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Demis Hassabis

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2025-07-23
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2025-07-23
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  1. I mean, it's already mind blowing given that our mind was developed for hunting buffaloes on the tundra. And so I think this is just a next step. And it's actually kind of interesting to see how society has already adapted to this mind-blowing AI technology we have today already. It's sort of like, oh, I. Talk to chatbots. Totally fine.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  2. Well, what gives me hope is that I think our almost limitless ingenuity, first of all, I think the best of us and the best human minds are incredible. And I love meeting and watching any human that's the top of their game, whether that's sport or science or art. It's just nothing more wonderful than that, seeing them in their element in flow. I think it's almost limitless. You know, our brains are general systems, intelligent systems. So I think it's almost limitless what we can potentially do with them. And then the other thing is our extreme adaptability. I think it's going to be okay in terms of a lot of change, but look where we are now without effectively how to gatherer brains. How is it we can cope with the modern world, right? Flying on planes, doing podcasts, playing computer games. Virtual simulations.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  3. I mean, Lou, we sort of, there are animal studies on this of like, of course, higher animals, killer whales and dolphins and dogs and monkeys. They have some and elephants. They have some aspects certainly of consciousness, right? Even though they might not be that smart on an IQ sense. So we can already empathize with that. And maybe even some of our systems one day, like we built this thing called dolphin gemma, which can won a version of our system was trained on dolphin and whale sounds. And maybe we'll be able to build an interpreter or translator at some point. Should be pretty cool.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  4. And it probably couldn't know what we felt, at least in the first stages. Maybe when we get to superintelligence and the technologies that builds, perhaps we'll be able to bridge that.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  5. Eventually, maybe to keep up with the AI systems, we might actually be able to feel for ourselves what it's like to compute on silicon, right? And maybe that will tell us. So I think it's going to be interesting. I had a debate once with the late Daniel Dennett about why do we think each other are conscious. Okay, so it's for two reasons. One is you're exhibiting the same behavior that I am. So that's one thing. Behaviorally you seem like a conscious being if I am. But the second thing, which is often overlooked is that we're running on the same substrate. So if you're behaving in the same way and we're running on the same substrate, it's most parsimonious to assume you're feeling the same experience that I'm feeling. But with an AI that's on silicon, we won't be able to rely on the second part, even if it exhibits the first part that behavior looks like a behavior of a conscious being. It might even claim it is. But we wouldn't know how it actually felt.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  6. Well, look, our Penrose is an amazing thinker, one of the greatest of the modern era, and he's had a lot of discussions about this. Of course, we cordially disagree, which is, you know, I feel like, I mean, he collaborated with a lot of good neuroscientists to see if he could find mechanisms for quantum mechanics behavior in the brain. And to my knowledge, they haven't found anything convincing yet. So my betting is there's mostly it is just classical computing that's going on in the brain, which suggests that all the phenomena are modelable or mimicable by classical computer. But we'll see. There may be this final mysterious things of the feeling of consciousness, the qualia, these kinds of things that philosophers debate where it's unique to the substrate. We may even come towards understanding that when if we do things like neural link and have neural interfaces to the AI systems, which I think we probably will.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  7. One of the best definitions I like of consciousness is it's the way information feels when we process it, right? It could be. I mean, it's not a very helpful scientific explanation, but I think it's kind of interesting, intuitive one. And so, you know, on this journey, this scientific journey we're on, I think, help uncover that mystery.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  8. I think that's what I've always imagined when I was a kid and starting on this journey of course fascinated by things like consciousness did a neuroscience PhD to look at how the brain works especially imagination and memory I focused on the hippocampus and it's sort of going to be interesting I always thought the best way of course one can philosophize about it and have thought experiments and maybe even do actual experiments like you do in neuroscience on on real brains but in the end i always imagine that building ai a kind of intelligent artifact and then comparing that to the human mind and seeing what the differences were would be the best way to uncover what's special about the human mind if indeed there is anything special and i suspect there probably is but it's going to be hard to you know i think this journey we're on will help us uh understand that and define that and you know there may be a difference between carbon based substrates that we are and silicon ones when they process information

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  9. It's a special moment, and it was great for Lisa Dole. And, you know, I think it's in a way there was sort of inspiring each other. We as a team were inspired by Lisa Dole's brilliance and nobleness. And then maybe he got inspired by what AlphaGo was doing to then Kunja this incredible inspirational moment, which were, you know, captured very well in the documentary about it. And I think that'll continue in many domains where there's this, at least for the, again, for the foreseeable future of like the humans bringing in their ingenuity and asking the right question, let's say, and then utilizing these tools in a way that then cracks a problem.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  10. Yeah, it's a hard problem. I mean, look, we can maybe also use the technology itself to help early warning on some of the bad actor use cases, right? Whether that's bio or nuclear or whatever it is, like AI could be potentially helpful there as long as the AI that you're using is itself reliable, right? So it's a sort of interlocking problem. And that's what makes it very tricky. And again, it may require some agreement internationally, at least between China and the US, of some basic standards.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  11. I think they operate over different timescales and they're equally important to address. So there's just the common gardenal variety of like bad actors using new technology, in this case general purpose technology and repurposing it for harmful ends. And that's a huge risk. And I think that has a lot of complications because generally, I mean, huge favor of open science and open source. And in fact, we did it with all our science projects like Alphafold and all of those things for the benefit of the scientific community. But how does one restrict bad actors access to these powerful systems, whether they're individuals or even rogue states?

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  12. But on the other hand, there are these risks that we know are there, but we can't quite quantify. So the best thing to do is to use the scientific method to do more research to try and more precisely define those risks and, of course, address them. And I think that's what we're doing. I think there probably needs to be 10 times more effort of that than there is now as we're getting closer and closer to the AGI line.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  13. On the one hand, we could solve all diseases, energy problems, the scarcity problem, and then travel to the stars and conscious of the stars and maximum human flourishing. On the other hand, is this sort of p-doom scenarios? So given the uncertainty around it and the importance of it, it's clear to me the only rational sensible approach is to proceed with cautious optimism. So we want the benefits, of course, and all of the amazing things that AI can bring. And actually, I would be really worried for humanity that we have, climate, aging, resources, all of that, if I didn't know something like AI was coming down the line, right? How would we solve all those other problems? I think it's hard. So I think it could be amazingly transformative for good.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  14. Well, look, I don't have a, you know, I don't have a P doom number. The reason I don't is because I think it would imply a level of position that is not there. So I don't know how people are getting their P-Doom numbers. I think it's a little bit of a ridiculous notion because what I would say is it's definitely non-zero and it's probably non-negligible. So that in itself is pretty sobering. And my view is it's just hugely uncertain, right? What these technology is going to be able to do, how fast are they going to take off, how controllable they're going to be. Some things may turn out to be, and hopefully, like way easier than we thought, right? But it may be there's some really hard problems that are harder than we guess today. And I think we don't know that for sure. And so under those conditions of a lot of uncertainty but huge stakes both ways.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  15. Science has always been, I think, quite a very collaborative endeavor. And scientists know that it's a collective endeavor as well. And we can all learn from each other. So perhaps it could be a vector to get a bit of cooperation.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  16. I hope not. I think that would be very dangerous to do. And I think also not the right use of the technology. I hope we'll end up with more something more collaborative if needed, like more like a CERN project where it's research focused and the best minds in the world come together to carefully complete the final steps and make sure it's responsibly done before deploying it to the world. We'll see. I mean, it's difficult with the current geopolitical climate, I think, to see cooperation, but things can change. And I think at least on the scientific level, it's important for the researchers to keep in touch and keep close to each other, at least on those kinds of topics.

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  17. Yeah, be able to adapt society, be able to adapt to these technologies. Like we've always done in the past with the incredible technologies we've invented in the past

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  18. And I agree with that. I think we need to approach it with whatever you want to call it a spiritual dimension or humanist dimension. It doesn't have to be to do with religion, right? But this idea of a soul, what makes us human, this spark that we have perhaps is to do with consciousness when we finally understand that. I think that has to be at the heart of the endeavor And technology. I've always seen technology as the enabler, right? The tools that enables us to flourish and to understand more about the world. And I'm sort of with Feynman on this. And he used to always talk about science and art.

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  19. Be 10 times at least of the Industrial Revolution. So I think he's right. So I think he would have been, I imagine, fascinated by where we are now.

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  20. Yeah, I'm sure there is. I mean, I read a lot of books for that time as well, Chronicle Time and some brilliant people involved. But I agree with you, I think maybe there needs to be more dialogue and understanding. I hope we can learn from those times. I think the difference here is that the AI has so many, it's a multi-use technology. Obviously, we're trying to do things like that, like solve all diseases, help with energy and scarcity. These incredible things. This is why all of us and myself, you know, I work, started on this journey 30 plus years ago. And, but of course, there are risks too. And probably von Neumann, my guess is he foresaw both. And I think he sort of said, I think to his wife, that it would be, this computers would be even more impactful in the world. And as we just discussed, I think that's right. I think it's going to be.

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  21. About what's going on? Well, that would be an amazing experience. He's a fantastic mind. And I also love where he spent a lot of his time at Princeton, at the Institute of Advanced Studies. It's a very special place for thinking. And it's amazing how much of a polymath he was and the spread of things he helped invent, including, of course, the von Neumann architecture that all the modern computers are based on. And he had amazing foresight. I think he would have loved where we are today and he would have, I think he would have really enjoyed AlphaGo being a game that he also did game theory. I think he foresaw a lot of what would happen with learning machines, systems that kind of grown. I think he called it rather than programmed. I'm not sure how even maybe he wouldn't even be that surprised, that's the fruition of what I think he already foresaw in the 1950s.

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  22. Definitely. And I think we'll need new governance structures, institutions probably to help with this transition. So I think political philosophy and political science is going to be key to that. But I think the number one thing, first of all, that is to create more abundance of resources, right? Then there's, so that's the number one thing, increased productivity, get more resources, maybe eventually get out of the zero-sum situation. Then the second question is how to use those resources and distribute those resources. But yeah, you can't do that without having that abundance first.

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  23. I think that's right. Anytime where there's a lot of disruption and change, and we've had this, it's not just this time, we've had this many times in human history with the internet, mobile, but before that was the industrial revolution, and it's going to be one of those eras where there will be a lot of change. I think there'll be new jobs we can't even imagine today, just like the internet created. And then those people with the right skill sets to ride that wave will become incredibly valuable, right? those skills. But maybe people will have to relearn or adapt a bit their current skills. And it's the thing that's going to be harder to deal with this time around is that I think what we're going to see is something like probably 10 times the impact the industrial revolution had, but 10 times faster as well, right? So instead of 100 years,

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  24. Exploit them to the maximum. And so I think that's what we're going to see in the next domain. So that's going to cause quite a lot of change, right? And so that's coming. A lot of people benefit from that. So I think One example of that is if coding becomes easier, it becomes available to many more create if do more. But I think the top programmers will still have huge advantages in terms of specifying going back to specifying what the architecture should be, the question should be, how to guide these coding assistants in a way that's useful and check whether the code they produce is good. So I think there's plenty of headroom there for the foreseeable next few years.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  25. Well, it's interesting that programming, and it's again counterintuitive to what we thought years ago, maybe that some of the skills that we think of as harder skills are turned out maybe to be the easier ones for various reasons, but coding and math because you can create a lot of synthetic data and verify if that data is correct. So because of that nature of that, it's easier to make things like synthetic data to train from. It's also an area, of course, we're all interested in because as programmers, right, to help us and get faster at it and more productive. So I think for the next era, like the next five, ten years, I think what we're going to find is people who are kind of embrace these technologies become almost at one with them, whether that's in the creative industries or the technical industries will become sort of superhumanly productive, I think. So the great programs will be even better, but there'll be even 10x even what they are today. And because there you'll be able to use their skills to utilize the tools to the maximum

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  26. To mean. So I think in the economy, and we're going to have much bigger issues to work through, and how does the economy function in that world and companies? So I think it's a little bit of a side issue about salaries and things like that today.

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  27. But as and I was expecting this because more and more people are finally realizing leaders of companies, what I've always known for 30 plus years now, which is that AGI is the most important technology probably that's ever going to be invented. So in some senses, it's rational to be doing that. But I also think there's a much bigger question. I mean, people in AI these days are very well paid. You know, I remember when we were starting out back in 2010, I didn't even pay myself a couple of years because it wasn't enough money. We couldn't raise any money. And these days, interns are being paid the amount that we raised as our first entire seed round. So it's pretty funny. And I remember the days where we used, I used to have to work for free and almost pay my own way to do an internship, right? Now it's all the other way around. But that's just how it is. It's the new world. But I think that we've been discussing what happens post-AGI and energy systems are solved and so on. What is even money going?

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  28. Yeah, well, look, of course, you know, there's a strategy that Meta is taking right now. I think that from my perspective, at least, I think the people that are real believers in the mission of AGI and what it can do and understand the real consequences, both good and bad from that and what that responsibility entails, I think they're mostly doing it to be like myself, to be on the frontier of that research. So they can help influence the way that goes and steward that technology safely into the world. Meta right now are not at the frontier. Maybe they'll manage to get back on there. And it's probably rational what they're doing from their perspective because they're behind and they need to do something. But I think there's more important things than just money. Of course, one has to pay people their market rates and all of these things. And that continues to go up.

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  29. I agree, and I would love to see a lot of people, a lot of the other labs talk about science, but I think we're really the only ones using it for science and doing that. And that's why projects like Alpha Fold are so important to me. And I think to our mission is to show how AI can be clearly used in a very concrete way for the benefit of humanity. And also we spun out companies like Isomorphic off the back of Alphafold to do drug discovery. It's going really well and build sort of, you know, you can think of build additional alpha fold type type systems to go into chemistry space to help accelerate drug design. And the examples I think we need to show and society needs to understand are where AI can bring these huge benefits.

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  30. Yeah, that would be awesome. And we've talked about that in the past, and it may be a cool thing that we can do. And I agree with you, it'd be nice to have kind of side projects in a way where one can just lean into the collaboration aspect of it. And it's a sort of win-win for both sides. And it kind of builds up that collaborative muscle.

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  31. Been okay so far. I tried to pride myself in being collaborative. I'm a collaborative person. Research is a collaborative endeavor. Science is a collaborative endeavor, right? It's all good for humanity. In the end, if you cure incredible, you know, terrible diseases and you come with an incredible cure. This is net win for humanity. And the same with energy, all of the things that I'm interested in in helping solve with AI. So I just want that technology to exist in the world and be used for the right things. And the kind of the benefits of that, the productivity benefits of that being shared for the benefit of everyone. So I try to maintain good relations with all the leading lab people. They're very interesting characters, many of them, as you might expect. But yeah, I'm on good terms. I hope with pretty much all of them. And I think that's going to be important when things get even more serious than they are now, that there are those communication channels.

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  32. Well, I don't see it as sort of winning. I mean, I think we need to think winning is the wrong way to look at it given how important and consequential what it is we're building. So funnily enough, I don't, I try not to view it like a game or competition, even though that's a lot of my mindset. It's about, in my view, all of us, those of us at the leading edge have a responsibility to steward this unbelievable technology that could be used for incredible good, but also has risks, steward it safely into the world for the benefit of humanity. That's always what I've dreamed about and what we've always tried to do. And I hope that's what eventually the community, maybe the international community, will rally around when it becomes obvious that as we get closer and closer to AGI, that's what's needed.

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  33. Yeah. And then other things that even Moisoteria come into play like, you know, the style of the persona, of the system, how it verbose, is it succinct? Is it humorous? And different people like different things. So, you know, it's very interesting. It's almost like cutting-edge part of psychology research or personality research. I used to do that in my PhD, like five-factor personality. What do we actually want our assistance to be like? And different people will lie different things as well. So these are all just sort of new problems in product space that I don't think have ever really been tackled before, but we're going to sort of rapidly have to deal with now.

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  34. But then does it make worse your language system or your translation systems and other things that you care about? So you've got to kind of continually monitor this increasingly larger and larger suite of benchmarks. And also when you stick them into products, these models, you also care about the direct usage and the direct stats and the signals that you're getting from the end users, whether they're coders or the average personing the chat interfaces.

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  35. You need them, but it's important that you don't overfit to them, right? So there shouldn't be the end with a be all and end all. So there's LM Marina, or it used to be called Lemsys, that's one of them that turned out sort of organically to be one of the main ways people like to test these systems, at least the chatbots. Obviously, there's loads of academic benchmarks from the test mathematics and coding ability, general language ability, science ability and so on. And then we have our own internal benchmarks that we care about. It's a kind of multi-objective optimization problem, right? You don't want to be good at just one thing. We're trying to build general systems that are good across the board. And you try and make no regret improvements. So where you improve in like, you know, coding, but it doesn't reduce your performance in other areas, right? So that's the hard part because you can, of course, you could put more coding data in or you could put more, I don't know, gaming data in.

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  36. Million products. It's very unclear Exactly. Exactly. And then you're constantly, this process of converging upstream, we call it ideas from the product surfaces or from the post training and even further downstream than that. You kind of upstream that into the core model training for the next run. So then the main model, the main Gemini track, becomes more and more general. And eventually, AGI.

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  37. Completely define the frontier. So, whatever your trade off is that you want as an individual user or as a developer, you should find one of our models satisfies that constraint.

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  38. Experimenting phase there, which you can also get a lot of gains out, and that's where you see the version numbers usually referring to the base model, the pre-trained model, and then the interim versions of 2.5 and the different sizes and the different little additions, they're often patches or post training ideas that can be done afterwards of the same basic architecture. And then, of course, on top of that, we also have different sizes, pro and flash and flashlight that are often distilled from the biggest ones. the flash model from the pro model and that means we have a range of different choices if you are the developer of do you want to prioritize performance or speed right and cost and we like to think of this pareto frontier of of you know on the one hand uh the y-axis is you know like performance and then the the x-axis is you know cost or latency and speed uh basically and we we have models that

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  39. Yeah, well, so the way it works with our different version numbers is we try to collect, so maybe it takes roughly six months or something to do a new kind of full run and the full productization of a new version. And during that time, lots of new interesting research iterations and ideas come up. And we sort of collect them all together that, you know, you could imagine the last six months worth of interesting ideas on the architecture front. Maybe it's on the data front. It's like many different possible things. And we collect package that all up, test which ones are likely to be useful for the next iteration, and then bundle that all together. And then we start the new, you know, giant hero training run. And then, of course, that gets monitored. And then at the end, then of the pre-training, then there's all the post training. There's many different ways of doing that, different ways of patching it. So there's a whole lot of

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  40. Yes. You know, it's the sort of thing that I guess Steve Jobs always talked about, right? It's simplicity, beauty, and elegance that we want, right? And nobody's there yet, in my opinion. And that's what I would like us to get to. Again, it sort of speaks to like go again, right, as a game, the most elegant, beautiful game. Can you, you know, can you make an interface as beautiful as that? And actually, I think we're going to enter an era of AI generated interfaces that are probably personalized to you so it fits the way that your aesthetic, your feel, the way that your brain works. And the AI kind of generates that depending on the task. That feels like that's probably the direction we'll end up in.

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  41. Yeah, I mean, typing is a very low bandwidth way of doing it, even if you're very fast typer. And I think we're going to have to start utilizing other devices, whether that's smart glasses, audio, earbuds. And eventually maybe some sorts of neural devices where we can increase the input and the output bandwidth to something maybe 100x of what is today.

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  42. Research track on schedule to basically intercept that in six months or a year's time. So you've kind of got to intercept where this highly changing technology is going, as well as new capabilities are coming online all the time that you didn't realize before that can allow like research to work. Or now we've got video generation. What do we do with that? This multimodal stuff, you know, is it one question I have is it really going to be the current UI that we have today, these text box chats seems very unlikely once you think about these super multimodal systems. Shouldn't it be something more like minority report where you're sort of vibing with it in a kind of collaborative way? It seems very restricted today. I think we'll look back on today's interfaces and products and systems as quite archaic in maybe in just a couple of years. So I think there's a lot of space actually for innovation to happen.

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  43. I mean, it's such a false evolving space. We're evaluating this all the time. But where we are today is that you want to continually simplify things, whether that's the interface or what you build on top of the model. You kind of want to get out of the way of the model. The model train is coming down the track and it's improving unbelievably fast. This relentless progress we talked about earlier, you know, you look at 2.5 versus 1.5 and it's just a gigantic improvement. And we expect that again for the future versions. And so the models are becoming more capable. So you've got the interesting thing about the design space in today's world, these AI first products, is you've got to design not for what the thing can do today, the technology can do today, but in a year's time. So you actually have to be a very technical product person because you've got to kind of have a good intuition for and feel for, okay, that thing that I'm dreaming about now can't be done today, but is the

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  44. Yeah, well, I mean, again, that comes back from my game design days where I used to design games for millions of gamers, people to forget about that. I've had experience with cutting-edge technology in product. That is how games was in the 90s. And so I love actually the combination of cutting-edge research and then being applied in a product and to power a new experience. And so I think it's the same skill, really, of imagining what it will be like to use it viscerally and having good taste. Coming back to earlier, the same thing that's useful in science, I think can also be useful in

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  45. Right. And we want it to be as good as possible. And in a lot of cases, it's just under the hooding, making something like maps or search work better. And ideally, for a lot of those people, it should just be seamless. It's just new technology that makes their lives more productive and helps them

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  46. Yeah, I know it's funny because if you live on X and Twitter, and I mean, it's sort of at least my feed, it's all AI and there's certain places where, you know, in the valley and certain pockets where everyone's just all they're thinking about is AI. But a lot of the normal world hasn't come across it yet

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  47. Amazing combination. And we're continually fighting and cutting away bureaucracy to allow the research culture and the relentless shipping culture to flourish. And I think we've got a pretty good balance whilst being responsible with it. As you have to be as a large company and also with a number of huge products, surfaces that we have.

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  48. Right. It is. Yeah, exactly. That's what relentlessness looks like. I think it's a question of any big company ends up having a lot of layers of management and things like that is sort of the nature of how it works. But I still operate and I was always operating with old DeepMind as a startup still. Large one, but still as a startup. And that's what we still act like today with Google DeepMind and acting with decisiveness and the energy that you get from the best smaller organizations. And we try to get the best of both worlds where we have this incredible billions of users, surfaces, and credible products that we can power up with our AI and our research. And that's amazing. And there's very few places in the world you can get that, do incredible world-class research on the one hand, and then plug it in and improve billions of people's lives the next day. That's a pretty...

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  49. You look at where we've come to from two years ago to one year ago to now, you know, I think our, we call it relentless progress along with relentless shipping of that progress is being very successful. And it's unbelievably competitive, the whole space, the whole AI space with some of the greatest entrepreneurs and leaders and companies in the world all competing now because everyone's realized how important AI is. And it's very been pleasing for us to see that progress.

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  50. Yeah, well, firstly, it's absolutely incredible, team that we have led by Core and Jeff Dean and Oriel and the amazing team we have on Gemini, absolutely world class. So you can't do it without the best talent. And of course, you have, you know, we have a lot of great compute as well. But then it's the research culture we've created, right? And basically coming together, both different groups in Google, you know, there was Google Brain, world-class team, and then the old deep mind. pulling together all the best people and the best ideas and gathering around to make the absolute greater system we could. And it has been hard, but we're all very competitive and we love research. This is so fun to do. And we're great to see our trajectory. It wasn't a given, but we're very pleased with where we are in the rate of progress is the most important thing.

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source