# Demis Hassabis on AGI Timelines, AI Safety, and Scientific Revolution

**Podcast:** The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
**Published:** 2026-04-07

## Transcript

The returns are still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.
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.
Those labs that have capability to invent new algorithmic ideas are going to start having bigger advantage over the next few years.
The last set of ideas are sort of all the juices being wrung out of them.
I sometimes quantify like AGI, the coming of AGI is like 10 times the industrial revolution at 10 times the speed.
This is 20 VC with me, Harry Stebbings, and I'm so excited for the show today.
I walked to this interview and I described it to my mother like this.
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 be here with her.
Sit down with him and discuss what we did today.
This is a truly special one and one that I'll remember for a very long time.
Enjoy the episode.
And I so appreciate the time we had with a very special human being.
I'm thrilled to welcome Demis Hassabis at DeepMind.
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Demis, I'm so excited to be doing this.
Thank you so much.
Great to be here.
Now, there are many places that we could have stopped, but I was watching actually the documentary that you did, which was fantastic.
And I actually wanted to start on AGI.
Definitions are very varying.
You've been very thoughtful about what it means to you.
And so I wanted to start, can you explain to me how you think about it today?
So we get that as a kind of ground center.
Yeah, 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, which is the brain that's the brain that's the brain that's the brain that's the brain is the only existence proof we have that we know of in maybe in the universe 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 says different things.
And it's very difficult when you have, you know, very prominent figures saying it could be as early as 2026, 2027.
Yeah.
I mean, I think, look, I've got a probability distribution around the timings, but I would say there's a very good chance of it being within the next five years.
So that's not long at all.
Is that closer than you thought?
Has that changed over time?
Not really.
I mean, actually, when you, when you, it's funny, my co-founder Shane Legg, who's chief scientist here, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about when AGI would happen.
And bearing in mind in 2010, when we started, almost nobody was working in AI and everyone thought AI basically didn't work.
And with the greatest of respects, no one was reading the blog posts.
No.
And, but they're still there on the internet for people to check.
And we used to do this extrapolation of compute and algorithmic.
Yeah.
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 in the documentary that 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 your ideas and your systems as, as, you know, the scaling laws, as they're called, you know, keeping on building bigger and bigger architectures with more and more parameters.
And as you do that, you get more intelligence.
Yeah.
And you get more knowledge about the systems.
But the other thing you need a lot of compute for is for doing experiments.
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 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, of course, when the leading companies all started building these large language models, you're getting enormous jumps with each generation of new system.
You know, maybe they're almost like doubling in performance.
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 still for scaling the existing, you know, systems up further.
So we and the other frontier labs are getting a lot of great returns on that kind of compute expansion.
So I would say the returns are kind of 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?
I think actually, in most areas, we are ahead of where I thought we would be.
If you think about things like the video models, or even now with our newest systems like Genie, they're interactive world models, which I think is kind of incredible if you sort of step back and think about it.
I think if you'd show me that, I'd be like, oh, I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
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I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
I don't know.
I don't five, 10 years ago, I would have been pretty amazed.
So I think in most domains where we are ahead of where the field thought, there's still some big things missing, though, like continual learning.
These systems don't learn after you finish training them after you put them out into the into the world.
You know, they're not very good at learning further things.
And I think some critical capabilities.
Why is that?
I'm sorry to ask blunt and basic questions.
Why do we not have continuous learning?
Well, people haven't quite figured out yet.
And we're 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.
Of course, the brain does this very elegantly, right?
And 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 to incorporate new information along with 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'm tweeting.
I think you liked it, but I basically tweeted what I used and how it's changed over time.
And DeepMind now is my number one for research for new shows.
It wasn't that way before.
What has led to the acceleration?
And progression of DeepMind in a way that it wasn't maybe that 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 or plus, you know, 15 years, I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or Google Research or DeepMind.
So one of our groups, 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 make those breakthroughs in the future, if there are any missing ones.
And I think we've basically helped put together all the talent from around the company sort of pushing in one direction.
And then we talked earlier just about compute resources.
It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions around the company.
So I think a lot of it was assembling together all the ingredients we already had, and then kind of...
Pushing with relentless sort of focus and pace, acting almost like a startup really, to get back to the frontier and be ahead in many areas.
You say if anyone's going to do the breakthrough, it could and should be us.
Yes.
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 continual learning.
I think there's a lot of mileage in looking at different memory systems.
At the moment, we have these long context windows, which are kind of a bit brute force.
You just put everything in them.
I think there's...
There's a lot of interesting probably architectures to be invented there.
And then there's stuff like long-term planning, hierarchical planning.
These systems are not very good at planning at long time horizons, many years into the future, which we as...
With our minds, we can do.
There's quite a lot of problems I think that are still left to overcome.
Maybe one of the biggest is consistency.
So I sometimes call these systems jagged intelligences because they're really amazing at certain things when you pose the question in a certain way.
But if you're not able to 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 no longer configure, it completely falls over.
Sure, exactly.
A hundred percent.
That's a disaster.
Yeah.
Well, I mean, the general intelligence, if you think about how our minds work, it shouldn't have those kinds of holes in it.
We said about a plateauing of scaling.
Everyone talks about a commoditization of models in terms of capabilities.
Do you think we...
Do you see that or do you think we see one to two continuously accelerate ahead of the others?
Yeah, I feel like maybe, you know, the three or four leading labs now, which we're one, I think the gap is sort of starting to pull away because a lot of these tools also, of course, help you build the next generation.
So things like coding tools, math tools, and it's getting harder and harder, I would say, to kind of eke out the same gains from just the same ideas.
So I think those labs that have capability to, you know, invent new algorithmic ideas are going to start having bigger advantage over the next few years as the last set of ideas are sort of, you know, all the juices being wrung out of them.
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 big, supporters of open science and open models.
And we've done many, many things, obviously, from the original transformers to AlphaFold, you know, these are all things we've sort of given out into the world and to help 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.
But increasingly, you know, what you're going to see is the open source models are probably one step back from the absolute frontier.
You know, it usually takes about six months for the open source community to sort of reimplement and figure out what those ideas are.
But we are also pushing hard on a 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 academics, or the, 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 applications.
How do you think about a world post LLMs?
You have different people with different views, you have Yann LeCun's 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, 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, these kinds of approaches.
But my betting is, 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, you know, gains from the returns from the scaling laws.
I think that's a big question really is when you think about a future AGI system is, 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, 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.
If we just start, generally, what does that world look like to you?
Well, I think on the positive side and the things obviously I've, I 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, finding cures to diseases, I think we need that kind of technology.
And so I hoping in five years plus time, we'll be sort of entering a new golden era, golden age of scientific discovery.
And so my mother's got multiple cirrhosis.
So it's like something, it's the thing that I'm always most excited about.
Yeah.
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?
Yeah.
So 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, 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, checking it's not toxic, and all the different properties you need for drugs to be safe.
I think we'll have that whole drug design engine ready in, you know, the next five plus, five to 10 years.
Then you're right.
The next problem is the clinical trials still take many, many years, right?
But I think AI can help there in terms of simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their genomic makeup.
And so I think AI can help there too, but I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process, and then the government and the regulatory body see that, and they have enough data to sort of back test the predictions of those models.
And then maybe what we can do will be in the future, where maybe 10 further years, where we can really just trust the predictions 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 ladder quicker because you can rely on these models.
So I think we've got to do in two steps, solve the drug design problem first, and then look at the regulatory 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 was obviously the documentary.
And I think with Stephen Hawking, he 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 stakes that we have to deal with.
And 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 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 aging, GI, can they be kept on the guardrails that we want?
And I think the right kind of regulation could help here in terms of making sure there's at least sort of minimum standards from all of the leading providers, but it needs to ideally be a kind of international standards.
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.
Which I think would be an unwavering truth.
Yes, for sure.
I mean, it's sort of crazy the timing that we're in, right, with this most consequential maybe technology the world's ever seen, at the same time as a very fragmented sort of international system.
And 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 maybe minimum standards, some benchmarks that test for undesirable properties, for example, deception.
You know, nobody should be building systems that are capable of deception because then they could be getting around other safeguards.
And then I imagine if things go well, some kind of certification process that basically, it's almost like a kite mark of inequality that this model has certain safeguards and certain guarantees, and so therefore consumers and companies can safely sort of build on top of it.
And I think that is how it should go ideally, but it does have to be international because of course, these systems are cross border and they're cross territory.
Will Barron there.
Who is the king of the country?
Who is that ultimate verification system?
You obviously started with Theme Park.
Yes.
A long time ago.
Yes, brilliant.
Don't put the burgers down too close to the roller coaster.
But, you know, obviously as a media company, I go through any media platform and say, 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 are, I mean, ultimately it's got to be government, I think.
But, you know, the kinds of technical bodies that would 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 was set up under Prime Minister Sunak and I think is doing great work.
And then 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 that can actually evaluate and audit these kinds of systems against certain benchmarks and 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, sadly.
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 international body, maybe similar to the Atomic Agency, something like that, that perhaps the AI Safety Institutes 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?
Maybe there are other safeguards too, like, you know, it wouldn't be desirable to have AI systems 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 introduce a new vulnerability.
So there's quite a few sort of things like that, which I think most of the leading labs would agree probably not best to do.
And then these bodies would, you know, these institutions would test against those things.
And I think that would give the public confidence and, you know, academia could be involved as well, as well as civil society, that these systems, which are going to get incredibly, really powerful, have been independently checked and audited.
That's it.
Your magic wand's done now.
Yeah.
Shut up.
Maybe I used it on the wrong thing.
Time will tell.
Yes, exactly.
You said there about science being one of the most exciting areas in five years' time.
I have to ask you, because it's one of the biggest concerns, is the labour displacement problem.
I just had Mark Andreessen on the show, actually, and he said that I was a, he said I was a Marxist.
I know, which I thought was like, it's a bit harsh.
For bringing, yeah, Mark's wonderful.
So I'm not blaming him, but he was, it's completely rubbish.
I don't agree with it at all.
We've always overpassed, overcome it.
How do you think about the labour displacement problem when you look at how truly capable these systems are?
Yeah.
And what that does to labour markets?
Well, certainly, you know, in the past, with every new revolutionary technology, there's been a lot of jobs disruption.
So that's for sure.
And I think that's definitely happened.
So a lot of old jobs, you know, go away or not viable anymore.
But then actually the history of it is that a whole set of new jobs arrive that maybe one can't even imagine before and those are higher 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 the last sort of, you know, 10 massive breakthroughs like the internet, mobile and so on.
I do think this is going to be bigger than all of those previous breakthroughs, technological breakthroughs.
I mean, I sometimes quantify like AGI, the coming of AGI is like 10 times the industrial revolution at 10 times the speed.
So unfolding, over a decade instead of a century.
You know, I've been reading a lot about the industrial revolution.
There's a lot of great books about it that cause 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 think you wouldn't want it not to have happened.
But ideally this time around, we mitigate some of the downsides a bit better than we did during the industrial revolution.
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 I should be more wise.
Yeah.
And I'm told that, you know, we always overestimate what can be done in a year and underestimate what can be done in 10.
Is that the truth here or is it actually coming faster than we know?
No, I think that's still the truth.
I mean, maybe all the both timescales of short term and long term are nearer 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.
But on the other hand, interestingly, I still think it's still very underappreciated how revolutionary this is going to be in the sort of timescale of about 10 years.
We could call that long term.
So there's still that dichotomy even today with AI.
With the concern around labor markets, there's also a concern around income inequality and the concentration of wealth of 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, 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 investment way of doing it.
I think also there needs to be thought about if there is this massive productivity gain, but it's sort of narrow where that accrues, you know, how do we redistribute and how do we distribute that so that everyone benefits from these huge gains?
And I can see all sorts of ways that could be done, including like providing sort of infrastructure and other things with that additional productivity gain.
I mean, there could be unbelievable things happening in the five to 10 year timescale, including like a breakthrough in some kind of renewable free energy.
You know, maybe we solve fusion.
We're working on that, right?
With our partners at Commonwealth Fusion.
I think AI is going to usher in, 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 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 hard.
But how do we solve that unprecedented need for new energy?
Well, I think actually AI will in the medium to long run more than pay for itself, I think, in terms of energy costs.
So, you know, we work on all these projects of like optimising existing infrastructure, like optimising the grid.
I think we could probably get 30, 40% more efficiency, out of our national grids.
And then there's like modelling the climate and weather.
And we have all sorts of the best kind of weather modelling systems in the world.
So that helps us work out where the effects are really happening to mitigate that.
And then finally, the most exciting maybe is like these new breakthrough technologies like fusion, like new batteries, superconductors that I think 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, and then, that will, of course, help with things like the climate and environment, and eventually also help us get into space much more cheaply.
Because if you have a, you know, an incredible energy source like fusion, then you have effectively unlimited rocket fuel because you can just distill catalyzed seawater.
I'm not going to ask you to solve space, don't worry then.
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 to prod it at every turn.
Yeah.
To move to the UK.
To move to the US.
Why have you stayed?
Well, I should ask you that question too.
But I think I saw in London when we started DeepMind as a place that, and the UK in general, and Europe 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 10 universities in the world with Cambridge and Oxford, Imperial, UCL, these kind of universities.
So we're producing the envy of the world, really, these amazing graduates and PhD students.
We have incredible scientists here.
We've got rich heritage of that, all the way from, you know, Turing and Hawking and Darwin, Newton.
So, you know, we have this incredible history of scientific breakthroughs and having great thinkers.
So I felt we had all the ingredients and the talent and great engineers here, but it just hadn't been galvanized into an ambitious startup idea, deep tech startup idea.
But I felt it was possible and I felt that there was actually less competition here for that sort of talent and we could even draw in the best talent from the top European universities.
And that's what it was like in the early days of DeepMind.
So I think it was a huge structural advantage for us.
And then the final thing is maybe being a bit away from the valley.
There is some disadvantage in that you're not plugged into the network and the gossip and the latest trends and vibes and all of these things.
We're a little bit out of it here, but I think it's very conducive to thinking deeply about things, being more original about how you think.
And I think that's great for things like deep tech, where, you know, you don't want to be distracted by the latest fad, you know it's going to be a 20 emission, which is what we knew at the beginning of DeepMind.
So I think being a little bit away from that maelstrom is quite good.
I mean, Palmer Lockheed Angel 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 see the Americans always bash us for our lack of large companies.
I ping Daniel Ek and be like, come on, dude.
Yes, exactly.
But we don't have a trillion dollar company.
Not yet.
I mean, Daniel might 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 going to try and do that with Isomorphic, which is headquartered here and I think has the potential to be that.
That's one of the disadvantages of Europe is obviously we're a combination of smaller markets.
So that's one thing we have to kind of overcome.
Maybe this EU Inc thing could be a good innovation.
I'm pulling out the magic wand again.
Yes, you can change one.
But this time applied to European technology.
Yeah.
What would you do to implement a growth mindset, a 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 global player, then where are the billion dollar rounds going to come from where you can really take on those, you know, the existing incumbents?
I think that certainly was missing 10 years ago when I was doing fundraising for DeepMind.
And I think it's still kind of missing today.
Just that kind of level of ambition and the amount the capital markets can support.
I read about some of your early rounds raising in the Seb Malibu book.
Yes, it was quite hard work.
Pitching families, kids.
Yes, exactly.
OK, we're going to do a quick fire round.
So take me to meeting Elon for the first time.
How was that?
Oh, yeah, it was amazing.
It was at a founder's fund because we were both, SpaceX and DeepMind were part of a same portfolio, a kind of amazing portfolio that Peter Thiel had at a founder's fund.
I think we were both invited.
I think I was invited to my first portfolio kind of conference.
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 Elon was the, you know, big thing in that portfolio.
So he had the keynote.
But then we met afterwards.
I think it was in, Elon says it was like we were passing each other in the bathroom or something.
We said hi and we both hit off, you know, as sort of, you know, people that were almost too ambitious in their thinking perhaps and love sci-fi.
And I really wanted to visit his rocket factory.
So I was sort of trying to get an angular invite to SpaceX and in LA.
And I think I got there a couple, you know, he invited me at the end of that meeting.
And that was our second meeting in the space effects factory.
I love it.
Now your speaking slot's as big as his.
Yeah, sure.
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 cliche, 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 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?
So I think a lot of people are worrying about the economic questions around AGI.
I worry a lot about the philosophical questions around it.
Like when it comes, let's assume we get the technical right.
Let's assume we get the economics part of it right.
Both of those are hard.
Then there's a philosophical question of what is meaning?
What is purpose?
We'll find out won't be what consciousness is.
What does it mean to be human?
I think that's what's coming down the road.
And I think we need some great new philosophers to help us navigate that.
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?
I would like my legacy to sort of be remembered for like advancing science, building technologies that bring incredible benefits into the world, like curing terrible diseases.
Damus, thank you so much for putting up with my meandering conversation.
You've been fantastic.
I really appreciate it.
Thank you very much.
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