Demis Hassabis on AI Breakthroughs and the Future of Medicine

And it's such a pleasure to see everybody here for the conversation with Dennis Hassabus. We're especially honored today to have President John Levven leading this fireside chat. One of the things that makes Stanford University so distinctive is that some of the most important ideas emerge not within a single school or discipline, but at the intersections of schools and units. That spirit of crossuniversity university collaboration is particularly important right now as advances in AI begin to reshape nearly every domain of society. And nowhere is this more consequential than in medicine. I feel that personally through the GSB's close

partnership with the Stanford School of Medicine, which is undertaking an extraordinary effort to reimagine cancer innovation and care by bringing together social scientists, scientists, clinicians, engineers, and innovators to transform the patient journey from prevention through survivorship. That vision is ambitious and it will require the full capabilities of a great university working together. Stanford's strength lies not just in excellence within fields but in our ability to connect fields. We bring AI researchers together with physicians, organizational leaders together with scientists and entrepreneurs together

with people deeply committed to human well-being. And that is one reason that today's conversation feels so important. Dennis Habibus is an artificial intelligence researcher, an entrepreneur, and a Nobel laureate whose work sits exactly at these intersections. He's the co-founder and CEO of Google DeepMind, one of the world's leading AI research companies. Um, this was founded as Deep Mind in 2010 and acquired by Google in 2014. And the company is now central to Google's AI efforts and has produced some of the defining breakthroughs in the field. Some of these breakthroughs include Alpha Go, the first program to defeat a world champion at the game of Go, and Alpha Fold, which solved the 50-year grand challenge of protein

structure prediction by accurately predicting the three-dimensional shapes of proteins. This was a breakthrough with enormous implications for disease understanding and drug discovery. For this work, Demis alongside John Jumper and David Baker was awarded the 2024 Nobel Prize in Chemistry. He's also a fellow of the Royal Society and the Royal Academy of Engineering. And in 2024, he was knited for services to artificial intelligence. He has also been named the Time 100 list of the world's most influential people multiple times, including in 2017 and 2025. But what makes this moment especially compelling at Stanford is that the conversation around AI here has never been only about capability.

It has also been about human flourishing. Several years ago, professors FE Lee and Jennifer Oer began teaching a Stanford course on AI for human flourishing built around a profound set of questions. What does it mean to be human? What does flourishing look like? And when does technology help advance those goals versus undermine them? One insight from this work has stayed really deeply with me and that this is that some forms of friction are actually loadbearing. The struggle to find the right word, the discomfort of difficult conversations, the challenge of learning something new are not inefficiencies to eliminate. Uh they are instead the very experiences through which growth, agency, resilience and meaning emerge.

And that too is why our discussion today matters so much. At Stanford, advances in AI are not abstract. They are already reshaping how we think about discovery, diagnosis, leadership, learning, and human potential itself. Advances in AI are also forcing us to grapple with larger questions about judgment, ethics, institutions, and what kinds of lives we ultimately want technology to help us build. So, thank you all for being here and please join me in welcoming President John Levan and Deis Hassabus to the stage. Dennis, it's great to have you here at Stanford. Fantastic to be here. Thanks everyone for coming.

Really appreciate you doing this. Um so we're going to I'm going to ask you some questions. We'll have some questions from uh from students and uh looking forward to hearing your thoughts. Um thought maybe we would you've been chronicled a lot recently a movie a book. So some many people have heard about your trajectory. It is quite remarkable. I chess prodigy uh video game developer scientist uh tech entrepreneur and leader Nobel laurate um that's just the first half of your career. So um I if you were going to try to draw a through line through all those different things that you've done what would it be? Well, um I think there's several through lines actually with what seems maybe somewhat unconnected subjects. Um

first of all, I've always really enjoyed working at the intersection of creativity and technology and very broadly construed. So, um, actually the games industry, the video games industry, which is the first my first very early part of my career, uh, in the '9s, was one of the most creative spaces in any industry that was using cutting edge technology with art and design to sort of create an entirely new entertainment medium. So, that was uh really an amazing time. In fact, some of the most fun times I've had in my career was early in the '9s. um the chess and the neuroscience I did all of those things I've tried to uh I had from this idea of working on AI and AGI

being the most important thing one could and most interesting one thing one could spend your career working on also from a very early age so as a teenager probably I read too much science fiction reading things like girdle bark these types of books and biographies of the of some of my scientific heroes you know Turing and Fineman and so on so all of these were serving to inspire me to try and understand the world around us uh in a really deep way. Uh and then building AI was my expression of that mission to try and build the ultimate tool for science. And um I've tried to because life's short, I've tried to reuse and repurpose every experience I've had in service of that bigger northstar mission that I've

had, you know, for more than 30 years. So um you know my chess training is the way that I think about business and organizing things and planning and how I think I've been able to break down very ambitious plans into smaller more manageable steps. Um that all comes from kind of chess thinking I would say uh and then using games first of all building games learning about um engineering projects at scale running companies startups um and then fusing this creativity with engineering actually it's what we do today with AI it's an engineering science so you're fusing creative work scientific work with very hardcore cutting edge engineering um so that all served together and then finally on games as everyone knows we used games the

early days of DeepMind as the perfect proving ground for testing out algorithmic ideas probably most famously with Alph Go which I think you know we've just had the 10-y year anniversary of and was really um looking back now maybe the start of the modern AI era when you went into AI professionally in 2010 or so you started Deep Mind you had this very ambitious vision you were going to solve intelligence and then solve everything else How's it going? Let me expand a little bit. What has gone according to plan and sort of what has been off plan? Well, the broad arcs of it have gone um I mean unbelievably well perhaps uh you know when we started Deep Mind in 2010 that's

can you imagine we used to go to try to go to VCs in the UK of which there weren't very many and uh with that as the business plan it was literally step one solve intelligence step two use it to solve everything else and people were quite confused um but we really meant it and actually um we go back to exactly uh using that mission statement because um so by solve intelligence we meant build AGI um ideally also understand the nature of intelligence on the way to building AGI and perhaps using AGI to help that help us understand our own brains uh and minds better. You know things like the nature of consciousness, what is creativity, dreaming, all of these deep mysteries of the mind. And one of the reasons I studied

neuroscience was try to learn from what we understood about the brain as inspiration for algorithmic ideas. Um and sort of so step one was to try and build AGI and then we always had in mind what sort of happened which is that of course it's a general purpose technology maybe the general purpose technology uh and uh if it was built in the right way so it was a learning system that was very general what would be the limit of what it could be applied to? it's not, you know, it could be applied to almost anything was the dream. Um, and I think, uh, that's what's borne out. I had specifically in mind for that step two, advancing science and medicine. So, that's what I meant by using it to solve

everything else. I meant the big questions uh, in science, all of them. Um, I wanted to, you know, I was fascinated by all of them. The nature of time, the nature of reality, maybe that's the most fundamental one. And, um, I loved physics when I was at school. that was my favorite subject and I think when you're interested in the big questions you end up doing physics probably but um the reason I decided that there were too many interesting big questions so how was one to you know try and tackle all of that in a lifetime and that meant in my view building new tools and to aid us the best scientists the best experts uh to make much faster progress in the fields uh that they were tackling and the big

questions and important questions that they were tackling uh and Then of course AI in itself is also um a fascinating artifact in itself scientific sort of object one could call worthy of study itself. It's almost a new field. So this is sort of to me it would felt like the most fascinating and most important thing to spend one's life on and I would have been doing it even if it hadn't worked out. I would have found some way to be doing this you know in academia or wherever I would this is what I would was always plann to spend my life working on and all those things I did earlier were different expressions gathering the experience and I suppose the knowledge to be able to attempt something like deep mind in 2010 when we

were sort of I felt we were ready to make um fast progress and of course the second part of that use it to solve everything else is now much broader than just science and medicine although that's where I've tried to personally do my work in as well as running the overall uh organization. Um but obviously it's going to be amazing for productivity um and many other things in the world outside of just science and medicine. As you've been building these different um models at Deep Mind you started with games and then you went into science. Have there been were there particular moments where that I mean you started with a lot of conviction but I'm curious if there were sort of particular moments where you sort of saw this is actually going to work

like the alpha go and playing move the yeah there were many moments where I thought it wasn't going to work put it that way. So what some of the ones I remember really well are um we started with games because they're self-contained. They obviously were designed to be by other humans to be challenging or fun to for other humans to play. Um they're often I love games they're often microcosms of a lot of real world um uh scenarios. You know if you think of go or poker or chess I often thought as you know an MB one of the thing courses I'd have in an MBA or business school course would be a games module to you know study uh those types of games. diplomacy. They all have really interesting aspects of the best

games of uh real life and you can obviously practice many times uh in a kind of safe scenario. That's what I think games are really useful for. And that applies to AI systems that are learning too that the you know they're neat environments, they're challenging um and they have clear objective functions which is also was very important for our early days of reinforcement learning. Almost no one had used reinforcement learning for any kind of scaled up problem. it was just being used, you know, it was very kind of it was obviously an academic discipline, but it was used mostly for toy problems like little grid worlds.

Um, it wasn't clear it could scale up to anything uh major. Um, and so we started with um, I would say the most uh, famous set of but most basic games that had become, you know, world popular, which were Atari games and um, from the ' 70s. And uh, we started with the simplest game of all which was Pong. you know, there just the two the bat and the ball, just two bats and a ball. And um there's an inbuilt AI system. It's not really an AI system, an inbuilt system that controls your opponent and uh uses all the information that the game has about where the ball is and so on to move the bat around. And what we wanted to do was could you play Pong just from the pixels on the screen. So

the raw data, the raw visual input and no other information, no privileged information about no access to the insides of the program about you know where the ball is or the speed of it and so on which obviously the program knows. But we didn't give the uh the DQN system as it was called our Atari system any of that information. It just got the 20,000 pixels on the screen. and 20,000 pixels. I mean, it seems uh you know, sort of trivial now, but back in 2010, that was in enormous amount of input data. No one had ever dealt with something that complex and then multiplied by all the frames that you were doing. And for

about it felt like six months, maybe it was only two months, but it we couldn't win a single point at POP, right? So, it was, you know, jerking the bat around. It was like, oh, is it ever going to even be able to like control the bat? And um and of course he had no notions of any of these things and it was just losing 21 nil to the inbuilt AI and I did think and we had a couple of different ways of trying to attack this and we had almost no money you know the runway you know the bare the couple of uh million dollars of funding that we had which wouldn't even cover an intern these days um was uh which is good for all of you students um was our entire funding and uh and you know we're paying we had

we making like no salary and uh we was running out the money and I was like oh well maybe it turns out maybe we are still 10 years too early maybe we're 20 years too early and then magically it got a point and it was like oh maybe it was just luck and then it started winning a lot of points and then it started winning the games and then it was like okay we have liftoff now so now um and those of you working machine learning will know this if you get a foothold you can usually hill climb your way out of that's been the history of um AI I would say right once you have something working there's usually a way of optimizing it more and that's what turned out with Atari and then so that was our first big

result and our first nature paper was the you know really the first deep reinforcement learning uh model certainly at scale combining deep learning to learn the domain and deal with the perceptual inputs and the complexity of the inputs and find the patterns in it and then reinforcement learning built on top of that to kind of make the decisions and do the planning and then of course that culminated in Alph Go which was the always our aim. Uh Dave Silva and I who was head of that project. We were undergrad friends as undergrads at Cambridge and we were just had been discussing it since our undergrad about um we you know we were there in the mid '9s. The Deep Blue Casparov match happened while we were at college. Of

course I was fascinated by it both from the chess and the AI point of view. Um, but I was more impressed with Kasparov's brain than I was with D flu because Kasparov, you know, with his incredible mind, still one of the biggest chess geniuses that there's been of all time. Um, he was able to basically compete on an equal footing with this supercomput brute force machine next to him. Um, but of course he could do all the other things with his mind, speak five languages, do his politics, drive cars, all the rest of the things humans can do. To me, that was, you know, incredible. That's much more impressive. So there was something missing from the deep blue system and obviously those techniques those expert system

techniques where you hand curate the heristics and then you use brute force search on top which is still how a lot of traditional chess programs work today that works for chess but it's never worked for go because go's too esoteric a game it hasn't got material every piece is worth the same it's all about patterns and intuition even the top go players that's how they play it so we real sort of thought okay if someone could actually get to world champion level at go. It's not just about really that was an aside getting to that level. It was more about the approach we would have taken would probably be a really interesting algorithmic approach and maybe and hopefully would generalize to other domains and that's what turned out

with Alph Go. So, and then it went beyond our wildest dreams really because not only did it win the match against Lisa Dole in 2016. Um, it also created new famously new strategies that had never been seen before, even though we've played Go. Go is the oldest game humanity has invented, 2,000 years old, 2,000 plus years old and been played professionally for hundreds of years. And um, we hadn't discovered those strategies. So, I was sort of that was sort of double whammy for me. I was waiting for that moment that uh AI was able to come up with something novel. Um and we you know it's not there's more levels of creativity than that but beyond that but at least it was a novel idea and then that was for me

was what I was waiting for to then start using AI for science. So the moment we got back from soul uh we started the alpha fold project. Now when you when so let's talk about the science a bit because then you went into the protein folding problem and again you picked a problem where there was data and where there was a clear objective function in terms of thinking about protein folding and it worked. You I mean you actually managed to solve this long-standing problem of predicting protein structure. You did something very interesting when you uh when you came up with Alfold which was um it was obviously a huge science Nobel worthy scientific breakthrough probably also of commercial value and you just

gave it away for free. I'm curious how did you come to that decision? Did you was it something did you think about other ways of going about it? Why give it away? Yeah. So we picked the protein folding problem. I had my kind of eye on that since also you know my undergrad days at Cambridge. That's when I first came across it. I had a few biologist friends who uh were obsessed with the protein folding problem and actually they ended up becoming structural biologists of course in their career and uh one specifically I remember he was you know every time we were in the pub playing table football or something he would be talking about obsessively how this was the most

important problem in biology and more importantly I think of it as a root note problem. Like if you could unlock that and find the structures of proteins that would unlock whole you know new avenues of research things like drug discovery obviously we're trying to ex push that but also fundamental biology and disease understanding. So this was um this was a problem worth really spending a lot of attention and time on because of the downstream effects that it would have. It also felt to me it was a fascinating problem. It felt to me like the ultimate puzzle, you know, 3D puzzle of how does uh this sort of, you know, amino acid sequence, you think of it as genetic sequence fold up into this 3D structure.

It's amazingly interesting, intricate thing. And the more I looked into proteins, the more incredible my respect and wonder is for biology like these unbelievable little bio nanom machines. You know, everything on life obviously depends on proteins. And as you start looking at their structure, you start understanding their function. So this was fascinating to me as a science question. Um and then yes there was the clear objective is sort of like minimizing the free energy in the system. Presumably this is how physics is doing. It's why the body you know these proteins fold in milliseconds in your body you know billions of times a second. Um so somehow physics has solved

this. So it and it can't be there must be some uh topology let's say that you could learn maybe with a deep learning system that would guide the search just like we've done with Alph Go to find a great move in go a great strategy out of the you know more possibilities than there are atoms in the universe in go and protein folds are even larger search space than that but there's some way to narrow that down in a sensible way you learn a kind of heristic using the deep learning models to then guide your search for that to become tractable and it felt like a really uh analogous problem uh in science to what we'd solved in go um sort of applying some of those same approaches those same theories to this domain. Um and then

the other thing was there was obviously 50 years worth of painstaking crystalallography structural biology work by many great labs and people and uh they after all of that effort um there was about 150,000 structures in the PDB the main database which isn't a lot actually obviously it's it's a huge amount of effort that's gone into that but there's 200 million proteins and 150,000 also for machine learning systems is a very small amount of data so most people thought it was at least 10 20 years away um before we would have enough uh data and the right types of algorithms to tackle that. But we felt that using every technique we knew in the end that we could make progress with that. Um and it turned out to be

the case. Uh and then um when we decided to well how would we make the maximum impact with this it was obvious to me that we should um fold all the proteins so that because not only was AlphaFold accurate, it was extremely fast. it could fold a protein in a matter of seconds and then collaborate with in the end European Bionformmatics Institute uh in Cambridge uh to which host many of the biggest databases biology databases scientists use uh and just host the entire 200 million protein structures on their database and just allow it to be as simple as a kind of Google search to just find your protein structure u along with the confidence intervals the machine learning system had about which

parts of the protein structure it was confident on which is very important for biologists to know. So we put that all together. Um and it was it was uh of course could have been very valuable. Um I don't know how many billions of dollars or whatever. I mean it depends how you calculate it. to do that experimentally would be incalculable cost, but it would have been hugely valuable to keep proprietary. But for us, it felt um like we would only be able to scratch the surface of the downstream impact that putting all those structures out in the world could have uh on our own because you know there's three million researchers around the world that use AlphaFold uh pretty much every day.

Almost every biologist medical researcher in the world there's no way one organization could have done that. So it was obviously the right thing to do. Uh we also had depended on public data to train the first versions of Alpha Fold. So it only felt right to give back to that community, the structural biology community um this amazing resource that was amplifying the resource they had psacularly built and um so it was just it wasn't even a question for me and you know um it was great that also the executives at Google also you know love science and totally got that. Um I don't think all companies would have made that decision so I give them a lot of cudos on that too. That was an easy discussion. Um and then we've tried to

ourselves push that downstream with isomorphic labs an alphabet spinout that is um building you can think uh several more type of alpha fold level breakthroughs putting them together into a way that will accelerate hopefully drug discovery you know take it down from years to months maybe even one day weeks just like we did with um protein structures which used to take you know years for a single one and then we could do it in seconds that's one of the really exciting areas of the future with AI. I want to turn for a minute to something you said earlier this week. Uh you were in the news this week because at a big Google event you said um uh that we're in the foothills of the singularity.

Um yes, it got quite a lot of pickup that line. It got a lot of pickup. I and I understand that maybe the maybe the Google press team might not have been so thrilled about it, but since you're out there saying that um what did you mean by that? Yeah. So the full thing I said to sort of close the conference with was um when we look back at this time I think that you know maybe I'm thinking sort of 10 years from now I think we will realize that we were standing in the foothills of the singularity. Now what I mean by that and the word reason I chose that word is that um so there's the technology which is AGI we've been calling AGI this next version of really general artificial intelligence I believe that we're only a

few years away from that maybe like 20 30 plus or minus a year which is astounding to think really um and then the era I think that will be such an enormous transformative technology it's going to effectively be a new human era and that's what I mean by the singularity is that and what many science fiction writers have written about that is that it's sort of the it's describing the era that we will be in um in and around when AGI the advent of AGI happens. So, and I think we can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way that agents are working and tool use, um, it started to become really useful for, you know, still early days of it, but

genuinely useful in people's workflows. Um, and we can sort of see what extra things need to be done and all of us, the leading labs are working on that. Um I think this is the beginnings of that but the foothills I still think there's a lot more work and it's just the beginnings but and it's not any one thing it's it's several different technologies several use cases that I see um several things that I thought were maybe a bit further out turned out to be now um that are coming together uh that make me feel that uh in aggregate and to the extent that I wanted to say that um because I think um society needs to hear that because we don't have long to prepare for what that means.

Um and uh it's going to be enormously profound I think uh and the future in my view still to be written but these next few years are going to be very critical as to which way that will go and how we collectively uh want that to look like. If you look at surveys of how people perceive AI in this country in particular it's very negative right now. Uh it's and uh maybe more negative here than even in other countries and there's probably lots of things driving that concerns about privacy or state control or the size of the tech companies or jobs. Um how do you how do you I mean you're running one of the leading labs and how do you think about that public concern about the technology? I think um the public's right to be concerned. I think that

there are things that and I'm concerned about several aspects of what the technology is. It's a dual purpose technology. It's something this profound. You know, I sometimes describe it quantify it as 10 times the this the impact the industrial revolution was 10 times faster. And so, you know, taking place over a decade instead of a century. So, that's like a 100x uh of the industrial revolution. And it's probably an underestimate to be honest, but that's probably enough for us to try and comprehend, right, and deal with. And so of course uh I think there are uh it's super exciting. There's going to be amazing things that are going to happen like we're trying to do that with

solving all disease. I think you know a lot of the other challenges facing society today from climate to energy to disease will be enh helped by AI. I'm sure of it. And in fact, I'd be much more worried about those challenges if I didn't think something like AI was coming down the line. Um, but it's going to cause a lot of change and disruptions and um and actually both on the technical side, economic and philosophical. And I think we've we've got to um uh think through very thoughtfully and bring together all parts of society to discuss this, not just the technologists. The technology and the safety of the technology is just one piece of this. um it needs

economists, social scientists, human and humanity experts to kind of chart out what is going to happen next. And I think one of the reasons it's negative here is that um specifically because it's different in other countries. For example, when I go to we came back from the summit in India, it's hugely popular with the youth of uh India because they see the opportunities that it's going to democratize for them having access to basically the same tools that you would have needed to go to Silicon Valley for. you know, the world, we're in an amazing moment in that everyone can access pretty much what's going on in the frontier labs, but only, you know, with a delay of just a few months, right?

That's unheard of really if you think about that. So, there's incredible things, but I think it's also partly the way some of my peers are articulating the that I don't think they're being very careful with their communication and understanding. they're being way too certain I would say with some of their pronouncements where I think actually there's just huge uncertainty and that is worrying in itself but it's also means that nothing is decided in my opinion I think there's it's it's kind of unknown and I think anyone who says that I think directionally I could tell you some things but I think a lot of it really depends on the actions taken uh in the next few years and also what

the youth of today the many students in the room today you're going to uh be forming you're the first generation to kind of grow up in AI native, should we say, like I did, computer native, and just like every generation, you'll master these technologies, become um super uh productive with them. And I actually think over the next 10 years at least, hard to predict beyond that, but you'll almost be superpowered with those if you use them in the right way. The amount of creativity and projects you can do and the amount an individual will be able to do, but maybe that will change the nature of jobs. there'll be more entrepreneurial small entrepreneurial things rather than big companies. I don't know. It's going to

change a lot. But um and I think part of that is for so society to come together, really take this exponential seriously. Um not just the technologists, economists and others. We were discussing this last night need to take this seriously right now and start charting out what does that look like? If we're in a post scarcity world, for example, how does everyone benefit from that? You know, a lot of it's about how does you know, it doesn't it's obviously not correct for just a few people or a few company or even a few companies or even a few nations to be benefiting from this technology. It needs to be broad.

It's going to affect all of humanity has to broadly acrue the benefits to everyone. But how's that going to be done? We and we really need we've been a lot of us have been talking about this for a while, but we really need answers now and concrete things and actions to be taken. And I plan to do my bit on that. I've been um thinking a lot about this over the years and planning and building sort of influence on this and I will do what I can. Obviously, we're only we're an important actor, but we're only one actor in this space and but I hope I know that the good news is I know that all of the sort of leading labs and the leaders of the of those labs, although they disagree on a lot of things, they do worry about uh what you know these sorts of um issues coming

down the line. But we need more forums to allow them to, you know, for us to come together to discuss these things uh more candidly. Uh and I think that's what probably the public is detecting is just sort of slightly skewed discussions about what's going to happen and um maybe there's some ulterior motives behind some of that messaging, you know, raising money, other things. But I think we need to get to, you know, use the scientific method, be really rigorous and thoughtful about uh this, you know, critical moment in history. And then maybe the final thing I would say is I would love to see I think it's incumbent on the industry and the field to show more unequivocally what the benefits are and not just talk about them but

demonstrate them. So in health, in medicine, in medicine, in science, um these things are all in my view are sort of unequivocal goods, right? Like alpha fold, but there aren't enough examples. They should be 20 alpha folds, right? And they should be, you know, we got to like stop talking sort of in the hypothetical about curing cancer and actually cure cancer, right? And so this these are the things that I think um are going to be needed to demonstrate to the public, you know, why uh are those of us who are excited about it, and many of us are in this room, why are we excited about this? why have we spent our whole life building towards this and also how

are we going to uh concretely mitigate the risks um while enabling all of the amazing things that we would like to see and I think society needs. I think a lot of great points there that if there were some tangible benefits that were realized because of AI breakthroughs say to human health or drug discovery that might change people's you know perception in some ways and I love the suggestion of sort of trying to think farther out about a world that might look very different in terms of productivity and so forth. It's hard actually to do that. Yes. R, you know, rarely in social science people can do can people get out of the current frame they're in and actually project way forward. I think of Keynes's great

article during the depression when he looks out the economic lives of our grandchildren. It's a rare case that you were saying last night you thought we need another Canes right now and that's a maybe someone in the maybe there's someone in the audience who will do that. Uh let me ask one of the things you've talked about for a lot of years is um is the need for the frontier labs to in a sense regulate themselves. That is to sometimes sort of not release certain kinds of technologies uh you know that might be threats to safety and so forth. Um right now it's pretty clear that the labs are just at a breakneck competition. They're investing everything. They're they're going all out. There's Do you still feel the labs ought to be

self-regulating? Do you think the government ought to step in and regulate AI in some way? How do you see the current dynamic relative to the way you've thought about talked about it in the past? Well, look f first of all to just to give some historical context to this. This is not what in terms of we talked earlier about how the technology's gone. And I think the technology has gone amazingly and maybe even on the better side of what I imagined 20 years ago. But the environment it's kind of been birthing in is not the ideal. Far from it. Right. I was very worried about 15 years ago, 10 years ago about this race dynamic happening as more and more people, more and more

companies, more and more ambitious, you know, uh tech leaders uh realized what I had known for 20 plus years of how important this technology was going to be. Um and we talked about some of this in the room with about the dangers of this kind of uh race dynamic and unfortunately we've ended up because of the way the technology's gone. So my what if I could have waved a magic wand what I would have done was um build AGI the general technology more in a research uh facility perhaps like a CERN maybe all the best minds helping critique each other's uh ideas and making sure we were rigorous with the scientific method and the testing of it and uh understanding each step that we took. Um but then we wouldn't have to

wait for that. Of course that means AGI would arrive uh later maybe 10 years later but we wouldn't need to wait for that to get the benefits societal benefits of it because at the same time we would break off bits of that and use it for specialized systems like more alpha vaults curing diseases that can be done right because those are alpha fold is a specialized hybrid system uses a lot of the ideas the general uh purpose systems use but it's specialized to protein folding so that was actually my and you can see that was my vision for it because that's what we were doing. Um but then chat bots changed that because uh effectively and that was probably the only surprise to me of the

last sort of 15 years on the science side is how effective transformers ended up being for language and the fact that you could separate language and sort of learn it just from the internet without having to act in the world either robotics or simulations you know it's kind of very interesting and that would be a whole another topic why that was and I have some theories on that you know language is more grounded than we than linguists probably thought there's some there's some grounding coming from the reinforcement learning feedback that the human testers are doing because obviously we're we're grounded in the real world. So when we say yes no to certain things, our grounding is then ending up in a very low

bandwidth way but still ending up modifying what the foundation model understands. So there was these sort of unexpected things I would say that happened and then that made it a commercial very important commercial technology that could be scaled with engineering and money and so on which is what you see today and that changed the dynamic and then has created what we see today which is probably the most ferocious competitive environment I would say there's ever been. I mean certainly in the tech industry tech era maybe ever maybe other historians here from the business school will tell me otherwise but it feels unbelievably intense being in the middle of it and it feels like that for all of the participants. Um and then on top

of that you layer the geopolitical complexities. So there's also this is a double race going on. there's the race between the companies and it's pretty life or death for them and then there's the you know USChina dynamic and others right the geopolitical and there's a race there so it's a double layered one very tricky now um I still have hope that the um there can be some cooperation and coordination between we certainly discussed this as lab leaders uh on the safety elements and the security elements everybody wants that all n you know nobody wants something catastrophic to go wrong. The problem is we're in a kind of prisoners dilemma where you know anyone who by definition if you take more time to release something or

or make something safer that's harder than just u putting it out there and letting it see what happens. So you so a defector has some advantage. Um and that's the this is the classic problem with the race to the bottom dynamic and we got to change that somehow and I think urgently and I think part of that is um some form of government involvement. Um the hard part there of course is that anything to do with regulation it's too slow. Like this is every week there's something new. If we were to regulate something two years ago it'd be just like it's like ancient history now. So it would almost be you know almost certainly the wrong thing. So whatever is design and I have some

ideas on this and I'll probably be talking about this later this year is like it needs to be dynamic which is doesn't go usually that word doesn't go with regulation right. So it's got to be light uh flee footed and able to informed by the you know latest developments uh so that it can adapt to where the actual risk is rather than some kind of perceived risk that turns out not to be the case or not the critical thing um you know many years before we just it's just not going to work for AI and even today that that we you know the leading scientists wouldn't necessarily agree on a short list in fact I know they definitely wouldn't agree on a short list of what checks and balance ers are needed. So, and that's because the science isn't

settled. It's we're just it's and that's partly the speed. Um but also the pace of the um progress um is running ahead of the understanding of it. That's just how it is. It's part of the race dynamic. Um but uh we need to kind of somehow uh rebalance that and I think uh some form of really almost uh smart regulation uh is required that is dynamic and can adapt with the times very quickly and probably informed by the lab the leading labs uh so because they're seeing what's actually at the coal phase. All right, that open I think that's a there's so much more to discuss there in terms of the prospects of how you set up a regulatory system for AI and do it in a way that didn't prevent some of the

breakthroughs positive breaks you're talking about to the geopolitics all that innovation right we want to solve the disease so exactly how do you enable the good use cases um you know and without and mitigate the bad right I'm I'm looking forward to when you bring out your plan for that this year I think that'll that'll be fantastic. And that'll give us a lot to talk about here on campus and everywhere. I want to give the We've got a couple of student questions. I want to give a chance for some student questions. Hi Deis. I'm Arinda, a second year at the business school. My question is how do you balance pushing the frontier of AI with ensuring that the health and scientific dividends um is like evenly distributed um in

places like Africa and like the global south where the need is like the greatest but the infrastructure for like deployment and um research is most limited. Yeah, we think about that a lot actually and that was that goes back to um one of some examples of that I can give is back to the alpha fold question where we you know folded all the all the proteins um we put that out on databases you could access from anywhere around the world so these three million researchers come from 190 countries just to be clear it's pretty much every country every researcher um and it's uh it means that they and what was great what we did actually in the early days of seeding some collaborations what you could do with alpha fold. We worked with uh the DNDI

drugs for neglected disease part of the WHO in Switzerland which work on diseases in the poor places of the world that have don't have good health as good health care systems and some of those diseases are neglected as you know because um the big farmer can't make money by you know uh in those markets. So then the diseases that affect primarily those areas of the regions of the world um don't get as much research uh resources behind them. So what we were able to do uh in collaboration with this institute and many actual universities on the ground uh is jump them straight to not needing to trying to figure out like malaria virus or the structures of you know Zika virus or something like that

which they would have had to do all the painstaking structural biology. they can just start that as a given and work straight away on the drugs. So um you know that allows to speed up massively the whole process. You know they can sort of take the structure and of interest and move forwards from there. Same with crop resilience in uh affected by climate change. Um we work with uh Jennifer Dner's Institute and many others on these things because um lots of plant proteins uh you know were not had we didn't know what the structures were of them because obviously most of the structural work has gone into human proteins. So if it's animals or plants there's a lot less uh data out there. So they were able so it's even more

differentially impactful in those types of uh those types of areas. And then the final thing I would say is like if we can and I think this is where the capitalist engine can actually work for good here is if we can make um the uh the drug discovery platform that we're working on at isomorphic as efficient as I'm talking about you know down from years to months. So instead of it costing billions of dollars it costs tens of millions of dollars maybe single millions of dollars. Then suddenly um what I'm hoping we'll be able to do with isomorphic is we you know we cure these terrible diseases that maybe affect the richer parts of the world that makes money and that fuels it fuels the engine but then we can do sort of philanthropically the

company could uh find cures to diseases where we don't need to make any return because it's fast enough and it's cheap enough that it can just be done uh and in a short amount of time. So I think that's sort of my dream for how I can make isomeorphic help the whole world. Hi Deise, thank you so much for taking the time to talk to us. My name is Miki. I'm a senior um in the door school of sustainability and you've described extensively how AGI um could be humanity's most transformative technologies and I'm just curious um the responsibility or how you think about the societal impacts alongside this intellectual pioneering um and productivity that AGI presents particularly when thinking about you know how is this going to redefine and

reshape ape people that challenges that we're trying to solve today but kind of the downstream effects that could bring forward. Thank you. Yeah, thanks for your question. I think about this all the time and uh have done from the beginning because um we were planning for success, right? So uh even though it seemed very improbable back uh 15 20 years ago and I think um this is why I like doing talks like this and meeting folks in these kinds of places is I it is a bit of a call to arms now like it's very urgent that we um really think about the second order consequences. Um and um I think

many of you in the room and many of you in the humanity subjects it's now's your time in my opinion because okay we got to get the technology right but then um if we do that then there's the economics question and if we get that right there's the philosophical questions about the human condition and um and I'm very excited about and I'm a big belie I'm very optimistic I should just very obviously I'm a cautious optimist is the way I would say it is I'm I'm very optimistic that we're going to get this right and I'm a big believer in human ingenuity uh especially when the pressure's on. I think humanity has always figured it out when the chips are down and they are now. But we do really need to start taking that ser I think the

technologists are taking it seriously but the other parts of society need to as well. Economists, you know, I'm always a little bit astounded when I talk to economists about what's happening and it's sort of they're pretty skeptical. Where's it coming in the GDP? And it's like look, it's 10 times the industrial revolution. C can we start planning for that now? Like you know and we'll be in a world where and we talked about this last night. I think we do need uh some giants of these fields like Kanes was uh for now like why would that hold in a post scarcity world? We're going to be in a world for the first time if we get the technology right where we're nonzero sum world for the first time in

humanity's existence. How can that not need a new type of economic system, right? It has to. And I don't think it's any of the ones we've tried because they were all done under the guise of um a zero sum more, you know, and a limited a scarce uh world. Um, and I'm talking about, you know, traveling to the stars and utilizing um, all the resources that are out in the solar system, not just the limited ones on Earth. And I think that really is going to happen if we get the technology right in the next 10, 20, 30 years. Um, and then after all of that, there's the even harder question of how do we want to evolve our society and what is virtuous, what is meaning, what is purpose. And I think that's going to

need lots of great philosophers. So um that would be my appeal to people in those fields is now in my view could not be more of an exciting time if you're working on those types of projects as long if as long as you understand what's and really viscerally sort of uh understand and lean into what's actually happening here. It's a good charge to university. Okay, one more student question. Hi Dennis, I'm Janai. I'm a second year MBA student. My question to you is what do you not want AI to touch in this lifetime and what do you hold secret from your perspective? Thanks.

Yeah, that's a great question. I um look AI is going to be in terms of the scientific world of things. Um it's a fully general technology, right? You can think of it as a chewing machine. That's the way I think about it was my favorite course at college. Um, and I think our minds are actually fully general. So we're kind of approximate machine. So as Turing showed, we anything that's computable, a Turing machine can compute. And most things we know about in the universe, non-quantum things are computable. So that's a pretty large set of general things that we can turn our minds to. Hence, we built modern

civilization, which is miraculous if we stop to think about it. And I don't think we wonder enough. we keep our sense don't keep our sense of wonder for long enough about that. Um but it also means these systems that we're building they're also going to be sort of tring powerful as well. Um one thing I would say is uh that is there are very big questions to come that I think it would be better if we took more time over. So one example that's pretty topical right now is consciousness. And we you know it's not a very well-posed problem from philosophy and neuroscience still although I think we all have intuitions as to what the important aspects of that. My feeling is the current systems are don't exhibit

any are not but others disagree. Um we as what I would recommend though in terms of like what area should AI not touch is that we build our first systems as tools intelligent tools. That's enough of a challenge already in my opinion because that's already AGI. And then using those tools, I think we should study neuroscience and other things like that and philosophy and actually come up with a more rigorous definition of things like consciousness. I think that is possible. Um, and then test things against that and then maybe as society decide if we want to cross the second Rubicon of trying to make entities that at least seem like uh conscious to us. So we may not want to

make that decision. I could right I think that intelligence and consciousness are dissociable. So I don't think you have to do that to have an intelligent system. I think it's a choice some of our and so you can probably feel that in the when you use some of the leading chat bots um there's differences in opinion that come through. Um and my view is it'd be better to take that as two steps. They're both enormous um for humanity rather than conflate the two. Dennis, we you've we've got an auditorium with many students in it. Uh if you were back in school, um how would you be thinking about what would you be studying? How would you be thinking

about what you know what should they what would be your advice on how they should be thinking about to study in their careers? Uh well, look, I would be really excited uh if I was back at college now. Um my recommendation would be um those of you doing science and STEM subjects and mathematics and computer science still do those things. I think you'll be able to take better advantage of these tools if you understand how they are put together and what they're capable of. I think that's going to be true for the next uh the next period, the next 10 years at least. And um I would also lean

in though to not wish it away. the genie is not going back in the bottle like lean into what these tools can do. I can tell you that the leading labs are so busy making the tools that we have not we probably only scratched the surface of not even probably of what they can actually do. Even today's tools I have this you know sometimes people call it capability overhang. There's so much potential these things can do if you figure out how to pair them with other things or compare it pair it with another domain you're expert in. Um, build it into your workflow in an interesting way. You have those tools.

They're the most powerful tools anyone's got. You have them in the palm of your hand. There's so much more you can do as an individual. I think it should unleash creativity. like those of you studying humanities or product or business maybe you didn't have coding uh skills before but you don't need you don't you can produce a lot of what's in your mind now uh using these tools right um and I but I think also the coders the people who are expert at that could do 100x more like the terms of the size of project you can do if you're expert at coding so I think it enables both the democratization and the people that are specialized in those areas so I think it's an amazing time but it's also So I

get it's also worrying because everything's going to change. So that's the only thing I can tell you for sure. Everything is going to change in the next 10 years, probably more than people assume. But that also and anytime there's enormous change like that, there's enormous opportunities. There has to be, right? And the world's sort of your oyster really. And I kind of envy some of you now because you're the first generation that will be AI native just like my generation was computer and internet native. And uh it's going to be in your hands in the end. uh the students in the room like how that future world gets built and um I think it's a very exciting time if you think about it in the right way from the right angle and with a lot of

imagination and creativity but I think that's always been uh true um and maybe it's more so now in changes of enor you know periods of enormous change like this that accentuates it we were saying last night that in a period of a lot of change where you don't quite know what the future holds, but you have to be able to be adaptable and have broad domain of knowledge. It's a going to be a golden era for liber liberal education. So, I think I mean the main thing is to just um make sure you double down your own agency. The future's still to be written. I would say that's so don't listen to anyone who says it's not. Yeah. Dennis, thank you for joining us. This is amazing.