Google DeepMind CEO Demis Hassabis + Google Co-Founder Sergey Brin: Scaling AI, AGI Timeline, Simulation Theory

Demis Hassabis is the CEO of Google DeepMind. Sergey Brin is the co-founder of Google. The two leading tech executives join Alex Kantrowitz for a live interview at Google's IO developer conference to discuss the frontiers of AI research. Tune in to hear their perspective on whether scaling is tapped out, how reasoning techniques have performed, what AGI actually means, the potential for an intelligence explosion, and much more. Tune in for a deep look into AI's cutting edge featuring two executives building it. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Alex Kantrowitz?

  Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:03Alex Kantrowitz?

  All right, buddy, we have an amazing crowd here today. We're going to be live streaming this, so let's hear you. Make some noise so everybody can hear that you're here. Let's go.

• 1:13Alex Kantrowitz?

  I'm Alex Kancheritz. I'm the host of Big Technology Podcast, and I'm here to speak with you about the frontiers of AI with two amazing guests. Demis Hassabis, the CEO of DeepMind, is here. Google DeepMind. Good to see you, Demis. Good to see you, too. And we have a special guest, Sergey Brin, the co-founder of Google, is also here.

• 1:37Alex Kantrowitz?

  All right, so this is going to be fun. Let's start with the frontier models. Demis, this is for you. With what we know today about frontier models, how much improvement is there left to be unlocked? And why do you think so many smart people are saying that the gains are about to level off?

• 2:00Speaker 01

  I think we're seeing incredible progress. You've all seen it today, all the amazing stuff we showed in the keynote. So I think we're seeing incredible gains with the existing techniques, pushing them to the limit. But we're also inventing new things all the time as well. And I think to get all the way to something like AGI, I think, may require one or two more new breakthroughs. And I think we have lots of promising ideas that we're cooking up, and we hope to bring into the main branch of the Gemini branch.

• 2:26Alex Kantrowitz?

  All right. And so there's been this discussion about scale. You know, does scale solve all problems, or does it not? So I want to ask you, in terms of the improvement that's available today, is scale still the star, or is it a supporting actor?

• 2:41Speaker 01

  I think I've always been of the opinion you need both. You need to scale to the maximum the techniques that you know about. You want to exploit them to the limit, whether that's data or compute scale. And at the same time, you want to spend a bunch of effort on what's coming next, maybe six months, a year down the line. So you have the next innovation that might do a 10x leap in some way to kind of intersect with the scale. So you want both, in my opinion. But I don't know. Sergey, what do you think?

• 3:09Speaker 02

  I mean, I agree, it takes both. You know, you can have algorithmic improvements and simply compute improvements, better chips, more chips, more power, bigger data centers. I think that historically, if you look at things like the N-body problem and simulating, you know, just gravitational bodies and things like that, as you plot it, the algorithmic advances have actually beaten out the computational advances, even with Moore's Law. If I had to guess, I would say the algorithmic advances are probably going to be even more significant than the computational advances. But both of them are coming up now. So we're kind

• 3:54Speaker 02

  of getting the benefits of both.

• 3:55Alex Kantrowitz?

  And Demis, do you think the majority of your improvement is coming from? Building bigger data centers and using more chips like there's talk about how the world will be just wallpapered with data centers. Is that your vision?

• 4:07Speaker 01

  Well, no, look, I mean, we're definitely going to need a lot more data centers. It's amazing that, you know, it still amazes me from a scientific point of view, we turn sand into thinking machines. It's pretty incredible. But actually, it's not just for the training. It's now we've got these models that everyone wants to use, you know, and actually, we're seeing incredible demand for 2.5 Pro. And I think Flash, we're really excited about how performant that is for the incredible sort of low cost. I think the whole world's going to want to use these things. And so we're going to need a lot of data centers for serving and also for inference time compute. Giving, you know, you saw you saw Deep Think today, 2.5 Pro Deep Think. The more time you give it, the

• 4:50Speaker 01

  better it will be. And certain tasks, very high value, very difficult tasks, you'll wanna, it will be worth letting it think for a very long time. And we're thinking about how to push that even further. And again, that's going to require a lot of chips at runtime.

• 5:04Alex Kantrowitz?

  Okay, so you brought up test time compute. We've been about a year into this reasoning paradigm, and you and I have spoken about it twice in the past as something that you might be able to add on to traditional LLMs to get gains. So I think this is like a pretty good time for me to be like, what's what's happening? What is, can you help us contextualize the magnitude of improvement we're seeing from reasoning?

• 5:25Speaker 01

  No, well, we've always been big believers in what we're now calling this thinking paradigm. If you go back to our very early work on things like AlphaGo and AlphaZero, our agent work on on playing games, they will all have this type of attribute of a thinking system on top of a model. And actually, you can quantify how much difference that makes if you look at a game like chess or Go. You know, we had versions of AlphaGo and AlphaZero with the thinking turned off. So it was just the model telling you its first idea. And you know, it's not bad. It's maybe like master level, something like that. But then if you turn the thinking on, it's be way beyond world champion level. You know, it's like a 600 elo plus difference between the two versions. So you can see that in games, let alone for the real world, which is way more complicated.

• 6:09Speaker 01

  And I think the gains will be potentially even bigger by adding this thinking type of paradigm on top. Of course, the challenge is that your models, and I talked about this earlier in the talk, need to be a kind of world model, and that's much harder than building a model of a simple game, of course. And, you know, it has errors in it, and yet those can compound over longer term plans. So, but I think we're making really good progress on all that, all those fronts.

• 6:36Speaker 02

  Yeah, look, I mean, as Demis said, I mean, DeepMind really pioneered a lot of this reinforcement learning work. And what they did with AlphaGo and AlphaZero, as you mentioned, showed, as I recall, something you would take 5,000 times as much training to match what you were able to do with, still a lot of training, and the inference time compute that you were doing with Go. So it's obviously a huge advantage. And obviously, like most of us, we get some benefit by thinking before we speak. And although...

• 7:13Alex Kantrowitz?

  Not always.

• 7:16Speaker 02

  I always get reminded to do that. But I think that the AIs obviously are much stronger once you add that capability, and I think we're just at the tip of the iceberg right now in that sense. It's been less than a year that these models have really been out.

• 7:35Speaker 01

  Especially if you think about, obviously, with an AI, during its thinking process, it can also use a bunch of tools, or even other AIs, during that thinking process to improve what the final output is. So I think it's going to be an incredibly powerful paradigm.

• 7:50Alex Kantrowitz?

  DeepThink is very interesting. I'm going to describe it, I'm trying to describe it right. It's basically a bunch of parallel reasoning processes working and then checking each other. And then it's like reasoning on steroids. Now, Demis, you mentioned that the industry needs a couple more advances to get to AGI. Where would you put this type of mechanism? Is this one of those that might get the industry closer?

• 8:14Speaker 01

  I think so. I think it's maybe part of one.

• 8:16Alex Kantrowitz?

  Okay.

• 8:17Speaker 01

  Shall I, shall we say? And there are others too that we need to, you know, maybe this can be part of improving reasoning. Where does true invention come from? Where, you know, you're not just solving a maths conjecture, you're actually proposing one or hypothesizing a new theory in physics. You know, that's, I think we don't have systems yet that can do that type of creativity. I think they're coming. And these types of, these types of paradigms might be helpful in that, things like thinking. And then probably many other things. I mean, I think we need a lot of advances on the accuracy of the world models that we're building. I think you saw that with VO, the potential VO3 of how, it amazes me like the, how it can intuit the physics of the light and the gravity.

• 9:01Speaker 01

  Having someone who, I used to work on, on computer games, not just the AI, but also graphics engines in my early career. And remember having to do all of this by hand, you know, and program all of the lighting and the shaders and all of these things, incredibly complicated stuff we used to do in early games. And now it's, it's just intuiting it within the model. It's, it's pretty astounding.

• 9:22Alex Kantrowitz?

  I saw you shared an image of a frying pan with some onions and some oil.

• 9:27Speaker 01

  I hope you all like that.

• 9:28Alex Kantrowitz?

  There was no subliminal messaging about that?

• 9:31Speaker 01

  No, not really, not really. Just maybe a subtle, subtle message.

• 9:34Alex Kantrowitz?

  Okay. So we've said, we said the word AGI or the acronym AGI a couple of times. There's, I think a movement within the AI world right now to say, let's not say AGI anymore. The term is so overused as to be meaningless. But Demis, it seems like you think it's important. Why?

• 9:51Speaker 01

  Yeah, I think it's very important, but I think, I mean, maybe I need to write something about this also with Shane Legg, who's our, our chief scientist, who was one of the people who invented the term 25 years back. I think there's sort of two things that are getting a little bit conflated. One is like, what can a typical person do? An individual do? And we can, you know, we're all very capable, but we can only do, however capable we are, there's only a certain slice of things. That one is expert in, right? And or, you know, you could say, what can you do what like ninety percent of humans can do? That's obviously going to be economically very important. And I think from a product perspective, also very important. So it's it's a very important milestone. So maybe we should say that's like, you know, typical human intelligence.

• 10:35Speaker 01

  But what I'm interested in and what I would call AGI is really a more theoretical construct, which is what is the human brain as an architecture able to do? Right? And and that's the human brain is an important reference point because it's the only evidence we have maybe in the universe that general intelligence is possible. And there it would have to be able to, you'd have to show your system was capable of doing the range of things even the best humans in history were able to do with the same brain architectures. Not one brain, but the same brain architecture. So what Einstein did, what Mozart was able to do, what Marie Curie and so on. And that it's clear to me today, systems don't have that. And then the other thing that why I think it's sort of overblown the hype today on AGI is that our systems are not consistent enough to be considered to be fully general yet.

• 11:19Speaker 01

  They're quite general, so they can do, you know, thousands of things. You've seen many impressive things today. But every one of us have experience with today's chatbots and assistants. You can easily, within a few minutes, find some obvious flaw with them. Some high school math thing that it doesn't solve, you know, some basic game it can't play.

• 11:38Speaker 01

  It's not very difficult to find that, those holes in the system. And for me, for something to be called AGI, it would need to be consistent, much more consistent across the board than it is today. It should take like a couple of months for for for maybe a team of experts to find a a hole in it, an obvious hole in it. Whereas, you know, today it takes an individual minutes to find that.

• 12:01Alex Kantrowitz?

  Sergey, this is a good one for you. Do you think that AGI is going to be reached by one company and it's game over? Or could you see Google having AGI, OpenAI having AGI, Anthropic having AGI? China having AGI?

• 12:16Speaker 02

  Wow, that's a great question. I mean. I guess I would suppose that one company or country or entity will reach AGI first. Now, it is a little bit of a, you know, kind of a spectrum. It's not like a completely precise thing, so it's conceivable that there will be more than one roughly in that range at the same time. After that, what happens, I mean, I think it's very hard to foresee, but you could certainly imagine there's going to be multiple entities that come through. In our AI space, you know, we've seen, whatever, when we make a certain kind of advance, like other companies are quick to follow and vice versa.

• 13:00Speaker 02

  When other companies make certain advances, it's, you know, it's a kind of a constant leapfrog. So I do think there's an inspiration element that you see, and that would probably encourage more and more entities to cross that threshold.

• 13:15Alex Kantrowitz?

  Demis, what do you think?

• 13:17Speaker 01

  Well, I think we probably do. I think it is important for the field to agree on a definition of AGI, so I will, maybe we should try and help that to coalesce, assuming there is one. You know, there probably will be some organizations that get there first, and I think it's important to, that those first systems are built reliably and safely. And I think after that, if that's the case, you know, we can imagine using them to shard off many systems that have safe architectures sort of built under, you know, sort of provably underneath them. And then you could have, you know, personal AGIs and all sorts of things happening. But it's, you know, it's quite difficult, as Sergey says, it's pretty difficult to predict, sort of see beyond the event horizon to predict what that's going to be like.

• 13:59Alex Kantrowitz?

  Right, so we talked a little bit about the definition of AGI, and a lot of people have said AGI must be knowledge, right, the intelligence of the brain. What about the intelligence of the heart? Demis, briefly, does AI have to have emotion? To be considered AGI, can it have emotion?

• 14:16Speaker 01

  I think it will need to understand emotion. I don't know if, I think it will be a sort of almost a design decision if we wanted to mimic emotions. I think there's no, I don't see any reason why it couldn't in theory. But it might be different, or we might, it might be not necessary, or in fact not desirable for them to have the sort of emotional reactions that we do as humans. So I think, again, it's a bit of an open question as we get closer to this AGI timeframe and, you know, sort of events, which I think is more on a five to 10 year time scale. So I think we have a bit of time, not much time, but some time to research those kinds of questions.

• 14:52Alex Kantrowitz?

  When I, when I think about how the timeframe might be shrunk, I wonder if it's going to be the creation of self-improving systems. And last week, I almost fell out of my chair reading this headline about something called Alpha Evolve, which is an AI that helps design better algorithms and even improve the way LLMs train. So Demis, are you trying to cause an intelligence explosion?

• 15:18Speaker 01

  No, not an uncontrolled one. I, look, I, I think it's an interesting first experiment, it's amazing system, great team that's working on that, where it's interesting now to start pairing other types of techniques, in this case evolutionary programming techniques, with the latest foundation models, which are getting increasingly powerful. And I actually want to see in our exploratory work, a lot more of this kind of combinatorial systems and sort of pairing different approaches together. And you're right, that is one of the things, a self-improvement, someone discovering a kind of self-improvement loop would be one way where things might accelerate further than they're even going today. So, and, and we've seen it before with our own work with things like AlphaZero, you

• 16:03Speaker 01

  know, learning chess and Go and any two-player game from scratch within, you know, less than 24 hours, starting from random with self-improving processes. So we know it's possible, but again, those are in quite limited game domains, which are very well described. So the real world is far messier and far more complex. So it remains to be seen if that type of approach can work in a more general way.

• 16:27Alex Kantrowitz?

  Sergey, we've talked about some very powerful systems and it's a race, it's a race to develop these systems. Is that why you came back to Google?

• 16:38Speaker 02

  I mean, I think as a computer scientist, it's a very unique time in history. Like, honestly, anybody who's a computer scientist should not be retired right now, should be working on AI. That's what I would just say. I mean, there's just never been a greater sort of problem and opportunity, a greater cusp of technology. So I don't, I wouldn't say it's because of the race, although we fully intend that Gemini will be the very first AGI. Clarify that.

• 17:12Speaker 02

  But to be immersed in this incredible technological revolution, I mean, it's unlike, you know, I went through sort of the Web 1.0 thing. It was very exciting and whatever. We had mobile, we had this, we had that. But I think this is scientifically far more exciting. And I think, I think ultimately the impact on the world is going to be even greater. And as much as, you know, the web and mobile phones have had a lot of impact, I think AI is going to be vastly more transformative.

• 17:47Alex Kantrowitz?

  So what do you do day to day?

• 17:50Speaker 02

  I think I torture people like Demis. It was amazing, by the way, he tolerated me crashing this fireside. I'm in the, you know, I'm across the street, you know, pretty much every day. And they're just people who are working on the key Gemini text models, on the pre-training, on the post-training. Mostly those, I periodically delve into some of the multimodal work. VO3 is, you've all seen. But I tend to be pretty deep into technical details. And that's a luxury I really enjoy, fortunately, because guys like Demis are, you know, minding the shop.

• 18:36Speaker 02

  Yeah, that's just where, you know, my scientific interest is. It's deep in the algorithms and how they can evolve.

• 18:44Alex Kantrowitz?

  Okay, let's talk about the products a little bit, some that were introduced recently.

• 18:49Alex Kantrowitz?

  I just want to ask you a broad question about agents, Demis, because when I look at other tech companies building agents, what we see in the demos is usually something that's contextually aware, has a disembodied voice, is often interacted with. You often interact with it on a screen. When I see DeepMind and Google demos, oftentimes it's through the camera. It's very visual. There was an announcement about smart glasses today. So talk a little bit about if that's the right read, why Google is so interested in having an assistant or companion that is something that sees the world as you see it.

• 19:26Speaker 01

  Well, it's for several reasons, several threads come together. So as we talked earlier, we've always been interested in agents. That's actually the heritage of DeepMind. Actually, we started with agent-based systems in games. We are trying to build AGI, which is a full general intelligence. Clearly, that would have to understand the physical environment, the physical world around you. And two of the massive use cases for that, in my opinion, are a truly useful assistant that can come around with you in your daily life, not just stuck on your computer or one device. It needs to, we want it to be useful in your everyday life for everything. And so it needs to come around you and understand your physical context. And then the other big thing is, I've always felt for robotics to work, you

• 20:06Speaker 01

  sort of want what you saw with Astra on a robot. And I've always felt that the bottleneck in robotics isn't so much the hardware, although obviously there's many, many companies and working on fantastic hardware, and we partner with a lot of them. But it's actually the software intelligence that I think is always what's held robotics back. But I think we're in a really exciting moment now, where finally, with these latest versions, especially 2.5 Gemini, and more things that we're going to bring in, this kind of VIO technology and other things, I think we're going to have really exciting algorithms to make robotics finally work and, you know, sort of realize its potential, which could be enormous. So I think this, and then in the end, AGI needs to be able to do all of those things. So

• 20:50Speaker 01

  for us, and that's why you can see, we always had this in mind. That's why Gemini Was built from the beginning, even the earliest versions, to be multimodal. And that made it harder at the start, because it's harder to make things multimodal than just text only. But in the end, I think we're reaping the benefits of those decisions now, and I see many of the Gemini and I team here in the front row, of the correct decisions we made. They were the harder decisions, but we made the right decisions, and now you can see the fruits of that with all of what you've seen today, actually.

• 21:17Alex Kantrowitz?

  Sergey, I've been thinking about whether to ask you a Google Glass question.

• 21:21Speaker 02

  Oh, fire away.

• 21:22Alex Kantrowitz?

  What did you learn from Glass that Google might be able to apply today, now that it seems like smart glasses have made a reappearance?

• 21:33Speaker 02

  Wow, yeah, a great question. I learned a lot. I mean, that was, I definitely feel like I made a lot of mistakes with Google Glass, I'll be honest. I am still a big believer in the form factor, so I'm glad that we have it now. And now it's like, looks like normal glasses, doesn't have the thing in front. I think there was a technology gap, honestly. Now in the AI world, the things that these glasses can do to help you out without constantly distracting you, that capability is much higher.

• 22:08Speaker 02

  There's also just, I just didn't know anything about consumer electronics supply chains, really, and how hard it would be to build that and have it be at a reasonable price point, managing all the manufacturing and so forth. This time we have great partners that are helping us build this.

• 22:29Speaker 02

  So that's another step forward.

• 22:32Speaker 02

  What else can I say? I do have to say I miss the airship with the wing suiting skydivers for the demo.

• 22:41Speaker 02

  Honestly, it would have been even cooler here at Shoreline Amphitheater than it was up in Moscone back in the day, but maybe we'll have to... We should probably polish the product first this time.

• 22:52Speaker 01

  Yes, we'll do it that way around this time.

• 22:54Speaker 02

  Make sure it's ready and available and then we'll do a really cool demo. So that's probably a smart move.

• 22:59Speaker 01

  Yeah, what I will say is, I mean, look, we've got obviously incredible history of glass devices and smart devices, so we can bring all those learnings to today. And very excited about our new glasses, as you saw. What I've always talking to our team and Sharam and the team about is that, I mean, I don't know if Sergey would agree, but I feel like the universal assistant is the killer app for smart glasses. And I think that's what's going to make it work, apart from the fact that it's all the hardware technology has also moved on and improved a lot, is this, I think, I feel like this is the actual killer app, the natural killer app for it.

• 23:35Alex Kantrowitz?

  Okay. Briefly on video generation. I sat in the audience in the keynote today and was like fairly blown away by the level of improvement we've seen from these models. And I mean, you had filmmakers talking about it in the presentation. I want to ask you, Demis, specifically about model quality. If the internet fills with video that's been made with artificial intelligence, does that then go back into the training and lead to a lower quality model than if you were training just from human-generated content?

• 24:10Speaker 01

  Yeah, well, look, we know there's a lot of worries about this so-called like model collapse. I mean, video is just one thing, but in any modality, text as well. There's a few things to say about that. First of all, we're very rigorous with our data quality management and curation. We also, at least for all of our generative models, we attach synth ID to them. So there's this invisible AI actually-made watermark that is pretty very robust, has held up now for, you know, a year, 18 months since we released it. And all of our images and videos are embedded with this watermark. So we can detect, and we're releasing tools to allow anyone to detect these watermarks and know that that was an AI-generated image or video.

• 24:54Speaker 01

  And of course, that's important to combat deep fakes and misinformation, but it's also, of course, you could use that to filter out, if you wanted to, whatever was in your training data. So I don't actually see that as a big problem. Eventually, we may have video models that are so good, you could put them back into the loop as a source of additional data, synthetic data it's called. And there, you've just got to be very careful that you're actually creating from the same distribution that you're going to model. You're not distorting that distribution somehow. The quality is high enough. We have some experience of this in a completely different vein with things like AlphaFold, where there wasn't actually enough real experimental data to build the final AlphaFold. So we had to build an earlier version that then predicted about a million protein structures.

• 25:39Speaker 01

  And then we selected, it had a confidence level on that. We selected the top three, four hundred thousand and put them back in the training data. So there's lots of, it's very cutting edge research to like mix synthetic data with real data. So there are also ways of doing that. But on the terms of the video sort of generator stuff, you can just exclude it if you want to, at least with our own work. And hopefully other gen media companies follow suit and put robust watermarks in. Also, obviously, first and foremost, to combat deep fakes and misinformation.

• 26:09Alex Kantrowitz?

  Okay, we have four minutes. I got four questions left. We now move to the miscellaneous part of my questions. Let's see how many we can get through and as fast as we can get through them. Let's go to Sergey with this one. What does the web look like in 10 years?

• 26:22Speaker 02

  What does the web look like in 10 years? I mean,

• 26:25Speaker 01

  You've got one minute.

• 26:26Speaker 02

  Boy, I think 10 years, because of the rate of progress in AI, is so far beyond anything we can possibly, not just the web. I mean, I don't know, I don't think we really know what the world looks like in 10 years.

• 26:40Alex Kantrowitz?

  Okay, Demis?

• 26:42Speaker 01

  Well, I think it's a good answer. I do think the web, I think in nearer term, the web is going to change quite a lot. If you think about an agent-first web, like, does it really need to, you know, it doesn't necessarily need to see renders and things like we do as humans using the web. So I think things will be pretty different in a few years.

• 26:59Alex Kantrowitz?

  Okay, this is kind of an under-over question. AGI before 2030 or after 2030?

• 27:08Speaker 02

  2030, boy, you really kind of put it on that fine line. I'm gonna, I'm gonna say before.

• 27:13Alex Kantrowitz?

  Before?

• 27:14Speaker 02

  Yeah.

• 27:14Alex Kantrowitz?

  Demis?

• 27:15Speaker 01

  I'm just after.

• 27:16Alex Kantrowitz?

  Just after?

• 27:17Speaker 01

  Yeah.

• 27:17Alex Kantrowitz?

  Okay.

• 27:20Speaker 02

  No pressure, Demis.

• 27:21Speaker 01

  Exactly. I have to go back and get working harder.

• 27:25Alex Kantrowitz?

  Is that...

• 27:26Speaker 02

  I can ask for it, he needs to deliver it.

• 27:28Speaker 01

  Exactly.

• 27:30Speaker 02

  Stop sandbagging. We need next week.

• 27:33Speaker 01

  That's true.

• 27:35Alex Kantrowitz?

  I'll come to the review. All right, so would you hire someone that used AI in their interview? Demis?

• 27:42Speaker 01

  Oh, in their interview?

• 27:46Speaker 01

  Depends how they used it. I think using today's models, tools, probably not. But I think that would be, well, it depends how they would use it, actually. I think it's probably the answer.

• 27:56Alex Kantrowitz?

  Sergey?

• 27:58Speaker 02

  I mean, I never interviewed at all, so...

• 28:03Speaker 02

  I don't know. I feel it would be hypocritical for me to judge people exactly how they interview. Yeah, I haven't either, actually. So, snap on that. I've never done a job with people.

• 28:13Alex Kantrowitz?

  Okay. So, Demis, I've been reading your tweets. You put a very interesting tweet up where there was a prompt that created some sort of natural scene.

• 28:23Speaker 01

  Oh, yeah.

• 28:24Alex Kantrowitz?

  Here was the tweet. "Nature to simulation at the press of a button does make you wonder," with a couple of emojis, and people ran with that and wrote some headlines saying, "Demis thinks we're in a simulation." Are we in a simulation?

• 28:39Speaker 01

  Not in the way that, you know, Nick Bostrom and people talk about. I think, I do think, though, this, so I don't think this is some kind of game, even though I wrote a lot of games, I do think that ultimately, underlying physics is information theory. So I do think we're in a computational universe, but it's not just a straightforward simulation. I can't answer you in one minute. But, um, but I think, I think the fact that these systems are able to model, um, real structures in nature is quite interesting and telling. And I've been thinking a lot about our work we've done with AlphaGo and AlphaFold and these types of systems. I've spoken a little about it. Maybe at some point I'll write up a scientific paper about what I think that really means

• 29:23Speaker 01

  in terms of what's actually going on here in reality.

• 29:26Alex Kantrowitz?

  Sergey, you want to make a headline?

• 29:29Speaker 02

  Well, I think that argument applies recursively, right? If we're in a simulation, then by the same argument, whatever beings are making the simulation are themselves in a simulation for roughly the same reasons, and so on and so forth. So I think you're going to have to either accept that we're in an infinite stack of simulations, or that there's got to be some stopping criteria.

• 29:52Alex Kantrowitz?

  And what's your best guess?

• 29:55Speaker 02

  I think that we're taking a very anthropocentric view, like when we say simulation in the sense that some kind of conscious being is running a simulation that we are then in, and that they have some kind of semblance of desire and consciousness that's similar to us. I think that's where it kind of breaks down for me. So I just don't think that we're really equipped to reason about sort of one level up in the hierarchy.

• 30:28Alex Kantrowitz?

  Okay, well, Demis, Sergey, thank you so much. This has been such a fascinating conversation.

• 30:32Speaker 02

  Thank you.

• 30:33Alex Kantrowitz?

  And thank you all. Alex. Thank you. Sergey. Pleasure.

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