with a conjecture worthy of study that someone like Terence Tao would have gone? You know what? That's a really deep question about the nature of maths, or the nature of numbers, or the nature of physics. And that is far harder type of creativity, and we don't really know today. Systems clearly can't do that, and we're not quite sure what that mechanism would be.
This kind of leap of imagination, like like Einstein had when he came up with, you know, special relativity and then general relativity, with the knowledge he had at the time.
as for for conjecture, the you want to come up with a thing that's interesting, it's amenable to proof. So, like, it's easy to come up with a thing that's extremely difficult, it's easy to come up with a thing that's extremely easy. But that at that very edge,
that sweet spot, right of of basically advancing the science and splitting the hypothesis space into two, ideally, right? Whether if it's true or not true, you, you've learned something really useful. And um, and and that's hard, and and making something that's also, uh, you know, falsifiable and within sort of the technologies that you have, you currently have available. So it's a very creative process, actually, highly creative process that, um, I I think just a kind of naive search on top of a model won't be enough for that.
okay, the idea of splitting the hypothesis space in two is super interesting. So, uh, I've heard you say that there's basically no failure in, or failure is extremely valuable if it's done. If you construct the questions right, if you construct the experiments right, if you design them right, that failure, success are both useful. So perhaps because it splits the hypothesis space in two, it's like a binary search.
that's right. So when you do, like, you know, real blue sky research, there's no such thing as failure, really, as long as you're picking experiments and hypotheses that that meaningfully split the hypothesis space. So, you know, and you learn something, you can learn something kind of equally valuable from, uh, an experiment that doesn't work. That should tell you if you've designed the experiment well and your hypotheses are are interesting, it should tell you a lot about where to go next.
And um, and then it's you. You're effectively doing a search process, um, and using that information in in, you know, very helpful ways.
So, to go to your dream of, uh, modeling a cell, uh, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that AlphaFold, I mean, there's just so many leaps, so AlphaFold solved, if it's fair to say, protein folding, and there's so many incredible things we could talk about there, including the open sourcing, uh, the everything you've released. AlphaFold three is doing protein RNA DNA interactions, which is super complicated and and fascinating.
This, uh, amenable to modeling AlphaGenome, uh, predicts, uh, how small genetic changes, like if we think about single mutations, how they link to actual, uh, function. So, um, those are. It seems like it's creeping along to sophisticated to to much more complicated, uh, things like a cell, but a cell has a lot of really complicated components.
yeah. So what I've tried to do throughout my career is I have these really grand dreams. And then I try to, as you've noticed, and then I try to break, but I try to break them down. I any, you know, it's easy to have a kind of, uh, a c crazy ambitious dream, but the the the trick is, how do you break it down into manageable, achievable, uh, interim steps that are meaningful and useful in their own right?
And so virtual cell, which is what I call the project of modeling a cell, uh, I've had this idea, you know, of wanting to do that for maybe more like 25 years. And, uh, I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the the, you know, he founded the Crick Institute and and won the Nobel Prize in in 2001. Uh, it, it is is. We've been talking about it since, you know, t before the year, you know, in the 90s, and um, and I come used to come back to it every five years.
It's like, what would you need to model of the full internals of a cell? So that you could do experiments on the virtual cell? And what those experiment? PR, you know, in silico, and those predictions would be useful for you to save you a lot of time in the wet lab, right? That would be the dream. Maybe you could hundred x speed up experiments by doing most of it in silico, the search in silico, and then you do the validation step in the wet lab.
That would be, that's the, that's the dream. And so, um, but maybe now finally, uh, so I was trying to build these components, AlphaFold being one that that would, uh, allow you eventually to model the full interaction, a full simulation of a cell. And I'd probably start with a yeast cell, and partly that's what Paul Nurse studied, because a yeast cell is like a full organism, that's a single cell, right? So it's the kind of simplest single cell organism, and so it's not just a cell, it's a full organism.
And um, and yeast is very well understood, and so that would be a good candidate for, uh, uh, uh, a, a kind of full simulated model. Now, AlphaFold is the is the solution to the kind of static picture of what is a what is a protein look 3D structure of protein look like, a static picture of it. But we know that biology, all the interesting things happen with the dynamics, the interactions, and that's what AlphaFold three is is the 1st step towards, is modeling those interactions.
So 1st of all, pairwise, you know, proteins with proteins, proteins with RNA and DNA. But then, um, the next step after that would be modeling maybe a whole pathway, maybe like the Torah pathway, that's involved in cancer or something like this. And then eventually you might be able to model, you know, a whole cell.
Also, there's another complexity here that stuff in a cell happens at different time scales. Is that tricky? It's like, they're, you know, protein, uh, folding is, you know, super fast. Um, I don't know all the biological mechanisms, but some of them take a long time, and so is that that's an level. So the levels of interaction has a different temporal scale that you have to be able to model.
so that would be hard. So you'd probably need several simulated systems that can interact at these different temporal dynamics. Or at least, uh, maybe it's like a hierarchical system, so, um, you can jump up and down the the different temporal stages.
So can you avoid? I mean, one of the challenges here is not avoid simulating, for example, the the the quantum mechanical aspects of any of this, right? You want to not overmodel, you could skip ahead to just model the really high level things that get you a really good estimate of what's about to happen.
so you w. You gotta make a decision when you're modeling any natural system. What is the cutoff level of the granularity that you're gonna model it to? That, uh, then captures the dynamics that you're interested in? So probably for a cell, I, I would hope that would be the protein level, uh, and that one wouldn't have to go down to the atomic level. Um, so, you know, and of course, that's where AlphaFold start kicks in, so that would be kind of the basis.
And then you'd build these, um, uh, higher level simulations that, um, take those as building blocks, and then you get the emergent behavior.
apologize for the pothead questions ahead of time, but, uh, well, do you think, uh, we'll be able to simulate or model the origin of life? So being able to simulate the 1st from from non living organisms, the the birth of a living organism?
I think that's, uh, one of the, of course, one of the deepest and most fascinating questions. Um, I love that area of biology, you know? Uh, these people like, there's a great book by Nick Lane, one of the top top experts in this area, called the the ten great inventions of of of evolution. I think it's fantastic, and it also speaks to what the great filters might be. Bef, you know, prior, or are they ahead of us?
Think? I think they're most likely in the past, if you read that book of how unlikely to go, you know, have any life at all. And then single cell to multicell seems an unbelievably big jump that took like a billion years, I think, and on Earth to do right, so it shows you how hard it was, right,
bacteria were super happy for a very long
for a very long time time. before they captured mitochondria. Somehow, right? I don't see why not, why AI couldn't help with that, some kind of simulation again, it's again, it's a bit of a search process through a combinatorial space. Here's like all the che, you know, the chemical soup that that you start with, the primordial soup that, you know, maybe was on Earth near these hot vents. Here's some initial conditions. Can you, uh, generate something that looks like a cell?
So perhaps that would be a next stage after the virtual cell project is, well, how, how could you actually, um, something like that emerge from the chemical soup?
Well, I would love it if there was a move 37 for the origin of life. I think that's one of the so great mysteries. I think ultimately what we'll figure out is there a continuum. There's no such thing as a line between non living and living, but if we can make that rigorous, that that the very thing from the big big bang to today has been the same process. If we can break down that wall that we've constructed in our minds of the actual origin of, uh, from non-living to living.
And it's not a line, that it's a continuum that connects physics and chemistry and biology. There's there's no line.
I mean, this is my whole reason why I've worked on AI and AGI my whole life, because I think it can be the ultimate tool to help us answer these kind of questions. And I don't really understand why, um, you know, the average person doesn't think, like, worry about this stuff more, like, how, how, how can we not have a good definition of life and not and not living and non-living, and the nature of time, and let alone consciousness and gravity and all these things.
It's it's just, and quantum mechanics weirdness, it's just to me, it's I've always had this, this sort of screaming at me in my face the whole, and it that's, it's getting louder, you know, it's like, how, what is going on here, you know, in in, and I mean that in the deeper sense, like in the, you know, the nature of reality, which has to be the ultimate question, uh, that would answer all of these things. It's sort of crazy if you think about it.
We can stare at each other and all these living things all the time. We can inspect it in microscopes and take it apart, uh, almost down to the atomic level, and yet we still can't answer that clearly in a simple way. That question of how do you define living? It's kind of amazing.
yeah. Living, you can kinda talk your way out of thinking about. But like consciousness, like, we have this very obviously subjective conscious experience. Like we're at the center of our own world, and it it feels like something. And then how, how, how are you not screaming at the mystery of it all? We haven't, I mean, but really, humans have been contending with the mystery of the world around them. Uh, for a long, long. There's a lot of mysteries, like, what's up with the sun and and the rain, like, what's that about?
And then, like, last year we had a lot of rain and this year we don't have rain. Like, what did we do wrong? H- Humans have been asking that question for a long time.
exactly, so we're quite, I guess we've, we've developed a lot of mechanisms to cope with this. Uh, these deep mysteries that we can't fully, we can see, but we can't fully understand. And we have to have to just get on with daily life and and and we get, we keep ourselves busy, right? In a way, do we keep ourselves distracted?
yeah. I mean, weather is one of the most important questions of human history. We still, that's, that's the go-to small talk direction of of the weather.
especially in England. Yeah, and then Yeah,
it's which is, you know, famously, is an extremely difficult system to model, and, uh, even that system, uh, uh, the Google DeepMind has made progress on.
yes, we've, yeah. We've created the the best weather prediction systems in the world, and they're better than traditional fluid dynamics sort of systems that usually calculated on massive supercomputers takes days to calculate it. Um, we've managed to model a lot of the weather dynamics with neural network systems, with our weather next system. And again, it's interesting that those kinds of dynamics can be modeled, even though they're very complicated, almost bordering on chaotic systems in some cases.
A lot of the interesting aspects of that, um, uh, can be modeled by these neural network systems, including very recently. We had, you know, cyclone prediction of where, you know, paths of hurricanes might go. Of course, super useful, super important for the world, and and and it's super important to do that very timely and very quickly, and as well as accurately. And, uh, I think it's very promising direction again of, you know, simulating and uh, uh, so that you can run forward predictions and simulations of very complicated real world systems.
I should mention that, uh, I've gotten a chance in, uh, Texas to meet a community of folks called the Storm Chasers. And w- What's really incredible about them, I need to talk to them more, is they're extremely tech savvy. Because what they have to do is they have to use models to predict where the storm is. So they're it's this, it's, it's this beautiful mix of like, crazy enough to, like, go into the eye of the storm.
And like, in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of of weather. Yeah, it's it's a a beautiful balance of, like, being in it as living organisms and the the cutting edge of science. So they actually might be using, uh, a deep mind system, so that's
yeah, they are, but hopefully they are, and I I'd love to join them in one of those trips. Because they look amazing, that's great, right, to actually experience it one time.
e- exactly. And then also to experience the correct prediction of where something will come and how it's going to evolve. It's incredible. You've estimated that we'll have AGI by 2030. Um, so there's interesting questions around that. How will we actually know that we got there? Uh, and uh, what may be the move quote, move 37 of AGI?
My estimate is sort of 50 chance by in the next five years, so, you know, by 2030, let's say. And, uh, so I think there's a good chance that that could happen. Part of it is what, what is your definition of AGI? Of course, people arguing about that now, and p- and, uh, mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has, right? So we know our brains are pretty much general Turing machines, approximate, and of course, we created incredible modern civilization with our minds.
So that also speaks to how general the brain is. And, um, for us to know we have a a true AGI, we would have to, like, make sure that it has all those capabilities. It isn't kind of a jagged intelligence, where some things it's really good at, like today's systems, but other things it's really, uh, flawed at. And and that's what we currently have with today's systems. They're not consistent, so you'd want that consistency of intelligence across the board.
And then we have some missing, I think, capabilities, like sort of, uh, the true invention capabilities and creativity that we were talking about earlier, so you'd want to see those. How you test that, um, I think you just test it. One way to do it would be kind of brute force test of tens of thousands of cognitive tasks that, um, you know, we know that humans can do, uh. And maybe also make the system available to, uh, a few hundred of the world's top experts.
Uh, the Terrence Tao's of each each subject area. And see if they can find, you know, given, give them a month or two and see if they can find, uh, an obvious flaw in the system. And if they can't, then I think you're, you're pretty, uh, you know, pretty. You can be pretty confident you we have a a fully general system.
maybe to push back a little bit, it seems like humans are really incredible as the the intelligence improves across all domains. To take it for granted, uh, like you mentioned, Terrence Tao, uh, these brilliant experts. They might quickly, in a span of weeks, take for granted all the incredible things it can do. And then focus in on, ha ha, right there, you know, I, I consider myself a hu- first of all human, uh, and so it's yeah, I identify as human.
Um, there's I, you know, some people listen to me talk and they're like, that guy is not good at talking, the stuttering, the, you know. So, like, even humans have obvious across domains limits, uh, even just outside of of course, calc, mathematics and physics and so on. It I I. I wonder if it will take something like a move 37, so on the positive side versus like a barrage of 10,000 cognitive tasks, where it will be one or two, where it's like, Holy shit, this So I think is special.
there are exactly so. I think there's the sort of blanket testing to just make sure you've got the consistency. But I think there are the sort of lighthouse moments like the move 37 that will I would be looking for. So one would be inventing a new conjecture or new hypothesis about physics, like Einstein did so. Maybe you could even run the back test of that very rigorously, like l- have a cutoff of knowledge cutoff of 1900, and then give the system everything that was, you know, that was written up to 1900.
And then and then see if it could come up with special relativity and general relativity, right, like Einstein did that. That would be an interesting test. Another one would be, can it invent a game like Go? Not just come up with move 37, a new strategy, but can it invent a game that's as deep, as aesthetically beautiful, as elegant as Go? And those are the sorts of things I would be looking out for. Uh, uh, and probably a system being able to do, uh, uh, several of those things right for it to be very general.
Um, not just one domain. And so I think that would be the signs, at least that I would be looking for. That we've got a system that's AGI level. And then maybe to fill that out, you would also check their consistency, you know, make sure there's no holes in that system either.
yeah, something like a a new conjecture or scientific discovery, that would be a cool feeling.
Yeah, that would be amazing. So it's not not just helping us do that, but actually coming up with something brand new.
And you would be in the room for that. And Ideally. so it would be like probably two or three months before announcing it, and you would just be sitting there, something trying not to tweet.
something like that, exactly. It's like, what is this amazing new, you know, physics uh idea? And then we would probably check it with world experts in that domain, right, and validate it and kind of go through its workings, and it, I guess it would be explaining its workings too. Um, yeah, be an amazing moment.
do you worry that we, as humans, even expert humans like you, might miss it, might Well, it miss?
may be c- pretty complicated, so it could be the analogy I give there is. I don't think it will be, um, uh, uh, totally mysterious to the to the best human scientists. But it may be a bit like, for example, in chess. If I was to talk to Garry Kasparov or Magnus Carlsen and play a game with them and they make a brilliant move, I might not be able to come up with that move. But they could explain why afterwards that move made sense, and we would be able to understand it to some degree.
Not to the level they do, but in it, you know, if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you're thinking about. Um, uh, I I think that that will be very possible for the best human scientists.
But I wonder, maybe you can, you can educate me on the side of Go. I wonder if there's moves for Magnus or Gary where they at first will dismiss it as a bad move.
Yeah, sure, it could be, but then afterwards, they'll figure out with their intuition that that this why this works. And then, and then, and then. Empirically, the nice thing about games is one of the great things about games is you can. It's it's a sort of scientific test. Does it do you win the game or not win? And then, um, that tells you, okay, that move in the end was good, that strategy was good. And then you can go back and analyze that, and and explain, even to yourself, a little bit more why explore around it.
And that's how chess analysis and things like that works. So perhaps that's why my brain works like that, cause I I've been doing that since I was four. And you're train, you know, tra- it's sort of hardcore training in that way.
But even even now, like when I generate code, the there is this kind of nuanced, fascinating con contention that's happening. Where I might at first identify as a set of generated code as incorrect in s in some interesting, nuanced ways. But then I'm always have to ask the question, is there a deeper insight here that, uh, that I'm the one who's incorrect? And th- that's going to, as the systems get more and more intelligent, you're gonna have to contend with that.
It's like, what, what, what do you? Is this a bug or a feature of what you just came up with?
yeah, and they're gonna be pretty complicated to do, but of course it will be. You could imagine also AI systems that are producing that code or whatever that is, and then human programmers looking at. But also not unaided with the help of AI tools as well. So it's gonna be kind of an interesting, you know, d, maybe different AI tools to the ones that the more va- you know, mo- kind of monitoring tools, to the ones that generated it.
So if we look at a AGI system, sorry to bring it back up, but Alpha evolve super cool, so Alpha evolve enables on the programming side, something like recursive self improvement. Uh, potentially, like, what if you can imagine what that AGI system, maybe not the first version, but a few versions beyond that? What does that actually look like? Do you think it will be simple? Do you think it will be something like a self-improving program in a simple one?
I mean, potentially that's possible, I would say, um, I'm not sure it's even desirable, because that's a kind of like hard takeoff scenario. But but you, you these current systems like AlphaFold, they have, you know, human in the loop deciding on various things. They're separate hybrid systems that interact. Uh, one could imagine eventually doing that end to end. I don't see why that wouldn't be possible, but right now, um, you know, I think the systems are not good enough to do that.
In terms of coming up with the architecture of the code, um, and again, it's a little bit reconnected to this idea of coming up with a new conjecture or hypothesis. How like they they're good if you give them very specific instructions about what you're trying to do, um, but if you give them a very vague, high level instruction, that wouldn't work currently. Like, Uh, and I think that's related to this idea of, like, invent a game as good as Go, right?
Imagine that was the prompt that's that's pretty underspecified. And so the current systems wouldn't know, I think, what to do with that, how to narrow that down to something tractable. And I think there's similar. Like, look, just make a better version of yourself, that's too, that's too unconstrained. But we've done it in s, you know, in and as you know, with AlphaFold, like things like faster matrix multiplication. So when you, when you hone it down to very specific thing you want, um, it's very good at incrementally improving that.
But at the moment, these are more like incremental improvements, sort of small iterations. Whereas if you know I, if you wanted a big leap in, uh, understanding, you'd need a you need a much larger, uh, advance,
yeah. But it could also be sort of to push back against hard takeoff scenario. It could be just a sequence of, um, incremental improvements like matrix multiplication. Like it has to sit there for days, thinking how to incrementally improve a thing, and that it does so recursively. And as you do more and more improvement, it'll slow down. Because there'll be like a like, uh, the path to AGI won't be like a uh, it'll be a gradual improvement over time.
Yes, if it was just incremental improvements, that's how it would look. So the question is, could it come up with a new leap, like the transformer's architecture, right? Could it have done that back in 2017, when, you know, we did it and brain did it? And it's it's not clear that that these systems, something like AlphaFold, wouldn't be able to do, make such a big leap. So for sure, these systems are good. We have systems, I think, that can do incremental hill climbing, and that's a kind of bigger question about is that all that's needed from here, or do we actually need one or two more?
Um, uh, big breakthroughs.
And can the same kind of systems provide the breakthroughs also? So make it a bunch of S curves, like incremental improvement, but also every once in a while leaps.
Yeah, I don't think a a anyone has systems that can sh- have shown unequivocally those big leaps. The the the right. We have a lot of systems that do the hill climbing of the S curve that you're currently on.
Yeah, and that would be the move 37 is a L
Yeah, I think would be a leap, um, something like that.
Uh, do you think s the scaling laws are holding strong on? uh, pre-training, post-training, test time, compute? Uh, do you, um, on the flip side of that, anticipate AI progress hitting a wall?
We certainly feel there's a lot more room just in the scaling. So, um, actually, all steps pre-training, post-training and inference time. So, uh, there's sort of three scalings that are happening. C- concurrently. Um, and we a again, there, it's about how innovative you can be. And we, you know, we pride ourselves on having the broadest and um, deepest research bench. Uh, we have amazing, you know, incredible, uh, researchers and, uh, people like Noam Shazeer, who, you know, came up with transformers and s, and Dave Silver, you know, who led the AlphaGo project and so on.
And um, it's, it's, it's w- that research base means that if some new new breakthrough is required, like an AlphaGo or Transformers, uh, I would back us to be the place that does that. So I'm actually quite like it when the terrain gets harder, right? Because then it veers more from just engineering to to true research, and, you know, re or research plus engineering, and that's our sweet spot. And I I think that's harder, it's harder to invent things than to than to, um, you know, fast follow.
And um, so, you know, we don't know. I would say it's a it's kind of fifty fifty, whether new things are needed or whether the scaling, the existing stuff is gonna be enough. And so, in true kind of empirical fashion, we're pushing both of those as hard as possible. The new blue sky ideas, and, you know, maybe about half our resources on that, and then and then, uh, scaling to the max, the the current, the current capabilities.
And um, we're still seeing some, f- you know, fantastic progress on, uh, each different version of Gemini.
that's interesting, the way you put it in in terms of the deep bench. That if, uh, progress towards AGI is more than just scaling compute, so the engineering side of the problem, and is more on the scientific side, where there's breakthroughs needed, then you feel confident. DeepMind as well, uh, Google DeepMind as well, positioned to kick kick ass in that domain.
well, I mean, if you look at the history of the last decade or fifteen years, um, it's been, I mean, you know, maybe I don't know. 80, 90 of the breakthroughs that mod- that underpins modern AI field today. Was from, you know, originally, Google Brain, Google Research and DeepMind. So, yeah, I would back that to continue, hopefully.
uh. So, on the data side, are you concerned about running out of high quality data, especially high quality human data?
I'm not very worried about that, partly because I think there's enough data, uh, or. And it's been proven to get the systems to be pretty good. And this goes back to simulations again. If you do, you have enough data to make simulations, or so that you can create more synthetic data that are from the right distribution. Obviously, that's the key. So you need enough real world data in order to be able to, uh, uh, create those kinds of generator data generators.
And, um, I think that we're at that step at the moment.
Yeah, he's done a lot of incredible stuff on the side of science and biology, doing a lot with not so much data. I mean, it's still a lot of data, but I guess enough takeoff. get that going. Exactly, Yeah, yeah,
so exactly.
uh. How crucial is the scaling of compute to building AGI? This is a question that's an engineering question, it's a almost a geopolitical question, because it also integrated into that is supply chains and energy. A thing that you care a lot about, which is, um, potentially fusion, so innovating on the side of energy also. Do you think we're gonna keep scaling compute?
I think so for several reasons. I think compute there's, there's the amount of compute you have for training. Uh, often it needs to be co-located, so actually, even like, you know, uh, bandwidth constraints between data centers can affect that, so it's, it's, it's, there's additional constraints even there. And that that's important for training, obviously, the largest models you can. But there's also because now AI systems are in products and being used by billions of people around the world.
You need a ton of inference compute now. Um, and then on top of that, there's the thinking systems, the new paradigm, uh, of the last year, that, uh, where they get smarter, the longer amount of inference time you give them at test time. So all of those things need a lot of compute, and I don't really see that slowing down. Um, and a as AI systems become better, they'll become more useful and there'll be more demand for them.
So both from the training side, the training side actually is is only just one part of that, it may even become the smaller part of of what's needed. Um, uh, uh, in the overall compute that that's required.
yeah, that's one sort of almost meme-y kind of thing, which is like the success and the incredible aspects of Veo three. There's, uh, people kinda make fun of, like, the more successful it becomes, the, you know, the servers are sweating to do the inference.
that's it. Yeah, yeah, exactly. We did a little video of of the s of the servers frying eggs and things, and um, that's right, and and and we're gonna have to figure out how to do that. Um, there's a lot of interesting hardware innovations that we do, as you know, we have our own TPU line and we're looking at like inference only things, inference only chips, and how we can make those more efficient. We're also very interested in building AI systems, and we have done that help with energy usage, so help, um, data center energy, like for the cooling systems, be efficient, um, grid optimization, um, and then eventually things like helping with, uh, plasma containment fusion reactors.
We've done lots of work on that with Commonwealth Fusion and also, uh, one could imagine reactor design, um, and then material design. I think, is one of the most exciting new types of solar material, solar panel material, super con- room temperature superconductors has always been on my list of dream breakthroughs, and, um, optimal batteries. And I think a solution to any, you know, one of those things would be absolutely revolutionary for, you know, climate and energy usage.
And we're probably close, you know, and again, in the next five years to having AI systems that can materially help with those problems.
So if you were to bet, sorry for the ridiculous question, but what? What is the main source of energy, eh? In like, 20, 30, 40 years, do you think it's gonna be nuclear fusion?
I think fusion and solar are the two that I I would bet on. Um, solar, I mean, you know, it's the fusion reactor in the sky, of course, and I think, really, the the problem there is is is batteries and transmission. So, you know, as well as more efficient, more more efficient solar material, perhaps eventually, you know, in space, you know, these kind of Dyson Sphere type ideas. And fusion, I think, is definitely doable. Seems, uh, if we have the right design of reactor and we can control the plasma and, uh, fast enough and so on.
And I think both of those things will actually get solved, so we'll probably have at least those are probably the two primary sources of renewable, clean, almost free, or perhaps free energy.
What a time to be alive. If I, uh, traveled into the future with you, 100 years from now, how much would you be surprised if we've passed, uh, type one Kardashev scale civilization?
I would not be that surprised if there's a h- like, a hundred year timescale from here. I mean, I think it's pretty clear. If we crack the energy problems in one of the ways we've just discussed, fusion or or very efficient solar, um, then if energy is kind of free and renewable and clean, um, then that solves a whole bunch of other problems. So, for example, the water access problem goes away because you can just use desalination.
We have the technology, it's just too expensive. So only, you know, uh, fairly wealthy countries like Singapore and Israel and so on, like, actually use it. But but if it was, uh, cheap, then every then, you know, all countries that have a coast could. But also you'd have unlimited rocket fuel, you could just separate seawater out into hydrogen and oxygen using energy, and that's rocket fuel. So, uh, combined with, you know, Elon's amazing self landing rockets, then it could be like, you sort of like a bus service to to space.
So that opens up, you know, incredible new resources and domains. Uh, asteroid mining, I think, will become a thing, and maximum human flourishing to the stars. Like, that's what I, uh, dream about. As well as, like, Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe. And I I think human civilization will do that in the full sense of time if we get AI right, and, uh, and and and crack some of these problems with it.
Yeah, I wonder what it would look like if you just, uh, a tourist flying through space. You would probably notice Earth, because if you solve the energy problem, you would see a lot of space rockets, probably, so it would be like traffic here in London, but in space, it's just a lot of rockets. And then you would probably see floating in space some kind of source of energy, like solar, potentially. So Earth would just look more on the surface, more, um, technological.
And then then you would use the power of that energy then to preserve the natural, like the rainforest and all that kind of stuff.
Because for the first time in in human history, we wouldn't be, uh, resource constrained. And I think that's could be amazing new era for humanity, where it's not zero sum, right? I have this land, you don't have it. Or if we take, you know, if the tigers have their forest, then the the local villagers can't. What are they gonna use? I I I think that this will help a lot. No, it won't solve all problems because there's still other human f- uh, foibles that will will will still exist, but it will at least remove one.
I think one of the big vectors, which is scarcity of resources, y- you know, including land and raw materials and energy. And, um, we, you know, we should be, as some people call it, like, and others call it about. This kind of radical abundance era where, um, there's plenty of resources to go around. Of course, the next big question is making sure that that's fairly, you know, shared fairly, uh, and everyone in society benefits from that.
So there is something about human nature where I go, you know, it's it's like Borat, like my neighbor, like I w- like you start trouble, we we, we do start conflicts, uh. And that's why games throughout, as I'm learning, actually more and more e- even in ancient history, serve the purpose of pushing people away from war, actually, hot war. So maybe we can figure out increasingly sophisticated video games that pull us, they that give us that, uh, that visceral, scratch the itch of like conflict, whatever that is.
But, uh, but us, the human nature, and then avoid the actual hot wars that would come with increasingly sophisticated technologies. Because we're now we've long passed the stage where the weapons we're able to create can actually just destroy all human civilization. So it's no longer, um, that's no longer a great way t to, uh, start shit with your neighbor. It's better to play a game of chess or football and or football. or or, yeah, Yeah,
and I think, I mean, I think that's what my modern sport is. So, and I love football, watching it, and and I just feel like, uh, and I used to play it a lot as well, and it's, it's, it's, it's, it's very visceral and it's tribal. And I think it does channel a lot of those energies into a which I think is a kind of human need to belong to some, some group. And um, but into a, into a, into a fun way, a a healthy way, and and a not a not destructive way, kinda constructive, uh, thing.
And I think going back to games again is, I think they're originally why they're so great as well for kids to play. Things like chess is they're great little microcosm simulations of the world, they're they are simulations of the world, too. They're simplified versions of some real world situation, whether it's poker or or go or chess, different aspects or diplomacy, different aspects of of the real world. And it allows you to practice at them too. And and cause, you know, how many times do you get to practice a massive decision moment in your life?
You know what job to take, what university to go to? You know, you, you get, maybe, I don't know, a dozen or so key decisions one has to make, and you've got to make those as best as you can. Um, and games is a kind of safe environment, repeatable environment, where you can get better at your decision making process. Um, and it maybe has this an add additional benefit of channeling some energies into, uh, into more creative and constructive pursuits,
well, I think it's also really important to practice, um, losing and winning, like losing is a really, you know, that's why I love games, that's why I love even, um, things like, uh, Brazilian jiu-jitsu. Where you can get your ass kicked in a safe environment over and over. It reminds you about the way about physics, about the way the world works, about sometimes you lose, sometimes you win. You could still be friends with everybody. But that that feeling of losing, I mean, it's a weird one for us humans to like, really like, make sense of, like, that's just part of life.
That is a fundamental part of life is losing.
yeah, and I think in martial arts, as I understand it, but also in things like like chess, there's a lot, at least the way I took it. It's a lot to do with self-improvement, self-knowledge, you know that, okay, so I did this thing. It's not about really beating the other person, it's about maximizing your own potential. If you do it in a healthy way, you learn to use victory and losses in a way. Don't get carried away with victory and and think you're the just the best in the world.
Key and and and the losses keep you humble. And always knowing there's always something more to learn, there's always a bigger expert that you can mentor you, you know, I think you learn that. I I'm pretty sure in martial arts, and and and I think that's also, uh, the way that at least I was trained in chess. And so, f- in the same way. And it can be very hardcore and very important. You, of course, you wanna win, but you also need to learn how to deal with setbacks, uh, in a in a healthy way, that, and and wire that.
That feeling that you have when you lose something into a constructive thing of next time I'm gonna improve this right or get better at this.
There is something that's a source of happiness, a source of meaning, that improvement step. It's not about the winning or losing.
Yes, the mastery. There's nothing more satisfying in a way is like, Oh, wow, this thing I couldn't do before, now I can. And and and again, games and physical sports and and mental sports, they're w- they're ways of measuring. They're beautiful because you can measure that, that progress, yeah, I mean, there right?
there's something about. I guess why I love role playing games, like the, uh, number go up of, like, my I'm the skill tree,
that's it.
like, literally, that is a source of meaning for us humans, whatever our yeah, we're quite, whatever.
we're. We're quite addicted to this sort of, yeah, these numbers going up and, uh, and and and. And maybe that's why we make games like that, because obviously that is something we're, we're, we're hill climbing systems ourselves, right?
yeah, it's. It would be quite sad if we didn't have any mechanism by different color belts, w- exactly,
all of that we do, we do this everywhere, right, where we just have this thing, that it's it's. And, great.
uh, I don't wanna dismiss that. Th- There is a source of deep meaning for us humans. Um, so, one of the incredible stories on the business, on the leadership side, is, um, what Google's has done over the past year. So I, uh, I think it's fair to say that Google was losing on the LLM product side, uh, a year ago. With Gemini, 1.5, and now it's winning with Gemini 2.5. And you took the helm and you led this effort.
What did it take to go from, let's say, quote unquote losing to quote unquote winning in the in in the span of a year?
yeah, well, firstly, it's absolutely incredible team that we have, you know, led by Corey and Jeff Dean and and Oriol and the amazing team we have on Gemini. Absolutely world class, so you can't do it without the best talent. Um, and of course you have, you know, we have a lot of great compute as well. But then it's the research culture we've created, right? And basically coming together both different groups in in Google, you know, there was Google Brain, world class team, and and then the old Deep Mind.
And pulling together all the best people and the best ideas and gathering around to make the absolute greatest system we could. And it was been hard, um, but we're all very competitive, uh, and we, you know, love research. This is so fun to do. Um, and we, you know, it's great to see our trajectory wasn't a given, but we're very pleased with, um, the the where we are. And the rate of progress is the most important thing.
So if you look at where we've come to from two years ago to one year ago to now, you know, I think our we call it relentless progress, along with relentless shipping of that progress, is, um, being very successful. And, you know, um, it's unbelievably competitive. Uh, the whole space, the whole AI space, with some of the greatest entrepreneurs and leaders, uh, and companies in the world, all competing now. Because everyone's realized how important AI is.
Um, and it's very, you know, been pleasing for us to see that progress.
you know, Google's a gigantic company. Uh, can you speak to the natural things that happen? In that case, is the bureaucracy that emerges? Like, you wanna be careful, like, you know, like the the natural kind of. There's, there's meetings and there's managers and that, like, what? What are some of the challenges from a leadership perspective, breaking through that? In order to, like you said, ship like the the number of products, Gemini related products that's been shipped over the past year is just insane.
right, it is, yeah, exactly, that's, that's what relentlessness looks like. Um, I think it's, it's a question of, like any big company, you know, ends up having, uh, a a lot of layers of management and things like that. It's sort of the nature of how it works. Um, but I still operate, and I was always operating with old deep mind as a as a startup, still large one, but still as a startup. And that's what we still act like today, in as with Google Deep Mind, and acting with decisiveness and the energy that you get from the best smaller organizations.
And we try to get the best of both worlds, where we have this incredible billions of users surfaces, uh, and credible products that we can power up with our AI and our and our research. Um, and that's amazing. And you can, you know, that's very few places in the world you can get that. Do incredible world class research on the one hand, and then plug it in and and improve billions of people's lives the next day.
Uh, that's a pretty amazing combination. And we're continually fighting and cutting away bureaucracy to allow the research culture and the relentless shipping culture to flourish. And I think we've got a pretty good balance whilst being responsible with it. You know, as you have to be, uh, as a large company, and also, uh, with a number of y- you know, uh, huge product surfaces that we have.
uh, so a funny thing you mentioned about, like, the the surface of the billion. I I had a conversation with a guy named, um, brilliant guy, uh, here at the British Museum called Ervin Finkel. He's a world expert at cuneiforms, which is a ancient writing on tablets. And he doesn't know about ChatGPT or Gemini. He doesn't even know anything about AI. But his first encounter with this AI is AI mode on Google. He's like, Is that what you're talking about, this AI mode?
And and you know, it's just in. It's just a reminder that there's a large part of the world that doesn't know about this AI thing. yeah. I know And like,
it's funny, cause if you live on, uh, X and Twitter, and I mean, it's sort of, at least my feed, it's all AI. And and there's certain places where, you know, in the valley and certain pockets where everyone's just all they're thinking about is AI. But a lot of the normal world hasn't hasn't come across it yet. But
and that's a great responsibility to the their first interaction, w- um, the the the grand scale of the rural India or a anywhere across the world. Like, you get to
right. And we want it to be as good as possible. And in a lot of cases, it's just under the hood, powering, making something like maps or search work better. And um, and it's ideally, for a lot of those people, it should just be seamless. It's just new technology that makes their lives more, you know, productive and and and helps them.
a bunch of folks on the Gemini product and engineering teams, spoken extremely highly of you. On another dimension that I almost didn't e- even expect, cause I kind of th- think of you as the like, deep scientist and caring about these big research, scientific questions. But they also said, you're a great product guy. Like, how to create a thing that a lot of people would use and enjoy using, so can you maybe speak to what it takes to create a a AI-based product that a lot of people enjoy using?
yeah, well, I mean, again, that comes back from my game design days, where I used to design games for millions of gamers. People forget about that. I've I've had experience with cutting edge technology in product, that that is how games was in the 90s. And so I love actually the combination of cutting edge research and then being applied in a product. And, uh, to power a new experience. And so, um, I think it's the same skill, really, of of, you know, imagining what it would be like to use it viscerally, um, and having good taste.
Coming back to earlier, the same thing that's useful in science, um, I think is is can also be useful in in product design. And, um, I, I've just had a very, you know, always been a sort of multidisciplinary person, so I don't see, uh, the boundaries really between, you know, arts and sciences or product and research. It's it's a continuum for me. I mean, I only work on I like working on products that are cutting edge.
I wouldn't be able, you know, have cutting edge technology under the hood. I wouldn't be excited about them if they were just run-of-the-mill products. Um, so it requires this invention, creativity, cap capability.
hm, what are some specific things you kind of learned about when you, um, even on the LLM side, you're interacting with Gemini? You're like, this doesn't feel like the layout, the the interface, maybe the trade off between the latency, like how how to present to the user, how long to wait and how that waiting is shown, or the reasoning capabilities. There's some interesting things, cause, like you said, it's the very cutting edge. We don't know how to present it, c- how to present it correctly.
So is there some specific things you've you've learned?
I mean, I. It's such a fast evolving space. We're evaluating this all the time, but where we are today is that you wanna continually simplify things. Um, the in, whether that's the interface
I simplify. or or the Yeah,
inter- the what you build on top of the model, you kind of want to get out of the way of the model. The model train is coming down the track and it's improving unbelievably fast. This relentless progress we talked about earlier, you know, you look at 2.5 versus 1.5, and it's just a gigantic improvement, and we expect that again for the future versions. And so the models are becoming more capable. So you've got the interesting thing about the design space in in in today's world, these AI first products is you've got to design.
Not for what the thing can do today, the technology can do today, but in a year's time. So you actually have to be a very technical product person. Because, uh, you've got to kind of have a good intuition for and feel for. W- Okay, that thing that I'm dreaming about now can't be done today. But is the research track on schedule to basically intercept that in six months or a year's time? So you've kind of got to intercept where this highly changing technology's going, as well as the um, uh uh.
New capabilities are coming online all the time that you you didn't realize before. That can allow, like, deep research to work, or now we've got video generation, what do we do with that? Um, this multimodal stuff, you know, is it? One question I have is, is it really going to be the current UI that we have today? These text box chats seems very unlikely give once you think about these super multimodal uh uh systems. Shouldn't it be something more like minority report, where you're, you're sort of vibing with it in a in a co in a kind of collaborative way, right?
It seems very restricted today. I think we'll look back on today's interfaces and products and systems as quite archaic in maybe in a just a couple of years. So I think there's a lot of SP space, actually, for innovation to happen on the product side, as well as the the research side.
and then we are offline talking about this. Keyboard is the the the open question is how, when, and how much will we move to audio as the primary way of interacting with the machines around us versus typing stuff?
Yeah, I mean, typing is a very low bandwidth way of doing, even if you're a very fast, you know, typer. And I think we're gonna have to start utilizing other devices, whether that's smart glasses, you know, audio earbuds, um, and eventually, maybe some sorts of neural devices. Where we can increase the the input and the output bandwidth to something, uh, you know, maybe a hundred x of what is today.
I think that, you know, underappreciated art form is the interface design. Because I think you cannot unlock the power of the intelligence of a system if you don't have the right interface. The interface is really the way you unlock its power. It's such an interesting question of how to do that. So how, how y- you would think, like, getting out of the way is an real art form.
yes, you know, it's the sort of thing that I guess Steve Jobs always talked about, right? It's simplicity, beauty and elegance that we want, right? And we're not there, nobody's there yet, in my opinion. And that's what I would like us to get to. Again, it sort of speaks to, like, go again, right? As a game, the most elegant, beautiful game. Can you, you know, that, uh, uh, can you make an interface as beautiful as that?
And actually, I think we're gonna enter an era of a AI generated interfaces that are probably personalized to you. So it fits the way that you, your aesthetic, your feel, the way that your brain works. And um, and and and the AI kinda generates that depending on the task. You know, that feels like that's probably the direction we'll end up in,
Yeah, cause some people are power users and they want every single parameter on the screen and everything e- everything based. Like, perhaps me with a keybo- keyboard-based navigation, I'd like to have shortcuts for everything, and some people like the minimalism just hide of
all of that complexity.
of of completely. Yeah, uh, well, I'm glad you have a Steve Jobs mode in you as well. This is great. Einstein mode, Steve Jobs mode. Um, all right, let me try to trick you into answering a question When, when will Gemini 3 come out? Is it before or after GTA 6? The world waits for both. And what does it take to go from two five to three? Oh, because it seems like there's been a lot of releases of two five, which are already leaps in performance.
So what, what does it even mean to go to a new version? Is it about performance? Is this about a completely different flavor of an experience?
yeah, well. So the way it works with our different, uh, version numbers is we, you know, we try to collect. So maybe it takes, you know, roughly six months or something to to do a new kind of full run and the full productization of a new version. And during that time, lots of new, interesting research, iterations and ideas come up, and we sort of collect them all together. That, you know, you could imagine the last six months worth of interesting ideas on the architecture front.
Uh, maybe it's on the data front. It's like many different possible things, and we collect, package that all up, test which ones are likely to be useful for the next iteration, and then bundle that all together. And then we start the new, you know, giant hero training run, right? And and then, uh, and then, of course, that gets monitored, uh, and then at the end, then there's the of the pre-training, then there's all the post-training. There's many different ways of doing that, different ways of patching it.
So there's a whole experimentin phase there, which you can also get a lot of gains out. And that's where you see the version numbers usually referring to the base model, the pre-train model, and then the interim versions of 2.5, you know, and the different sizes and the different little, uh, additions. They're often, uh, patches or post-training ideas that can be done afterwards, uh, off the same basic architecture. And then, of course, on top of that, we also have different sizes pro and flash and flashlight that are often distilled from the biggest ones, you know, the flash model from the pro model.
And that means we have a range of different choices. If you are the developer of Do you wanna pro prioritize performance or speed, right, and cost? And we like to think of this Pareto frontier of of, you know, on the one hand, uh, the y-axis is, you know, like performance, and then the the the x-axis is, you know, cost or latency and and speed, uh, basically. And we, we have models that completely define the frontier. So whatever your trade-off is that you want as an individual user, or as a as a developer, you should find one of our models satisfies that constraint.
so behind the version changes, there is a big hero run, and then there's, uh, just an insane complexity of productization. Then there's the distillation of the different sizes along that Pareto front. And then, as w- each step you take, you realize there might be a cool product. There's side quests, yeah, but and then you also don't wanna take too many side quests. Because then you have a million versions of a million products,
yes, precisely. right? It's very unclear, yeah,
but you also get super excited cause it's super cool. Like, how does even you look at Veos? Very cool. How does it fit into the bigger thing, right? Yeah,
And then you're constantly this process of converging upstream. We call it, you know, ideas from the from the product surfaces, or or or from the post-training, and and even further downstream than that. You, you kind of upstream that into the the core model training for the next run, right? So then the main model, the main Gemini track, becomes more and more general, and eventually, you know, AGI. one hero run at Yes, exactly,
a time. few hero runs later, Uh, yeah. So sometimes when you release these new versions, or every version, really, are benchmarks, um, productive or counterproductive for showing the performance of a model?
you need them, and and uh, but it's important that you don't overfit to them, right? So they shouldn't be the end with a be all and end all. So there's there's LM Arena, or it used to be called Elemsys. That's one of them that turned out sort of organically to be one of the the main ways people like to test these systems, at least the chatbots. Um, obviously, there's loads of academic benchmarks on from from that test, uh, mathematics and coding ability, general language ability, science ability and so on.
And then we have our own internal benchmarks that we care about. It's a kind of multi-objective y- you know, optimization problem, right? You w- you don't wanna be good at just one thing. We're trying to build general systems that are good across the board and you try and make no regret, uh, improvements, so where you improve in, like, you know, coding, uh. But it doesn't reduce your performance in other areas, right? So that's the hard part, cause you c- you can.
Of course, you could put more coding data in, or you could put more, um, I don't know, gaming data in. But then does it make worse your language, uh, system, or or, uh, in in your translation systems and other things that you care about? So it's you've gotta kinda continually monitor this l- increasingly larger and larger suite of of benchmarks. And also there's, uh, when you stick them into products, these models. You also care about the direct usage and the direct stats and the signals that you're getting from the end users, whether they're coders or or or the average person using, uh, using the chat interfaces,
Yeah, because ultimately you wanna measure the usefulness, but it's so hard to convert that into a number. It's it's really vibe-based benchmarks across a large number of users, and it's hard to know. And I, it would be just terrifying to me to, you know, you have a much smarter model, but it's just something vibe-based. It's not not not quite working. That's just s s scary, cause and everything you just said, it has to be smart and useful across so many domains.
So you you get super excited, cause it's all of a sudden solving programming problems it never been able to solve before, but now it's crappy at poetry or something, and it's just, I don't know. That's a stressful, that's so difficult, um, to balance, because to balance, and because you can't really trust the benchmarks, you really have to trust the end users.
yeah. And then other things that are even more esoteric come into play, like, um, you know, the style of the persona of the the the system, you know how it, you know. Is it verbose, is it succinct, is it humorous, you know, and they and different people like different things. So, um, you know, it's very interesting. It's almost like cutting edge part of psychology, research or person personality research. You know, I used to do that in my PhD, like five factor personality.
What do we actually want our systems to be like? And different people will like different things as well. So these are all just sort of new problems in product space that I don't think have ever really been tackled before. But, um, we're gonna sort of ha- rapidly have to deal with now.
I think it's a super fascinating space, developing the character of the thing. And in so doing, it puts a mirror to ourselves. What are the kind of things, um, that we like? Cause prompt engineering allows you to control a lot of those elements. But can the product, uh, make it easier for you to, uh, control the different flavors of those experiences, the different characters that you interact with? yeah, exactly, so So what's the probability of Google DeepMind winning?
well, I don't see it as sort of winning. I mean, I think we need to think winning is the wrong way to look at it, given how important and consequential what it is we're building. So, funnily enough, I don't. I try not to view it like a game or competition, even though that's a lot of my mindset. It's it's about st. In my view, all of us have, those of us at the leading edge, uh, have a responsibility to, um, steward this unbelievable technology that could be used for incredible good, but also has risks.
Um, steward it safely into the world for the benefit of humanity. That's always, um, what I've, um, uh uh, dreamed about and what we've always tried to do. And I hope that's what eventually the community, maybe the international community, will rally around. When it becomes obvious that as we get closer and closer to to AGI, that, um, that's what's needed.
I agree with you, I think that's beautifully put. You've said that, um, you talk to and are on good terms with the leads of some of these, uh, labs. As the competition heats up, uh, how hard is it to maintain sort of those relationships?
It's been okay. So far. I try to pride myself in being, uh, collaborative. I'm a collaborative person. Research is a collaborative endeavor, science is a collaborative endeavor, right? It's all good for humanity in the end. If you cure incredible, you know, terrible diseases, and you come up with an incredible cure. This is net win for humanity. And the same with energy, all of the things that I'm interested in, in, in helping solve with AI. So I just want that technology to exist in the world and be used for the right things.
And and and the the kind of the benefits of that, the productivity benefits of that being shared for every the benefit of everyone. So I try to maintain good relations with all the leading lab, uh, people, they're very interesting characters, many of them, as you might expect. Um, but yeah, I'm on good terms, I I hope, with pretty much all of them. And, uh, I I think that's gonna be important when when things get even more serious than they are now.
Uh, that there are those communication channels, and, uh, that's what will facilitate, uh, cooperation or collaboration, if that's what reco- is required, especially on things like safety,
yeah, I hope there's some collaboration on stuff that's, uh, sort of less high stakes. And in so doing, serves as a mechanism for maintaining friendships and relationships. So, for example, I think the internet would love it if you and Elon somehow collaborated on creating a video game, that kind of thing. That I think that enables camaraderie and good terms. And also, you two are legit gamers, so it's just fun to fun to grapple yeah, that would be with.
awesome, and we've talked about that in the past, and it may be a cool thing that that, you know, we can do. And I agree with you, it'd be nice to have, um, kind of side projects. In a way where where one can just lean into the collaboration aspect of it. And it's a sort of, uh, win-win for both sides, and it's, um, and it kind of builds up that, that, that, uh, collaborative muscle.
I see the scientific endeavor as that kind of side project for humanity. And I I think deep Google DeepMind has been really pushing that. Uh, I would love it if to see other labs do more scientific stuff and then collaborate, cause it just seems like easier to collaborate on the big scientific questions.
I agree, and, uh, I would love to see a lot of people. A lot of the other labs talk about science, but I think we're really the only ones using it for science and doing that. And that's why projects like AlphaFold are so important to me, and I think to our mission is to show, uh, how AI can vis, you know, be clearly used in a very concrete way for the benefit of humanity. And and also, we spun out companies like Isomorphic off the back of AlphaFold to do drug discovery, and it's going really well.
And build sort of, you know, you can think of build additional AlphaFold type type systems to go into chemistry space to help accelerate drug design. And the examples I think we need to show, uh, and society needs to understand are what AI can bring, these huge benefits.
well, from the bottom of my heart, thank you for pushing the scientific efforts forward. W- with rigor, with fun, with humility, all of it. I just love to see and still talking about P equals NP, I mean, it's just incredible, so I love it. Um, there, there, there's been, uh, seemingly a war for talent. Some of it is meme, I don't know. Um, what do you think about Meta buying up talent with huge salaries and and the heating up of this battle for talent?
And I, I should say that I think a lot of people see DeepMind as a really great place to do, uh, cutting edge work. For the reasons that you've outlined is like, there's this vibrant scientific culture.
yeah, well, look, uh, of course. Um, you know, there's a strategy that that Meta is taking right now. I think that, um, from my perspective, at least. I think the people that are real, uh, believers in the mission of AGI and what it can do, and understand the real consequences, both good and bad, from that, and what's what that responsibility entails. I think they're mostly doing it to be like myself, to be on the frontier of that research.
So, you know, they can help influence the way that goes and steward that technology safely into the world. And, you know, Meta right now are not at the frontier. Maybe they'll, they'll manage to get back on there. And, um, you know, it's probably rational what they're doing from their perspective, because they're behind and they need to do something. But I think, um, there's more important things than than just money. Of course, one has to pay, you know, people, their market rates and all of these things, and that continues to go up.
Um, but as pro and and and I was expecting this because more and more people are finally realizing leaders of companies. What I've always known for 30 plus years now, which is that AGI is the most important technology probably that's ever gonna be invented. So in some senses, it's it's rational to be doing that, but I also think there's a much bigger question. I mean, people in AI these days are very well paid. You know, I, I remember when we were starting out back in 2010, you know, I didn't even pay myself for a couple of years, cause we just wasn't enough money.
We couldn't raise any money. And these days, interns are being paid, you know, the amount that we raised as our first entire seed round. So it's pretty funny. And I remember the days where we used, I used to have to to work for free and p and almost pay my own way to do an internship, right? And now it's all the other way around. But that's just how it is, it's the new world. And um, but I think that, you know, we've been discussing, like, what happens post AGI?
And energy systems are solved and so on. What is even money going to mean? So I think, uh, you know, and the economy, and and we're gonna have much bigger issues to work through in. How does the economy function in that world and companies? So I think, you know, it's a little bit, uh, of a side issue about, uh, uh, salaries and things of like that today,
Yeah, when you're facing such gigantic consequences and and gi gigantic, fascinating scientific questions, right? Which for sure.
may be only a few years away. So
So, on the practical, sort of pragmatic sense, uh, if we zoom in on jobs, we can look at programmers, because it seems like AI systems are currently doing incredibly well at programming and increasingly so. So a lot of people that, uh, program for a living, love programming, are worried they will lose their jobs. How worried should they be, do you think? And what's the right way to, uh, sort of adjust to the new reality and ensure that you survive and thrive as a human in the programming world?
well, it's interesting that programming, and it's again counterintuitive to what we thought, uh, years ago. Maybe that some of the skills that we think of as harder skills are turned out maybe to be the easier ones for various reasons. But, you know, coding and math, because you can create a lot of synthetic data and verify if that data is correct. So be because of that nature of that, it's easier to make things like synthetic data to train from.
Um, it's also an area, of course, we're all interested in, cause as programmers, right, to help us and get faster at it and more productive. So I think the for the next era, like the next five, ten years, I think what we're gonna find is people who are kind of embrace these technologies, become almost at one with them. Um, whether that's in the creative industries or the technical industries, will become sort of superhumanly productive. I think so, the great programmers will be even better, but they'll be even 10x, even what they are today.
And because there, you'll be able to use their skills to utilize the the tools to the maximum. Uh, uh, you know, exploit them to the maximum. And um, so I think that's what we're gonna see in the next domain. Um, so that's gonna cause quite a lot of change, right? And so that's coming. A lot of people benefit from that. So I think one example of that is if coding becomes easier, um, it becomes available to many more creatives to do more.
Uh and uh. But I think the top programmers will still have huge advantages. As terms of specifying, going back to specifying what the architecture should be, the question should be how to guide these. Um, uh, coding assistants in a way that's useful and, you know, check whether the code they produce is good. So I think there's plenty of um uh headroom there for the foreseeable, you know, next few years.
So I think there's th. There's several interesting things there. One is there's a a lot of imperative to just get better and better consistently of using these tools. So they are, they're riding the wave of the improvement, improving models versus, like, competing against them. But s sadly, but that's the the nature of of life on earth. Um, there could be a huge amount of value to certain kinds of programming at the cutting edge, and less value to other kinds.
For example, it could be like, you know, front end web design might, uh, be more amenable to to to, as as you've mentioned, to generation, uh, by AI systems. And maybe, for example, game engine design or something like this, or back end design, or s, or guiding systems. In high performance situations, high performance programming type of design decisions, that might be extremely valuable, but it it will shift where the humans are needed most. And that's scary for people to
yeah, I can. adjust. I think that's right. There. There. At any time where there's a lot of disruption and change, you know, and we've had this, it's not just this time, we've had this in many times in human history, with the internet, uh, mobile, but before that was the industrial revolution. Um, and it's gonna be one of those eras where there will be a lot of change. I think there'll be new jobs we can't even imagine today, just like the internet created.
And then those people with the right skill sets to ride that wave will become incredibly, uh, valuable, right? Those skills. But maybe people will have to relearn or adapt a bit, uh, their current skills. And it's the the thing that's gonna be harder to deal with this time around is that. I think what we're gonna see is something like probably 10 times the impact the industrial revolution had, and but 10 times faster as well, right? So instead of 100 years, it takes ten years, and so that's gonna make, you know, it's like 100x, uh, the impact and the speed combined.
So that's what's, I think, gonna make it more difficult for society to to to deal with, and it's G. There's a lot to think through, and I think we need to be discussing that right now. And I, I, you know, encourage top economists in the world and philosophers to start thinking about, um, uh, w. How should is society gonna be affected by this, and what should we do? Including things like, um, y, you know, uh, universal basic provision or something like that, where a lot of the um, increase productivity, uh, gets shared out and distributed, uh, to society, um, and maybe in the form of surface services and other things.
Where if you want more than that, you still go and get some incredibly rare skills and things like that, um, and and make yourself unique. Um, but uh, uh. But there's a basic provision that is provided.
And if you think of government as a technology, there's also interesting questions, not just in economics, but just politics. How do you design a system that's responding to the rapidly changing times such that you can represent the different pain that people feel from the different groups? And how do you reallocate resources in a way that, um, addresses that pain and represents the hope and the pain and the fears of different people? Uh, in a way that doesn't lead to division, because politicians are often really good at sort of fueling the division and using that to get elected.
The other m defining the other and then saying that's bad. And sort of based on that, I think that's often counterproductive to leveraging a rapidly changing technology. How to, uh, help the world flourish. So we almost, uh, need to improve our political s systems as well rapidly, if you think of them as a technology.
definitely, and I think I think we'll need new governance, uh, structures, institutions, probably to help with this transition. So I think political philosophy and political science is gonna be key, uh, to that. But I think the number one thing, 1st of all, that is to create more abundance of resources, right? Then there's the GR, so that's the number one thing. Increase productivity, get more resources, maybe eventually get out of the zero sum situation. Then the 2nd question is how to use, uh, those resources and distribute those resources.
But yeah, you can't do that without having that abundance. 1st
uh, you mentioned to me, uh, the book The Maniac, uh, by Benjamin Labatut, a book on, uh, 1st of all about you. There's a bio about you. Um, it's strange. Yeah, it's, uh, it's unclear. Yes, sure, it's unclear how much is fiction, how much is reality. Um, but I think the central figure, that is, uh, John von Neumann, I would say, is a haunting and beautiful exploration of madness and genius. And let's say, the double edged uh sword of discovery.
And, you know, for, um, people who don't know, John Von Neumann is a kind of legendary mind. He contributed to quantum mechanics, he was on the Manhattan project. He is widely considered to be the father of, or pioneer the modern computer and AI, and so on. So his many people say he's like one of the smartest humans ever, which is fascinating. And what's also fascinating is as as a person who saw nuclear science and physics become the atomic bomb.
So you, you got to see ideas become a thing that has a huge amount of impact on the world. He also foresaw the same thing for computing. He's he, and that's the a little bit again beautiful and haunting aspect of the book. Um, then taking a leap forward and looking at this, at least at all AlphaZero, AlphaGo, AlphaZero. Big moment that maybe John Von Neumann's thinking was brought to to reality. So I, I, I guess the question is, um, what do you think if you got to hang out with John Von Neumann?
Uh, now, what? What would he say well, about what's going on that would be today?
an amazing experience, you know? He's a f a fantastic mind. And and I also love the where he. He spent a lot of his time at Princeton, at the Institute of Advanced Study, is a very special place for thinking. And um, it's amazing how much of a polymath he was. In the the spread of things he helped invent, including, of course, the Von Neumann architecture that all the modern computers are based on. And um, he had amazing foresight.
I think he would have loved where we are today, and he would have, um, I think he would have really enjoyed AlphaGo. Being a, you know, game, he that's, yeah, was a big game theory. I think he foresaw a lot of what would happen with learning machine systems. That that that are kind of grown, I think he called it, rather than programmed. I'm not sure how, even maybe he wouldn't even be that surprised. There's the fruition of what I think he already foresaw in the 1950s.
I wonder what advice he would give. He got to see the the building of the atomic bomb, with the Manhattan project. I'm sure there's interesting stuff that maybe is not talked about enough, maybe some bureaucratic aspect, maybe the influence of politicians, maybe maybe not enough of picking up the phone and talking to people that are called enemies by the said politicians. There might be some, like, deep wisdom that we just may have lost from that time, actually.
yeah, I'm sure, I'm sure there is, I mean, I've tra we we, you know, study. I read a lot of books for that time as well, chronicle time, um, and some brilliant people involved. But I, I agree with you, I think maybe there needs to be more dialogue and understanding. Um, I hope we can learn from those those times. I think the difference here is that the AI has so many, it's a multi use technology. Obviously, we're trying to do things like that, like solve, you know, all diseases, um, uh, help with energy, uh, and scarcity, these incredible things.
This is why all of us and and myself, you know, I work started on this journey 30 plus years ago, and um, but of course, there are risks too. And probably von Neumann, my guess is he foresaw both. And um, and I think he sort of said, I think it's to his wife, that, that, that it would be a. This is. Computers would be even more impactful in the world. And as we just discussed, you know, I think that's right.
I think it's gonna be ten times, at least, of the industrial revolution. So I think he's right. So I think he would have been, I imagine, fascinated by, uh, uh, uh, where we are now.
And I think one of the maybe you can correct me. But one of the takeaways from the book is that reason, as, uh, said in the book, Mad Dreams of Reason. It's not enough for guiding humanity as we build these super powerful technology, that there's something else. I mean, there there's also like a religious component. Whatever God, whatever religion gives you, it pulls at something in the human spirit that raw, cold reason doesn't give us.
and I, I agree with that. I think we need to approach it with whatever you wanna call it, the a spiritual dimension or humanist dimension. Doesn't have to be to do with religion, right? But this idea of of a soul, what makes us human, this spark that we have, perhaps it's to do with consciousness. When we finally understand that, um, I think that has to be at the heart of the endeavor. Um, and technology, I've always seen technology as the enabler, right?
The tools that that enable us to to flourish and to understand more about the the world. And I, I'm sort of with Feynman on this. And he used to always talk about science and art being companions, right? You can understand it from both sides. The beauty of a flower, how beautiful it is, and also understand why the colors of the flower evolved like that, right? That just makes it more beautiful, that, that, that just the intrinsic beauty of the flower.
And and I've always sort of seen it like that. And maybe, you know, in the Renaissance times, the great discoverers, then, like people like Da Vinci, you know, they were. I don't think he saw any difference between science and art, uh, and perhaps religion, right? They were. Everything was. It's just part of being human and, um, being inspired about the world around us. And that's what I the philosophy I try to take. And, uh, one of my favorite philosophers is Spinoza, and I think he combined that all very well.
You know, this idea of trying to understand the universe and understanding our place in it, and that was his kind of way of understanding religion. And I think that's quite beautiful. And for me, it re all of these things are related, interrelated, the technology and, um, what it means to be human. And, uh, I think it's very important, though, that we remember that as when we're immersed in the technology and the the research. I think a lot of researchers that I see in in our field are a little bit too narrow and only understand the technology.
And I think also that's why it's important for, um, this to be debated by society at large. And I'm very supportive of things like the S, the AI summits that will happen, and governments understanding it. And I think that's one good thing about the chatbot era and the product era of AI. Is that everyday person can actually feel and and interact with cutting edge AI and and and feel, feel it for themselves.
yeah, because they they force the technologists to have the human conversation. Yeah, for sure, that's the whole full aspect of it. Like you said, it's a dual use technology that we're forcefully integrating the entire of humanity into it by into the discussion about AI. Because ultimately, AI AGI will be used for things that states use technologies for, which is, uh, conflict and so on. And the more we, uh uh, integrate humans into this picture by having them chats with them, the more we will guide.
yeah. Be able to adapt, society will be able to adapt to these technologies, like we've always done in the past, with with, uh, the incredible technologies we've invented in the past.
Do you think there will be something like a Manhattan project, where, um, there will be an escalation of the power of this technology? And states, in their old way of thinking, will try to use it as weapons technologies, and there will be this kind of escalation?
I hope not. Um, I think that would be, uh, very dangerous to do, and I think also, um, you know, not the right use of the technology. I I hope we'll end up with more something more collaborative, if needed, like more like a, like a CERN project, you know, where, um, it's research focused. And the best minds in the world come together to carefully complete the final steps and make sure it's responsibly done before, you know, like, uh, deploying it to the world, we'll see.
I mean, it's difficult with the current geopolitical climate, I think, uh, to to see cooperation, but things can change. And um, I think at least on the scientific level, it's important for the researchers to to keep in touch and and and keep close to each other on, at least on those kinds of topics.
yeah, and I, I personally believe on the education side and, um, immigration side, it would be great if both directions, uh, people from the west immigrated to China and China back. I mean, there is some like family, human aspect of people just intermixing and thereby those ties grow strong. So you can't sort of divide against each other. This kind of old school way of thinking. And so, uh, multi, uh, multicultural, multidisciplinary research teams working on scientific questions.
That's like the hope. Don't don't let the the warm, the leaders that are warmongers, because they divide us. I think science is the ultimately really beautiful connector.
yeah, science has always been, uh, I think, quite a a very collaborative endeavor, and you know, scientists know that it's it's a. It's a collective endeavor as well, and we can all learn from each other. So perhaps it could be a vector to get a bit of cooperation.
What's your, uh, ridiculous question? What's your P doom? Probability? The human civilization destroys itself?
Well, look, I, I don't have a, it's, uh, you know, I don't have a P doom number. The reason I don't is because I think it's would imply a level of precision that is not there. So, like, I don't know how people are getting their P doom numbers. I think it's a kind of a a little bit of a ridiculous notion. Because, um, what I would say is it's definitely non zero, and it's probably non like negligible.
So that in itself is pretty sobering. And my my view is it's just hugely uncertain, right? What these technologies are gonna be able to do, how fast are they gonna take off, how controllable are they gonna be? Some things may turn out to be, and hopefully, like, way easier than we thought, right? Um, but it may be there's some really hard, um, uh uh problems that are harder than we guess today, and I think, uh, we don't know that for sure.
And so in un under those conditions of a lot of uncertainty, but huge stakes both ways, you know? On the one hand, we. We could solve all diseases, energy problems, the not, the the, the scarcity problem, and then travel to the stars and colonize the stars, and maximum human flourishing. On the other hand, is this sort of P doom scenarios. So, given the uncertainty around it and the importance of it, it's clear to me. The only rational, sensible approach is to proceed with cautious optimism.
So we want the outcome, we want the um, uh, the benefits, of course, uh, and uh, all of the the amazing things that AI can bring. And actually, I would be really worried for humanity if I, if given the o other challenges that we have, climate dise, you know, aging, uh, resources, all of that. If I didn't know something like AI was coming down the line, right? How would we solve all those other problems? I think it's hard.
Um, so I think we've, you know, it could be amazingly transformative for good, um, but on the other hand, you know, there are these risks that we know are there, but we can't quite quantify. So the the best thing to do is to use the scientific method to do more research, to try and, uh, more precisely define those risks and, of course, address them. Um, and I think that's what we're doing. I think there probably needs to be, uh, ten times more effort of that than there is now, as we're getting closer and closer to the to the to the AGI line.
What would be the source of worry for you? More? Would it be human caused or AI, AGI caused? Like humans abusing the technology versus AGI itself, through mechanism that you've spoken about, which is fascinating deception or this kind of stuff getting better and better and better secretly, and then
I think they're, they're. escapes. They operate over different timescales, and they're equally important to address. So there's just the the common garden variety of like, you know, bad actors using new technology, uh, in this case, general purpose technology, and repurposing it for harmful ends. And that's a huge, uh, risk. And I think that has a lot of complications. Because generally, you know, I'm a huge favor of open science and open source. And in fact, we did it with all our science projects, like AlphaFold and all of those things, uh, for the benefit of of of the scientific community.
Um, but how does one restrict bad actors access to these powerful systems, whether they're individuals or even rogue states? Uh, and but enable access at the same time to good actors to to maximally build on top of? It's a pretty tricky problem that there's I've not heard a clear solution to. So there's the bad actor use case problem, and then there's obviously, uh, as the systems become more agentic and and closer to AGI, um, uh, and more autonomous.
How do we ensure the guardrails and they stick to what we want them to do, uh, and under our control?
Yeah, I tend to, maybe in my mind, is limited. Worry more about the humans, so the bad actors. And there it could be, uh, in part, how do you not put destructive technology in the hands of bad actors? But in another part, from again, geopolitical technology perspective, how do you reduce the number of bad actors in the world? That's that's also an interesting human problem.
yeah, it's a hard problem. I mean, look, we, we, we can, um, maybe also use the technology itself to help. Um, uh, uh. Early warning on some of the bad actor use cases, right? Whether that's bio or nuclear, or whatever it is like. AI could be potentially helpful there, as long as the AI that you're using is itself reliable, right? So it's a sort of interlocking problem, and that's what makes it very tricky. And and again, it may require some agreement internationally, at least between China and the U and and the US.
Of of of some, uh, basic standards, right?
I, I have to ask you about the uh, the book The Maniac, there, there's this, the the hand of God moment. Lisa Doll's move 78. That perhaps the last time a human did a move of sort of pure human genius and beat AlphaGo, or, like, broke its brain. If sorry to anthropomorphize, but it's an interesting moment, cause I think in so many domains it will keep happening.
yeah, it's a special moment. And you know, it was great for Lisa Doll. And you know, I think it's in a way. They were sort of inspiring each other. We as a team were inspired by Lisa Doll's brilliance and nobleness. And then maybe he got inspired by, you know, what AlphaGo was doing to then conjure this incredible inspirational moment. It's all, you know, captured very well in the in the documentary about it. And um, I think that'll continue in many domains where there's this, at least for the for the again, for the foreseeable uh future of like, y, the hu.
Humans bringing in their ingenuity, um, and asking the right question, let's say, uh, and then utilizing these tools, uh, in a way that, um, then cracks a problem.
yeah, what? As the AI becomes smarter and smarter, one of the interesting questions we can ask ourselves is what makes humans special. It does feel, um, perhaps biased, that we humans are deeply special. I don't know if it's our intelligence. It could be something else, that that other thing that's outside the mad dreams of reason.
I think that's what I've always imagined, uh, when I was a kid and starting on this journey of like, um. I was, of course, fascinated by things like consciousness. Did did a neuroscience PhD. to look at how the brain works, especially imagination and memory. I focused on the hippocampus, and it's sort of gonna be interesting. I always thought the best way, of course, one can can philosophize about it and have thought experiments, and maybe even do actual experiments like you do in neuroscience on on real brains.
But in the end, I always imagined that building AI, a kind of intelligent artifact, and then comparing that to the human mind and seeing what the differences were, uh, would be the best way to uncover what's special about the human mind. If indeed there is anything special, and I suspect there probably is, but it's gonna be hard to def. You know, I think this journey we're on will help us, uh, understand that and define that. And you know, there may be a difference between carbon based substrates that we are and silicon ones when they process information.
You know, one of the best definitions I like of of of consciousness is it's the way information feels when we process it, right? Um, it could be, I mean, it doesn't. Ha. It's not a very helpful scientific explanation, but but I think it's kind of interesting, intui intuitive one. And um, and so, you know, on this, this, this journey, this scientific journey we're on, will, I think, um, help uncover that mystery.
yeah, what I cannot create, I do not understand. That's, uh, somebody you deeply admire, Richard Feynman, like you mentioned, you also reach, um, for the the Wigner's dreams of universality that he saw in constrained domains, but also broadly, generally in in mathematics and so on. So so many aspects on which you're pushing towards, not to start trouble at the end. But, uh, Roger Penrose, so, uh, you know, do you do? You think consciousness? there's this hard problem of consciousness, how information feels?
um, do you think consciousness? 1st of all, is a computation? And if it is, if it's information processing, like you said, everything is, is it something that could be modeled by a classical computer, or is it a quantum mechanical in nature?
well, look, uh. Penrose is amazing thinker, one of the greatest of the modern era, and he we've had a lot of discussions about this. Of course, we cordially disagree, which is, you know, I, I feel like, Um, I mean. He collaborated with a lot of good neuroscientists to see if he could find mechanisms for quantum mechanics behavior in the brain. And they th. To my knowledge, they haven't found anything, um, convincing yet. So my betting is there is is that, that, that it's mostly, you know, it is just classical computing that's going on in the brain, which suggests that all the phenomena, uh, are modelable or mimickable by a classical computer.
But we'll see, you know, there there may be this final mysterious things of the feeling of consciousness, the qualia, these kinds of things that philosophers debate, where it's unique to the substrate. We may even come towards understanding that when, if we do things like Neuralink and and or, uh, have neural interfaces to the AI systems, which I think we probably will eventually, um, maybe to keep up with the AI systems. Uh, we might actually be able to feel for ourselves what it's like to compute on silicon, right?
So, um, and maybe that will tell us, uh, so I think it's it's gonna be interesting. And I, I had a debate once with the late Daniel Dennett about why do we think each other are conscious? Okay, so it's for two reasons. One is you're exhibiting the same behavior that I am, so that's one thing. Behaviorally, you seem like a conscious being if I am. But the 2nd thing, which is often overlooked, is that we're running on the same substrate.
So if you're behaving in the same way and we're running on the same substrate, it's most parsimonious to assume you're feeling the same experience that I'm feeling. But with an AI, uh, that's on silicon, we won't be able to rely on the 2nd part, even if it exhibits the 1st part. That behavior looks like a behavior of a conscious being. It might even claim it is, um, but we w but. But we wouldn't know how it actually felt, um, and it probably couldn't know we, what we felt, at least in the 1st stages.
Maybe when we get to super intelligence and the technologies that builds, perhaps we'll, we'll be able to, um, bridge that,
no, I mean, that's a huge test. For radical empathy is to empathize with a different substrate. Yeah, I
we never had to confront that before. mean, yeah, to Yeah,
maybe. Maybe. Through brain computer Inter interfaces, be able to truly empathize what it feels like to be a computer, well, for to compute.
information to be computed, not on a carbon system.
I mean, that's d deeply ex. I mean, some people kind of think about that with plants, with other life forms, which are different, it could be it's some similar substrate, but the the sufficiently far enough on the, uh, evolutionary tree that it's requires a a radical empathy. But to do that with a computer,
I mean, no, we sort of. There are animal studies on this of like, of course, higher animals, like, you know, killer whales and dolphins and dogs and and monkeys, you know, they have some, and elephants, you know, they have some aspects. Certainly of consciousness, right? Even though they're not, might not be that that that smart on an IQ sense. So so we can already empathize with that, and maybe even some of our systems one day. Like, we built this thing called Dolphin Gemma, you know, which can?
Well, one. A version of our system was trained on dolphin and whale sounds. And maybe we'll be able to build a an interpreter or translator at some point. Should be pretty cool.
what gives you hope for the future of human civilization?
Well, what gives me hope is, I think our almost limitless ingenuity. 1st of all, I think the best of us and the best human minds are incredible. Um, and you know, I love l l, you know, meeting and watching, uh, any human that's the top of their game, whether that's sport or science or art. You know, it's, it's. It's just nothing more wonderful than that, seeing them in their element, in flow. Um, I think it's almost limitless, you know, our brains are general systems, intelligent systems, so I think it's almost limitless what we can potentially do with them.
And then the other thing is our extreme adaptability. I think it's gonna be okay in terms of there's gonna be a lot of change, but the but look where we are now without effectively our hunter gatherer brains. How is it? We can, you know, we can co cope with the modern world, right? F flying on planes, doing podcasts, you know, playing computer games in virtual simulations. I mean, it's already mind blowing, given that our mind was was developed for, you know, hunting buffaloes on the f on the tundra.
And and so w. I think this is just the next step. And and and it's actually kind of interesting to see how h h society's already adapted to this mind blowing AI technology. We have today already. It's sort of like, Oh, I talk to chatbots, totally fine.
And it's, uh, very possible that this very podcast activity, which I'm here for, will be completely replaced by AI. Well, I'm very replaceable and I'm re waiting not to the for it.
level that you can do it, Lex, I don't think, Uh, thank you. That's you know,
that's what we humans do to each other. We compliment all right. And, uh, I'm, uh, deeply grateful for us humans to have this c, uh, infinite capacity for curiosity, adaptability, like you said, and also compassion and ability to love all of those human things.
All the things that are deeply human.
Well, this is a huge honor. Demis, you're one of the truly special humans in the world. Uh, thank you so much for doing what you do and for talking today.
Well, thank you very much, Lex.
Thanks for listening to this conversation with Demis Hassabis. To support this podcast, please check out our sponsors in the description and consider subscribing to this channel. And now, let me answer some questions and try to articulate some things I've been thinking about. If you would like to submit questions, including in audio and video form, go to lexfridman. com ama. I got a lot of amazing questions, thoughts and requests from folks. I'll keep trying to pick some, uh, randomly, and comment on it at the end of every episode.
I got a note on May 21st this year that said, Hi, Lex, 20 years ago today, David Foster Wallace delivered his famous This is water speech at, Uh, Kenyon College. What do you think of this speech? Well, 1st, I think this is probably one of the greatest and most unique commencement speeches ever given. But of course, I have many favorites, including the one by Steve Jobs. And David Foster Wallace is one of my favorite writers and one of my favorite humans.
There's a tragic honesty to his work, and it always felt as if he was engaging in a a constant battle with his own mind. And the writing, his writing, were kind of his notes from the front lines of that battle. Now, onto the speech, let me quote some parts. There's, of course, the parable of the fish and the water that goes. There are these two young fish swimming along, and they happen to meet an older fish swimming the other way, who nods at them and says, Morning boys, how's the water?
And the two young fish swim on for a bit. And then eventually, one of them looks over at the other and goes, What the hell is water? In the speech, David Foster Wallace goes on to say. The point of the fish story is merely that the most obvious, important realities are often the ones that are hardest to see and talk about. Stated as an English sentence, of course, this is just the banal platitude, but the fact is that in the day to day trenches of adult existence, banal platitudes can have a life or death importance.
Or so I wish to suggest to you in this dry and lovely morning. I have several takeaways from this parable and the speech that follows. 1st, I think we must question everything, and in particular, the most basic assumptions about our reality, our life and the very nature of existence. And that this project is a deeply personal one. In some fundamental sense, nobody can really help you in this process of discovery. The call to action here, I think from, Uh, David Foster Wallace, as he puts it, is to quote.
To be just a little less arrogant, to have just a little more critical awareness about myself and my certainties. Because a huge percentage of the stuff that I tend to be automatically certain of is, it turns out, totally wrong and deluded. All right, back to me, Lex speaking. 2nd takeaway is that the central spiritual battles of our life are not fought on a uh mountaintop somewhere at a meditation retreat, but it is fought in the mundane moments of daily life.
3rd takeaway is that we too easily give away our time and attention to the multitude of distractions that the world feeds us. The insatiable black holes of attention. David Foster Wallace's call to action, in this case, is to be deeply aware of the beauty in each moment and to find meaning in the mundane. I often quote David Foster Wallace in his advice that the key to life is to be unbearable, and I think this is exactly right.
Every moment, every object, every experience, when looked at closely enough, contains within it infinite richness to explore. And since, uh, Demis Hassabis of this very podcast episode, and I are such fans of Richard Feynman, allow me to, uh, also quote Mr. Feynman on this topic as well. Quote I have a friend who's an artist and has sometimes taken a view which I don't agree with very well. He'll hold up a flower and say, Look how beautiful it is, and I'll agree.
Then he says, I as an artist can see how beautiful this is, but you as a scientist, take this all apart and it becomes a dull thing. And I think that's kind of nutty. 1st of all, the beauty that he sees is available to other people and to me too. I believe, although I may not be quite as refined aesthetically as he is, I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees.
I can imagine the cells in there, the complicated actions inside, which also have beauty. I mean, it's not just beauty at this dimension, at 1 cm, there's also beauty at the smaller dimensions, the inner structure, also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate. It is interesting, it means that the insects can see the color. It adds a question Does this aesthetic sense also exist in lower forms?
Why is it aesthetic? All kinds of interesting questions, which the science knowledge only adds to the excitement, the mystery and the awe of a flower. It only adds all right. Back to, Uh, David Foster Wallace's speech. He has a great story in there that I particularly enjoy. It goes There are these two guys sitting together in a bar in the remote Alaskan wilderness. One of the guys is religious, the other is an atheist, and the two are arguing about the existence of God.
With that special intensity that comes after about the 4th beer. And the atheist says, Look, it's not like I don't have actual reasons for not believing in God. It's not like I haven't ever experimented with the whole God and prayer thing. Just last month, I got caught away from the camp in that terrible blizzard, and I was totally lost and I couldn't see a thing, and it was 50 below. And so I tried it. I fell to my knees in the snow and cried out, Oh God, if there is a God, I'm lost in this blizzard and I'm gonna die if you don't help me.
And now, back in the bar, the religious guy looks at the atheist, all puzzled. Well, then you must believe now, he says. After all, there you are alive. The atheist just rolls his eyes. No man. All that happened was a couple of Eskimos happened to be wandering by and showed me the way back to the camp. All this, I think, teaches us that everything is a matter of perspective, and that wisdom may arrive if we have the humility to keep shifting and expanding our perspective on the world.
Thank you for allowing me to talk a bit about David Foster Wallace. He's one of my favorite writers and he's a beautiful soul. If I may, one more thing I wanted to briefly comment on. I found myself to be in this strange position of getting attacked online often from all sides, including being lied about, sometimes through selective misrepresentation, but often through downright lies. I don't know how else to put it. This all breaks my heart, frankly, but I've come to understand that it's the way of the internet and the cost of the path I've chosen.
There's been days when it's been rough on me mentally. It's not fun being lied about, especially when it's about things that are usually for a long time have been a source of happiness and joy for me. But again, that's life. I'll continue exploring the world of people and ideas with empathy and rigor, wearing my heart on my sleeve as much as I can. For me, that's the only way to live anyway. A common attack on me is about my time at MIT and Drexel, two great universities I love and have tremendous respect for.
Since a bunch of lies have accumulated online about me on these topics, to a sad and at times hilarious degree, I thought I would once more state the obvious facts about my bio. For the small number of you who may care, TLDR two things 1st, as I say often, including in a recent podcast episode that somehow was listened to by many millions of people. I proudly went to Drexel University for my bachelor's, master's and doctorate degrees.
2nd, I am a research scientist at MIT and have been there in a paid research position for the last ten years. Allow me to elaborate a bit more on these two things now, but please skip if this is not at all interesting. So, like I said, a common attack on me is that I have no real affiliation with MIT. The accusation, I guess, is that I'm falsely claiming an MIT affiliation because I taught a lecture there once.
Nope, that accusation against me is a complete lie. I have been at MIT for over ten years in a paid research position from 2015 to today. To be extra clear, I'm a research scientist at MIT, working in Lids, the Laboratory for Information and Decision Systems in the College of Computing. For now, since I'm still at MIT, you can, uh, see me in the directory and on the various lab pages. I have indeed given many lectures at MIT over the years, a small fraction of which I posted online.
Teaching, for me, always has been just for fun and not part of my research work. I personally think I suck at it, but I have always learned and grown from the experience. It's like Feynman spoke about. If you want to understand something deeply, it's good to try to teach it. But, like I said, my main focus has always been on research. I published many peer reviewed papers that you can see in my Google Scholar profile. For my 1st four years at MIT, I worked extremely intensively.
Most weeks were 80 to 100 h work weeks. After that, in 2019, I still kept my research scientist position, but I split my time, taking a leap to pursue projects in AI and robotics outside MIT, and to dedicate a lot of focus to the podcast. As I've said, I've been continuously surprised just how many hours preparing for an episode takes. There are many episodes of the podcast for which I have to read, write and think for hundred, 200 or more hours across multiple weeks and months.
Since 2020, I have not actively published research papers. Just like the podcast, I think it's something that's a serious full time effort. But not publishing and doing full time research has been eating at me because I love research and I love programming and building systems that test out interesting technical ideas, especially in the context of human AI or human robot interaction. I hope to change this in the coming months and years. What I've come to realize about myself is if I don't publish or if I don't launch systems that people use, I definitely feel like a piece of me is missing.
It legitimately is a source of happiness for me. Anyway, I'm proud of my time at MIT. I was and am constantly surrounded by people much smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to escape the world to focus on exploring fascinating questions at the cutting edge of science and engineering. This again makes me truly happy, and it does hit pretty hard on a psychological level when I'm getting attacked over this.
Perhaps I'm doing something wrong. If I am, I will try to do better. In all this discussion of academic work, I hope you know that I don't ever mean to say that I'm an expert at anything in the podcast and in my private life. I don't claim to be smart. In fact, I often call myself an idiot and mean it. I try to make fun of myself as much as possible, and in general, to celebrate others instead.
Now to talk about Drexel University, which I also love, am proud of, and am deeply grateful for my time there. As I said, I went to Drexel for my bachelor's, master's and doctorate degrees in computer science and electrical engineering. I've talked about Drexel many times, including, as I mentioned, at the end of a recent podcast, the Donald Trump episode, funny enough, that was listened to by many millions of people. Where I answered a question about graduate school and explained my own journey at Drexel and how grateful I am for it.
If it's at all interesting to you, please go listen to the end of that episode or watch the related clip. At Drexel, I met and worked with many brilliant researchers and mentors from whom I've learned a lot about engineering, science and life. There are many valuable things I gained from my time at Drexel. 1st, I took a large number of very difficult math and theoretical computer science courses. They taught me how to think deeply and rigorously, and also how to work hard and not give up, even if it feels like I'm too dumb to find a solution to a technical problem.
2nd, I programmed a lot during that time, mostly C, C. I programmed robots, optimization algorithms, computer vision systems, wireless network protocols, multimodal machine learning systems, and all kinds of simulations of physical systems. This is where I really develop a love for programming, including, yes, Emacs and the Kinesis keyboard. Uh, I also during that time read a lot, I played a lot of guitar, wrote a lot of crappy poetry, and, uh, trained a lot of, uh, in judo and jujitsu, which I cannot sing enough praises to.
Jujitsu humbled me on a daily basis throughout my 20s, and it still does to this very day whenever I get a chance to train. Anyway, I hope that the folks who occasionally get swept up in the chanting online crowds that want to tear down others don't lose themselves in it too much. In the end, I still think there's more good than bad in people, but we're all, each of us, a mixed bag. I know I am very much flawed.
I speak awkwardly, I sometimes say stupid shit. I can get irrationally emotional. I can be too much of a dick when I should be kind. I can lose myself in a biased rabbit hole before I wake up to the bigger, more accurate picture of reality. I'm human, and so are you, for better or for worse, and I do still believe we're in this whole beautiful mess together. I love you all.