Transcription
I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain or Google Research or DeepMind. So one of our groups, the returns are kind of still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.
We have amazing guests on the show, but very few, honestly, will be considered in the same realm as Newton, Turing, Einstein. Our guest today is one of the greatest minds on the planet, and I consider myself incredibly lucky to have had the chance to sit down with him.
Those labs that have the capability to invent new algorithmic ideas are going to start having a bigger advantage over the next few years, as the last set of ideas, all the juices being wrung out of them.
This is a truly special one and one that I'll remember for a very long time.
I think we could probably get 30-40% more efficiency out of our national grids.
Enjoy the episode, and I so appreciate the time we had with a very special human being.
I sometimes quantify the coming of AGI as 10 times the industrial revolution, at 10 times the speed. Thrilled to welcome Demis Hassabis at DeepMind. Ready to go.
Demis, I'm so excited to be doing this. Thank you so much for joining me today.
Great to be here.
Now, there are many places that we could have started, but I was watching actually the documentary that you did, which was fantastic, and I actually wanted to start on AGI. Mhm.
Definitions are very varying. You've been very thoughtful about what it means to you.
And so I wanted to start, can you explain to me how you think about it today, so we get that as a kind of ground center?
Yeah. Uh, well, we've we've always defined, we've been very consistent how we define AGI as basically a system that exhibits all the cognitive capabilities the human mind has. And that's important because the brain is the only existence proof we have that we know of, maybe in the universe, uh, that general intelligence is possible. So that, for me, is the bar for what AGI should be.
It's the worst question. How close are we? Everyone, everyone says different things, and it's very difficult when you have, you know, very prominent figures saying it could be as early as, you know, 2026, 2027.
Yeah, I mean, I think, look, I've got a probability distribution around, um, the timings, but I, I would say there's a very good chance of it being within the next five years. So that's not long at all.
Is that closer than you thought? Has that changed over time?
Not really. I mean, actually, when you, when you, uh, it's funny, um, my co-founder Shane Legg, who's chief scientist here, um, uh, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about, uh, when AGI would happen. And bearing in mind, in 2010, when we started, almost nobody was working in AI, and everyone thought AI, no one was reading it, it was a dead end. No. And, but they're still there on the internet for people to check. And, uh, we used to do this extrapolation of compute and algorithmic, uh, progress. And basically, we predicted around 20 years it would take from when we started out, and I think we're pretty much on track.
What are the biggest bottlenecks when you look today? You know, in in the documentary, you said you just never have enough compute.
What are the biggest bottlenecks when you look at where we are today?
I think compute is the big one. Not just for the obvious reason of scaling up, uh, your ideas and your systems, as, as, you know, the scaling laws, as they're called, you know, keeping on building bigger and bigger, um, architectures with more and more parameters. Um, and as you do that, you get more intelligent systems, but the other thing you need a lot of compute for is for doing experiments. So, um, the computers, the cloud is our workbench, basically. So if you have a new idea, a new algorithmic idea, but you want to test it, you kind of got to test it at a reasonable scale, otherwise it won't hold when you actually put it into the main system. So, um, you need quite a lot of compute if you have a lot of researchers with lots of new ideas.
You mentioned the word scaling laws.
A lot of people suggest that we're hitting scaling laws and we're starting to see that plateauing effect.
Yeah.
Do you think that's true?
No, I don't think so. I think it's a bit more nuanced than that. So, um, of course, when, uh, the leading companies all started building these large language models, you're getting enormous jumps with each generation of new system. Um, you know, maybe they're almost like doubling in performance. Uh, at some point, that had to slow down. So it's not kind of continuing to be exponential, but that doesn't mean there isn't great returns, uh, still for scaling the existing, you know, systems up further. So, and we, and the other frontier labs are getting, uh, a lot of great returns on on that kind of compute expansion. Um, so I would say the returns are kind of, um, still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.
Where are we behind where you thought we would be?
Um, I think actually in most areas, we are ahead of where I thought we would be. If you think about things like, um, the video models, or, um, even now with our newest systems like Genie, they're interactive world models. Um, which I think is kind of incredible if you sort of step back and think about it. I think if you'd shown me that five, ten years ago, I would have been pretty amazed. Um, so I think in most domains, we're, we, we are ahead of where, um, the field thought. Um, there's still some big things missing though, like continual learning. These systems don't learn, uh, after you finish training them, after you put them out into the into the world. You know, they're not very good at learning further things. And I think some critical capabilities that, I'm sorry to ask blunt and basic questions. Why do we not have continuous learning today?
Um, well, people haven't quite figured out yet, and all the leading labs are working on this, like how to integrate new learning into the existing systems that, you know, you spent months training. Um, so of course, the brain does this very elegantly, right? And, um, probably through things like sleep, reinforcement learning. So, you know, you just kind of get consolidation, it's called in the brain, where, you know, your memories during the day are replayed, and then some of that information is elegantly incorporated into your existing knowledge base, and perhaps we, I thought for a while, maybe we need something like that, uh, to incorporate new information along with, uh, uh, the existing information base.
You mentioned video models, you mentioned kind of media and image. It seems that DeepMind has progressed very quickly and caught up, slash, overtaken other providers.
I think I've tweeted, I think you liked it, but I basically tweeted, um, what I used and how it's changed over time, and DeepMind now is my number one for research for new shows.
It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that it wasn't maybe there two to three years ago?
Yeah. Well, we made some organizational changes. So, I think we've always had the deepest and broadest research bench at Google and at DeepMind. I mean, if you look at the last decade, uh, or plus, you know, 15 years, but I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or, uh, Google Research or DeepMind. One of our groups, um, if you think of like AlphaGo and reinforcement learning, and of course, transformers, you know, these are all the key breakthroughs. So I would back us to sort of, um, make those breakthroughs in the future, um, if there are any missing ones. Um, and I think we've basically helped put together all the talent from around the company, sort of pushing in one direction, um, and then we talked earlier just about, you know, compute resources. It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions, uh, around the the company. So I think a lot of it was assembling together all the ingredients we already had and then kind of pushing with relentless sort of focus and and and pace, um, acting almost like a startup, really, to get back to the the frontier and and be ahead in in many areas.
You say if anyone's going to do the breakthrough, it could and should be us. When you think about that, is continuous learning the next breakthrough that you're most excited by?
I think there's quite a few things that are missing. There's there's continual learning. I think there's a lot of, uh, I think a lot of mileage in looking at different memory systems. Um, at the moment, we have these long context windows, which are kind of a bit brute force. You just put everything in them. Um, I think there's there's there's a lot of, uh, uh, interesting probably architectures to be invented there. Um, and then there's stuff like, uh, long-term planning, you know, hierarchical planning. These systems are not very good at planning at long time horizons, you know, many years into the future, uh, which we, as you know, with our minds, we can do. So, um, there's quite a lot of, uh, uh, problems, I think, that's still left to overcome. Maybe one of the biggest is consistency. So, you know, the, I sometimes call these systems jagged intelligences because they're really amazing at certain things, uh, when you pose the question in a certain way, but in, if you pose a question in a slightly different way, they can actually still fail at quite elementary things. So a general intelligence shouldn't be that sort of jagged.
When you reposition files and you set up agents to perform in certain ways, and then the files fall over, configure it, completely falls over.
Exactly.
100%.
That's a disaster.
Yeah. Well, I mean, the general intelligence, you know, if you think about how our minds work, it shouldn't have those kinds of holes in it.
We said about a plateauing of scaling, was everyone talks about a commoditization of models in terms of capabilities. Do you think we see that, or do you think we see one to two continuously accelerate ahead of the others?
Yeah, I feel like, uh, maybe, you know, the, the, the three or four leading labs now, of which we're one, I think the gap is sort of, um, starting to pull away because, uh, a lot of these tools also, of course, help you build the next generation. So things like coding tools, math tools, and it's getting harder and harder, I would say, to kind of eke out the same, uh, gains from just the same ideas. So I think those labs that have the capability to, you know, invent new algorithmic ideas are going to start having a bigger advantage over the next few years, as, as the the last set of ideas are sort of, um, you know, all the juices being wrung out of them.
I mean, you know, you were very open with a lot of your research for years, and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that kind of use frontier models and then they use that to set a benchmark and then they use open models to kind of get as close as possible but with more cost effectiveness.
What does that future look like?
Yeah, I think it's probably similar to what we're seeing today. I mean, we're, we're big supporters of of open science and and open models, and we've done many, many things, obviously, from from the original Transformers to to AlphaFold, you know, these are all, uh, things we sort of given out into the world and to help the the research community, and we plan to continue to do that, especially in applied domains, you know, scientific domains, applying AI to science, which is obviously my passion. Um, but, uh, I, I think increasingly, um, you know, what you're going to see is the open source models probably one step back from the absolute frontier. Um, you know, it usually takes about six months for the open source community to sort of reimplement and figure out what those ideas are. Um, but we are also, uh, pushing hard on a kind of suite of open source models called Gemma, which are, you know, we're determined to kind of make best-in-class for their sizes. So specifically for small developers or, um, academics, or, or, you know, the beginnings of a startup, I think they're perfect for that, and also edge computing too. So we're very interested in open source models for certain types of, um, applications.
How do you think about a world post-LLMs? You have different people with different views. You have Yann LeCun with very different views.
For me, I don't think it's, you know, I kind of disagree with Yann on a few things in terms of, um, I think there might be, there's a 50/50 chance there's some things maybe missing that we still need to make breakthroughs in, perhaps their world models, um, uh, these kinds of, uh, approaches. But my betting is, uh, pretty strongly is we've seen how successful these foundation models have been. They can do incredibly impressive things. I don't think that's going to go away. We're still seeing, seeing, you know, gains from the returns from the scaling laws. Um, so my, I think the only question really is when you think about a future AGI system, is, you know, is an LLM foundation model going to be the key component only, or is it the total system, right? So I just think it's, it's a question of, um, uh, you know, is there anything else needed, not, is it not? I don't think it's going to get replaced. I think it's going to get built on top of these foundation models, just like the way we do with our world models.
When we think about that future five years out, as you said, potentially with AGI, what does that world look like? Many people have different concerns.
Yeah.
If we just start generally, what does that world look like to you?
I think on the positive side, and the things obviously I've, I've spent my whole career in life building towards AGI, is I think it will be the ultimate tool for science and medicine. So in terms of advancing scientific discovery, um, finding cures to diseases, I think we need that kind of technology. And so I'm hoping, um, in five years plus time, we'll be sort of entering a new golden era, golden age of scientific discovery.
Uh, so my mother's got multiple sclerosis. So it's like something, it's the thing that I'm always most excited about. The thing I worry about is actually kind of drug discovery, the process of getting it through all the trials and knowing that it takes a decade before my mother will actually get any benefits from it.
How do we solve that?
I think we'll get to that point soon. First of all, what we're doing is, you know, after we did the AlphaFold project to do protein folding, um, then we spun out a company called Isomorphic Labs, which is doing extremely well. And that is supposed to, you know, the idea there is we're focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, uh, checking it's not toxic, and all the different properties you need for for drugs to be safe. Um, I think we'll have that whole drug design engine ready in, you know, the next five plus five to ten years. Then you're right, the next problem is the clinical trials still take, uh, many, many years, right? Um, and, but I think AI can help there in terms of, um, maybe simulating, uh, parts of the human, uh, metabolism. Um, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their, uh, genomic makeup. Um, and so I think AI can help there too. But I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process. Uh, and then the government and the regulatory bodies see that, and they have enough data to sort of, uh, backtest the predictions of those models, and then maybe what we can do will be in the future, where maybe ten further years, where, um, we can really just trust the predictions, uh, that the models are making, and actually then maybe skip out some steps, perhaps like the animal testing is not needed anymore. Maybe we can go up the dosage, uh, uh, ladder quicker, um, because you can rely on these models. So I think we got to do in two steps. Solve the drug design problem first, and then look at the regulatory, uh, length of time it takes.
Speaking of regulatory, AI safety is a big topic and a big concern. I think it was, again, I watched it last night over dinner, which was a great watch, which is obviously the documentary, and I think it was Stephen Hawking who said, "We must get it right, because we might not get another chance." Do you think that's right?
Yeah, I do think that's right. I think that is the the the stakes, uh, that, uh, you know, we have to deal with. And, um, you know, there's two things I worry about. One is the misuse of these systems by bad actors, and they can be repurposed. These are dual-purpose technologies. They can be used for incredible good in science and health, as we've just discussed, but they can also be repurposed for harmful ends by a bad actor. So that's one issue. Second issue is a technical one. Making sure these systems, as they get more powerful, not today's systems, but maybe in a year or two's time, when they become more agentic, more autonomous, as we get towards AGI, um, can they be kept on the guardrails that we want? Um, and I think regulation, the right kind of regulation could help here in terms of making sure there's at least sort of minimum standards from all of the, uh, uh, leading providers, but it needs to ideally be a kind of international, uh, standards.
What is the right kind of regulation? And again, I'm kind of quoting yourself back from this documentary. You're like, "I think we need more global coordination," which worries me because we're getting worse at it.
Yes.
Which I think would be an unwavering truth.
Yes, for sure. I mean, that's it's sort of crazy the timing that we're in, right, with this most consequential, maybe technology the world's ever seen, um, at the same time as a very fragmented sort of international, uh, uh, system, and, uh, it's not ideal, but I think we're going to have to try and do the best we can to at least come up with a sort of set of min, maybe minimum standards, some benchmarks that test for undesirable properties. For example, deception. You don't, you know, nobody wants should be building systems that are capable of deception because then, um, they could be getting around other safeguards. U and then I imagine, you know, if things go well, some kind of certification process that basically it's almost like a kite mark of, you know, quality that this model, um, has certain, uh, uh, safeguards and certain guarantees, and, uh, and so therefore, um, consumers and companies can safely sort of build on top of it. And I think that is how it should go, ideally. Um, but it does have to be international because of course, these systems are cross-border and, you know, they're, they're cross-territory.
Who is that like ultimate verification system? I, you know, you obviously started with theme park.
Yes.
Uh, yeah, brilliant. Don't put the burgers down too close to the roller coaster. Um, but, you know, obviously as a media company, I go through any media platform saying, I don't know what's real or fake. I'm always having to ask, what's real or fake? Who is that arbiter of verification?
Yeah. Well, I think there, I mean, ultimately, it's got to be government, I think. But, um, you know, the kind of technical bodies that would, um, be able to do the technical work would be like, maybe the AI safety institutes. You know, there's a very good one in the UK that, uh, uh, you know, was set up under Prime Minister Sunak, and I think is doing great work, and there's one in the US, and maybe some of the leading countries that have the best research should also have an equivalent body that is staffed with high-quality researchers too, um, that can actually evaluate and audit these kinds of systems, uh, against certain benchmarks, and, um, I kind of like independently check whether they are meeting the right standards.
If I could give you like a magic wand that was only applicable to AI safety, uh, what would be your implementation idea program that you would put in place with this magic wand?
Yeah, I think we need some kind of, um, uh, international body, maybe similar to the atomic agency, something like that, that perhaps the, the AI safety institutes, uh, sort of feed into, and the research community has to also do this and be involved in, like, what are the right set of benchmarks to check, what types of traits, what types of capabilities, uh, maybe there are other safeguards too, like, um, you know, it's, it wouldn't be desirable to have, uh, AI systems, um, output tokens that are not human readable. So, you know, in some kind of machine language that we couldn't understand. I think that would, in, you know, introduce a new vulnerability. So there's quite a few sort of things like that, which I think most of the leading labs, uh, would agree are probably not best to do. Um, and then these, uh, these bodies would, uh, you know, these institutions would test against those things, and I think that would give the public confidence, and, um, and, you know, academia could be involved as well, as well as civil society, that these, uh, systems which are going to get incredibly powerful, um, have been independently, uh, checked and audited.
That's it. Your magic wand's done now. That was the one.
Maybe I used it on the wrong thing, but.
Time will tell.
Yes. Exactly. You said there about, um, science being one of the most exciting areas in five years' time.
I have to ask it because it's one of the biggest concerns is the labor displacement problem. I just had Mark Andreessen on the show, actually, and he said that I was a, he said I was a Marxist for, I know, which I was like, for bringing. Yeah, Mark's wonderful, so I'm not blaming him, but he was like, it's completely rubbish.
Yeah.
I don't agree with it at all. We've always overpass, overcome it. M.
How do you think about the labor displacement problem when you look at how truly capable these systems are, and what that does to labor markets?
Well, certainly, you know, in the past, uh, with every new revolutionary technology, there's been a lot of, uh, jobs, uh, disruption. So that's for sure, and I think that's definitely going to happen. So a lot of old jobs, you know, go away or not viable anymore, but then actually, uh, the history of it is that, um, a whole set of new jobs arrive that maybe one can't even imagine before, and those are high quality, higher paying. So that's the normal course. Of course, you have to be very careful to say this time is different, and I guess that's what people like Mark are claiming is like, you know, it's the same as, as, as the last sort of, you know, ten massive breakthroughs like the internet, mobile, and so on. Um, I do think this is going to be bigger, uh, than all of those previous, uh, uh, breakthroughs, uh, technological breakthroughs. I mean, I sometimes quantify like AGI, the coming of AGI, as like ten times the industrial revolution, uh, at ten times the speed. So unfolding over a decade instead of a century. So if you, you know, I've been reading a lot about the industrial revolution, there's a lot of great books about it, and, um, that caused a huge amount of upheaval, as well as a lot of advances. I mean, we wouldn't have modern medicine today. Child mortality was at 40% back in, back pre-industrial revolution. So things, things you wouldn't want it not to have happened, but ideally, this time around, we, uh, mitigate some of the downsides a bit better than we did during the industrial revolution. I often listen to amazing voices like yours, and I get very excited by how fast it's coming. Yeah. And then I try and stop myself from being too useful and think, ah, I should be more wise, and I'm told that, you know, we always overestimate what can be done in a year and underestimate what can be done in ten.
Is that the truth here, or is it actually coming faster than we?
No, I, I think that's still the truth. I mean, maybe all the, both time scales of short-term and long-term are nearer than than than other technologies. But I do think like literally today, as of today, and in the next year, things are a bit overhyped in AI. I mean, there couldn't be any more hyped in some ways. Uh, but on the other hand, interestingly, I still think it's still very underappreciated how revolutionary this is going to be in the, in the sort of time scale of about ten years. So we could call that long term. So there's still that dichotomy, even, even today with AI.
With the concern around labor markets, there's also a concern around income inequality and the concentration of wealth to few players.
How do you see that shaping out with the comment on industrial revolution and what happens there?
Well, I think there's different ways that could play out. So, um, you know, maybe pension funds should be buying into all the big AI companies and making sure that everyone has a piece of that, or sovereign funds. Maybe everyone, every country should have a sovereign wealth fund that does that. That would be the sort of, um, uh, investment way of doing it. I think also there needs to be thinking thought about if there is, uh, this massive, uh, productivity gain, but it's sort of narrow where that occurs, you know, how do we redistribute and, and how do we distribute that, um, so that everyone benefits from, uh, uh, these huge gains? And I can see all sorts of ways that could be done, including like providing, sort of infrastructure and other things, um, with that additional productivity gain. I mean, there could be unbelievable things happening in the five to ten year time scale, including like a breakthrough in some kind of renewable free energy. You know, maybe we solve fusion. Uh, we're working on that, right, with with our partners at Commonwealth Fusion. Um, uh, I think AI is going to usher in, you know, maybe we have amazing new superconductors, better batteries, you know, material science. There's all sorts of ways I could see that completely changing the nature of the economy.
How, how do we solve the energy crisis that comes with an AI revolution? What it means in terms of energy requirements is unprecedented. I know it's an incredibly hard question, which I'm delving from really hard question to really, but how do we solve that unprecedented need for new energy?
Well, I think actually, um, AI will, in the, in the medium to long run, uh, more than pay for itself. I think in terms of energy costs. In so, you know, we work on all these projects of like optimizing existing infrastructure, like optimizing the grid. I think we could probably get 30-40% more efficiency out of our national grids. Um, and then there's like modeling the climate and weather, and we have all sorts of the best kind of weather modeling systems in, in, in the world. So that helps us work out where the effects are really happening to mitigate that. Uh, and then finally, the most exciting, maybe, is like these new breakthrough technologies like fusion, like new batteries, superconductors, that I think, uh, AI will be essential for helping us reach. And then I think we'll be in a completely new energy situation than we've ever been as humanity, where, uh, and then that will, of course, help with things like the climate and environment, um, and eventually also help us, um, get into space much more cheaply, because if you have a, you know, an incredible energy source like fusion, um, then you have effectively unlimited rocket fuel, because you can just, um, distill, catalyze seawater.
I'm not going to ask you to solve space. Don't worry then.
My, my question was on being in the UK.
Yeah.
You're in London. I'm in London. I'm very proud to be in the UK.
You have been, I'm sure, pushed or prodded at every turn to move to the US.
Why have you stayed?
Well, um, I should ask you that question, too. But I think, uh, I think I saw in London when we started DeepMind as a place that, and and the UK in general, and and Europe in some to some degree, there's incredible talent here. You know, we've always had, I don't know what it is, three or four of the top ten universities in the world with Cambridge and Oxford, Imperial, or UCL, these kind of universities. So we're producing, um, kind of the envy of the world, really, these amazing graduates and PhD students. Um, we have incredible scientists here. We've got a rich heritage of that, all the way from, you know, Turing and and Hawking and Darwin, Newton. So, you know, we have this incredible history of of of scientific breakthroughs and having great thinkers. So I felt we had all the ingredients, uh, and the talent and great engineers here, but it just hadn't been galvanized into, uh, an ambitious startup idea, deep tech startup idea. And, and that's what I, but I, I felt it was possible, and I felt that there was actually less competition here for that sort of talent, and we could even draw in the best talent from the top, uh, European universities, and that's what it was like in the early days of DeepMind. So I think it was a huge structural advantage for us. And then the final thing is maybe being a bit away from the valley. There is some disadvantage in that you're not plugged into the network and the gossip and the the latest trends and vibes and all these things. We're a little bit out of it here, but, um, it does, I think it's very conducive to to thinking deeply about things, being more original about how you think. And I think that's great for things like deep tech, where, you know, you don't want to be distracted by the latest fad. You want to, you know, it's going to be a 20-year mission, which is what we knew at the beginning of DeepMind. So I think being a little bit away from that, um, maelstrom is quite good. I mean, Palmer Luckey at Oculus often talks about being 400 miles away from the valley. It's core to his kind of innovative thinking.
Yes, we're a few thousand miles away, but, yeah.
Terrible question. Will Europe have a trillion-dollar company? You know, you see the Americans always bash us for our lack of large companies. I ping Daniel Ek and be like, "Come on, dude. That's exactly."
But we don't have a trillion-dollar company.
Not yet. I mean, Daniel may well get there with one of his companies. You know, Spotify, Helsing, I think those are two good options. I think there's no reason why we can't have that. I'm I'm going to try and do that with Isomorphic, U, which is headquartered here, uh, and I think has the potential to be that. But I think that's one of the disadvantages of Europe is obviously we're a combination of, you know, um, smaller markets. So that's one thing we have to kind of overcome. Maybe this EU Inc thing could be a good innovation.
I'm pulling out the magic wand again. You can change, you've got the magic, but this time applied to European technology.
What would you do to implement a growth mindset, an ability to build that trillion-dollar company that we don't have today?
I think in the UK, I mean, this may apply to other European countries too. I think unlocking what pension funds can invest in, or just for the kind of growth stage. I think we're brilliant at doing the startup idea and getting it to a certain level, like we did with DeepMind. But then if you really want to cross that sort of chasm into the trillion-dollar, uh, global, you know, player, then where are the billion-dollar rounds going to come from? Uh, where you can really take on those, the, you know, the existing incumbents. And I think that certainly was missing ten years ago when I was doing fundraising for DeepMind, and, um, I think it's still kind of missing today, just that kind of level of ambition and, and the amount the capital markets can can support.
I read about some of your early rounds raising in the S Malibu families' kids. Exactly.
Um, okay, we're going to do a quick fire round. Meeting Elon for the first time. How was that?
Oh, yeah, it was amazing. Um, it was at a, it was at a Founders Fund because we were both SpaceX and DeepMind were part of a same portfolio, a kind of amazing portfolio that Peter Thiel had at Founders Fund, and, uh, I think we were both invited. I think I was invited to my first portfolio kind of conference, and I think it must be back in 2011 or 2012, very early days. So we were the small little upcoming thing, and I had a small speaking slot, and then, and then, and then Elon was the, you know, big thing in that portfolio. So he had the keynote, but then we met afterwards. I think it was, Elon says it was like we were passing each other in the bathroom or something, and, uh, we said hi, and we both hit it off, you know, immediately, like, uh, as sort of, you know, people that were, uh, almost too ambitious in their thinking, perhaps, and loved sci-fi, and, um, and, and I really wanted to visit his rocket factory. So I was sort of trying to get an invite to to SpaceX and, uh, in LA, and I think I got there a couple, you know, he invited me at the end of that meeting, and, and that was our second meeting in, in the SpaceX factory.
I love it. Not even speaking slots as big as his.
I don't know about that.
Healthcare revolution, disease eradication that you're most excited about. Again, for me, it's specifically with multiple sclerosis.
Yeah. Well, look, I want to literally cure cancer. I know people say that's the cliché, but I actually, what we're building at Isomorphic is general purpose. So we're trying to build a platform, a drug design platform that will be applicable to any therapeutic area. So ideally, it will help with everything from neurodegeneration, cardiovascular, immunology, cancer. Those are the ones we're we're focusing first, but eventually, it should be applicable to every disease area.
What are you thinking about that you're not reading about or seeing anyone talk about?
Um, I think it's more so, I think a lot of people are worrying about the economic questions around AGI, uh, that we talked about earlier, but I, I worry a lot about the philosophical questions around it. Like, when it comes, let's say, assume we get the technical right, let's assume we get the economical economics part of it right, both of those are hard, then there's the philosophical question of what is meaning, what is purpose. Um, we'll find out, won't be what consciousness is. Um, what does it mean to be human? I think that's, uh, uh, what's coming down the road, and I think we need some great new philosophers to help us to help us, uh, navigate that.
Hard final question.
There are many different ways you could describe what you do. What would you most like to be remembered for, your legacy to be?
Um, I would like, uh, my legacy to sort of be remembered for like advancing science, um, and doing, uh, building technologies that bring incredible benefits into the world, like curing terrible diseases. Demis, thank you so much for putting up with my meandering conversation. You've been fantastic. I really appreciate it.
Thank you very much.