Most of text on this page is extracted from interview & summarised using Google’s Gemini LLM.
The All-In Podcast is a popular weekly show featuring four prominent figures in the tech and business world – Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg.
These friends and poker buddies cover a wide range of topics including economics, technology, politics, and social issues in unfiltered, hour-long discussions.
Recently they organised a summit in LA. It’s called “All in Summit” I think it’s still going on and so far people like Elon Musk, JD Vance have done discussion with besties.
And bestie, David Friedberg interviewed Sergey Brin. Here is a YouTube recording of that.
Some short clips taken out of this interview
Below is a brief summary of what was discussed in this interview.
Topic 1: Sergey Brin’s Return to Google and Excitement for AI.
0:49 Sergey Brin agrees to appear on stage last minute, surprised by the size of the conference.
2:18 Brin is working at Google almost every day, driven by his excitement for AI. He describes the current progress in AI as the most exciting thing he’s seen in his career as a computer scientist.
2:34 Brin reflects on how AI was once a footnote in computer science curriculum, but neural nets have led to incredible progress.
3:35 Brin notes the rapid pace of AI development, with new capabilities emerging every month.
Topic 2: The Future of AI and Model Development
1:49 David Friedberg asks if large language models (LLMs) are an existential threat to Google Search. Brin acknowledges the narrative but doesn’t directly address the competitive threat.
6:16 Friedberg asks about the evolution of AI models – will there be one “god model” or many smaller, specialized models? Brin suggests a trend toward more unified models, incorporating learnings from different AI systems.
7:49 Friedberg questions the rationality of the massive compute build-out for AI. Brin acknowledges the need for compute but expresses some skepticism about extreme extrapolations of compute needs.
11:49 Brin believes competition in AI is beneficial, driving progress and innovation.
Topic 3: Applications of AI and Google’s Approach
- 4:00 Friedberg asks if AI is an extension of search or a rewriting of information retrieval. Brin believes AI will touch many aspects of life, with search being just one.
- 4:20 Brin discusses using AI for programming, describing how he has used AI to write code for him.
- 10:30 Friedberg asks about the most surprising successes of AI applications. Brin highlights AlphaFold’s impact on biology, noting its widespread use.
- 11:16 Brin acknowledges Google’s past timidity in deploying AI, partly due to concerns about mistakes and negative publicity.
- 13:29 Brin believes it’s important to release AI technology and allow for experimentation, even if it leads to some mistakes.
- 17:46 Brin emphasizes the magical potential of AI to benefit humanity and the importance of competition in driving progress.
Topic 4: Google’s Robotics Ventures
- 11:51 Friedberg brings up Google’s history of robotics ventures, many of which were spun out or sold. Brin admits the timing wasn’t right for those ventures.
- 12:19 Brin acknowledges that Google has had multiple robotics businesses, but they haven’t achieved widespread success yet.
And below is the full transcript of the interview. Enjoy 😊
David Friedberg: No introduction needed. Welcome.
Sergey Brin: I just agreed to this last minute. You know, I didn’t know where you pulled up that clip so fast. You guys are…
David Friedberg: The team is amazing.
Sergey Brin: Kind of amazing. This is kind of amazing.
David Friedberg: Yeah. I thought… Sergey, Sergey just asked… Like he asked to come check out the conference, and I was like, definitely, like come hang out.
Sergey Brin: I didn’t actually understand, to be perfectly honest. I thought you guys just kind of had a podcast and like a little get-together or something, but…
David Friedberg: Yeah.
Sergey Brin: This is kind of mind-blowing. Congratulations.
David Friedberg: Yeah.
Sergey Brin: Thank you.
David Friedberg: Well, I’m glad you came out. Thanks for doing it.
Sergey Brin: Yeah, thanks for doing it. I’m feeling a little bit shy, but…
David Friedberg: Yeah. Wow. But thanks for agreeing to chat for a little bit. We’re going to talk for a little bit. So this was not on the schedule. But, I thought it’d be great to talk to you given where you sit in the world as AI is on the brink of and is actively changing the world. Obviously, you know, you founded Google with Larry in 1998, and, you know, recently it’s been reported that you’ve kind of spent a lot more time at Google working on AI. I thought maybe, and a lot of industry analysts and pundits have been kind of arguing that LLMs and conversational AI tools are kind of an existential threat to Google search. That’s, that’s one of the… And I think a lot of those people don’t build businesses, or they have competitive investments, but we’ll leave that to the side. But there’s this big kind of narrative on what’s going to happen to Google, and, and where does Google sit with AI? And I know you’re spending a lot of time on it. So thanks for coming to talk about it. How much time are you spending at Google? What are you working on?
Sergey Brin: Yeah, honestly, like pretty much every day. I mean, like I’m missing today, which is, you know, one of the, or one of the reasons I was a little reluctant, but I’m glad I came. But I think as a computer scientist, I’ve never seen anything as exciting as all the AI progress that’s happened in the last few years. Thanks. No, but it’s, it’s kind of mind-blowing. When I went to grad school in the ‘90s, you know, AI was like kind of like a footnote in the curriculum almost. Like you’re like, oh, maybe you have to do this one little test on AI. We tried all these different things. They don’t really work. That’s it. That’s all you need to know. And then somehow miraculously all these people who are working on neural nets, which was one of the big discarded approaches to AI in like the ‘60s, ‘70s, and so forth, just started to make progress. A little bit more compute, a little more, more data, a few clever algorithms. And the thing that’s happened in this last decade or so is just amazing as a computer scientist. Like every month, you know, well, all of you, I’m sure, use all the AI tools out there. But like every month there’s like a new amazing capability, and I’m like probably, you know, doubly wowed as everybody else is that computers can do this. And so, yeah, for me, I really got back into the technical work because I just don’t want to miss out on this as, as a computer scientist.
David Friedberg: Is it an extension of search, or a rewriting of how people retrieve information?
Sergey Brin: I mean, I just think that the AI touches so many different elements of day-to-day life, and sure, search is one of them. But it kind of covers everything. For example, programming itself. Right. Like the way that I think about it is very different now. Like, you know, writing code from scratch feels really hard compared to just asking the AI to do it. Yeah, sorry. So what do you do then?
Sergey Brin: Actually, I’ve written a little bit of code myself just for, just for kicks, just for fun. And then sometimes I’ve had the AI write the code for me. Which was, which was fun. I mean, just one example, I wanted to see how good our AI models were at Sudoku. So I had the AI model itself write a bunch of code that would automatically generate Sudoku puzzles, and then feed them to the AI itself, and then score it, and so forth. Right. But it could just write that code, and I was like talking to the engineers about it, and, you know, whatever, we had some debate back and forth. Like I came back half an hour later, it’s done. And they, they were kind of impressed because they don’t honestly use the AI tools for their own coding as much as I think they ought to.
David Friedberg: Right. So that’s an interesting example because maybe there’s a model that does Sudoku really well. Maybe there’s a model that like answers information questions for me about facts and the, in the world. Maybe there’s an AI model that designs houses. A lot of people are working towards these ginormous general-purpose LLMs. Is that where the world goes? Some people, I think, refer, I don’t know who wrote this recently, said there’s a god model, like there’s going to be a god model, and that’s why everyone’s investing so much. As if you can build the god model, you’re done. You’ve got your AGI, whatever terms you want to use. There’s this one thing to rule them all. Or is the reality of AI that there are lots of smaller models that do application-specific things, maybe work together like in an agent system? Like what’s the, what, what, what is the evolution of model development, and, and the, then how models are ultimately used to do all these cool things?
Sergey Brin: Yeah, I mean, I think like if you looked 10, 15 years ago, there were different AI techniques that were used for different problems altogether. Like, you know, the chess-playing AI was very different than image generation, which was, you know, very different than…
David Friedberg: Like recently the graph neural net at Google that like outperformed every physics forecasting model. I don’t know if you know this, but you guys published it.
Sergey Brin: Yeah, that was pretty awesome.
David Friedberg: Yeah. Right. But it was like a totally different architec… It’s a different system. It was trained differently, and it ended up…
Sergey Brin: Yeah.
David Friedberg: …in that particular…
Sergey Brin: So there are, historically, there have been different systems. Yeah. And even recently, like the International Math Olympiad that we participated in, we got silver medal as an AI, actually one point away from gold. But we actually had three different AI models in there. There was one very formal theorem-proving model that actually did basically the best. There was one specific to geometry problems, believe it or not, that was just a special kind of AI. And then there was the general-purpose language model. But since then, we’ve tried to take the learnings from that. That was just a couple months ago. And tried to infuse some of the, sort of, knowledge and ability from the formal prover into our general language models that’s still work in progress. But I do think the trend is to have a more unified model. I don’t know if I’d call it a god model, but to have certainly sort of shared architectures, and ultimately even shared models.
David Friedberg: Right. So if that’s true, you need a lot of compute to train and develop that model, that big model.
Sergey Brin: Yeah. Yeah. I mean, you definitely need a lot of compute. I think like I’ve, I’ve read some articles out there that just like extrapolate. They’re like, you know, it’s like 100 megawatts, and a gigawatt, and 10 gigawatts, and 100 gigawatts, and I don’t know if I’m quite a believer in, you know, that level of extrapolation. Partly because also the algorithmic improvements that have come over the course of the last few years, maybe are actually even outpacing the increased compute that’s put into these models.
David Friedberg: So is it irrational, the build-out that’s happening? Everyone talking about the NVIDIA revenue, the NVIDIA profit, the NVIDIA market cap supporting all of what people call the hyperscalers, and the growth of the infrastructure needed to build these very large-scale models using the techniques of today. Is this irrational, or is it rational because if it works, it’s so big that…
Sergey Brin:
David Friedberg: …it doesn’t matter how much it costs?
Sergey Brin: Well, first of all, I’m not like an economist, or like a market watcher, or the way that you guys are very carefully watch companies. So I just want to disclaim my abilities in the space. I think that I know, for us, we’re kind of building out computers quickly as we can, and we just have a huge amount of demand. I mean, for example, our cloud customers just want a huge amount of TPUs, GPUs, you name it. You know, we just can’t… We have to turn down customers because we just don’t have the compute available. And we use it internally to train our own models, to serve our own models, and so forth. So I guess, I think there are very good reasons that companies are currently building out compute at a fast pace. I just don’t know that I would look at the training trends and extrapolate three orders of magnitude ahead just blindly from where we are today.
David Friedberg: But the enterprise demand is there out there.
Sergey Brin: You know, I mean, they, they want to do lots of other things. For example, running inference on all these AI models, applying them to all these new applications. There doesn’t seem to be a limit right now.
David Friedberg: And where have you seen the greatest success, surprising success in the application of models, whether it’s in robotics, or biology? What are you like seeing that you’re like, wow, this is really working? And where are things going to be more challenging, and take longer than I think some people might be expecting?
Sergey Brin: Now that you mention those. Well, I would say in biology, you know, we’ve had AlphaFold for quite a while. And I’m not personally a biologist, but when I talk to biologists out there, like everybody uses it. And it’s more recent variants. And that is, I guess, a different kind of AI. But like I said, I do think all these things tend to converge. Robotics, for the most part, I see in this sort of wow stage. Like, wow, you could make a robot do that with just, you know, this general-purpose language model, or just a little bit of fine-tuning this way or that, and it’s like amazing. But maybe not for the most part yet at the level of robustness that would make it like day-to-day useful.
David Friedberg: But you see a line of sight to it.
Sergey Brin: Yeah. Yeah. I mean, it would be, it’s I don’t see any particular…
David Friedberg: But Google had the robotics business, and then spun it out, or sold it.
Sergey Brin: We’ve had like…
David Friedberg: You had a lot of robotics.
Sergey Brin: …five or six robotics businesses. Just weren’t… The timing wasn’t right.
David Friedberg: Yeah.
Sergey Brin: Yeah. Unfortunately, I don’t know. I guess, yeah, I think that was just a little too early, to be perfectly honest. I mean, there was like Boston Dynamics, what was called, Schaft. I don’t even remember all the ones. We had… Anyway, we’ve had like five or six, embarrassingly. But they’re very cool. And they were impressive. It, yeah, it just feels kind of silly having done all that work, and seeing now how capable these general language models are that include, for example, vision, and image, and are multimodal, and they can understand the scene, and everything, and not having had that at the time. Yeah, it just feels like you were sort of on a treadmill that wasn’t going to get anywhere without the modern AI technology.
David Friedberg: You spend a lot of time on core technology. Do you also spend a lot of time on product visioning, where things are going, and what like the human-computer interaction modality there’s going to be in the future in a world of AI everywhere? Like…
Sergey Brin:
David Friedberg: What’s our life going to be like?
Sergey Brin: I mean, I guess there’s water cooler chitchat about things like that.
David Friedberg: Care to share any?
Sergey Brin: I… Try to think of things that aren’t embarrassing.
David Friedberg: Struggling. But, uh…
Sergey Brin: All friends.
David Friedberg: I, I guess it’s like just really hard to, you know, just forecast, like, you know, to think five years out because, you know, based on the base technical capability of the AIs, what enables the applications. And then sometimes, you know, somebody will just whip up a little demo that you just didn’t think about. And it’ll be kind of mind-blowing. Yeah. And, and of course, then from demo to actually making it, it real, and production, and so forth, it takes time. I don’t know if you’ve played with like, the Astra model, but it’s just sort of live video and audio, and you can chat with the AI about what’s going on in your environment.
David Friedberg: You’ll give me access, right?
Sergey Brin: Yeah, I’ll get… Well, once I have access. I mean, I’m, I’m sort of sometimes the, the slowest to get some of these things. But it’s yeah, there’s like a moment of, wow, and you’re like, oh my god, this is amazing. And then you’re like, okay, well, it doesn’t correctly like 90% of the time. But am I really, like… Is that then worth it if 10% of the time it’s kind of making a mistake, or taking too long, or whatever? And then you have to work, work, work, work, work, work, work to get to perfect all those things, make it responsive, make it available, whatever. Right. And then you actually end up with something kind of amazing.
David Friedberg: I heard a story that you went in, you were on-site… I should have mentioned this to you before you came on stage, see if you were cool about talking about it. Here we are. And there are like a bunch of engineers showed you that you could like use AI to write code. And it was like, well, we haven’t pushed it in Gemini yet because we want to make sure it doesn’t make mistakes. And there was this like hesitation culturally at Google to do that. And you were like, no, if it writes code, push it. And you really, and a lot of people have told me this story because they said, and or, you know, I’ve heard this that it was really important to hear that from you, the founder, in being really clear that Google’s conservatism, you know, can’t rule the day today, and we need to kind of see Google push the envelope. Is that accurate? Is that kind of…
Sergey Brin:
David Friedberg: …how you’ve spent some time, or…
Sergey Brin: I don’t remember the specific incident, just to be honest. But, but I’m not surprised. I mean, I guess that’s the question for me. Is like as Google’s gotten so big, there’s more to lose.
Sergey Brin: I think there’s like this… Yeah, I think there is a little bit of fear. I mean, language models to begin with, like we invented them basically with a Transformer paper that was, what are we, six, eight years ago, something like that. Right. Oh, Noam, by the way, is back at Google now, which is awesome. And yeah, we were, we were too timid to deploy them. And, you know, for a lot of good reasons. Like whatever, they, some make mistakes, they say embarrassing things, whatever, you know. They’re, you know, sometimes they’re just like kind of embarrassing, how dumb they are. Even today’s, like, the latest and greatest things, like make really stupid mistakes people would never make. And at the same time, like they’re incredibly powerful, and they can help you do things you never would have done. And, you know, like I’ve, like, programmed pretty, really complicated things with my kids, like they’ll just program it because they just ask the AI using all these really complicated APIs, and all kinds of things that would take like a month to like learn. So I just think that that capability is magic, and you need to be willing to have some embarrassments, and takes some risks. And, and I think we’ve gotten better at that. And, well, you guys have probably seen some of our embarrassments.
David Friedberg: But you’re comfortable with that. I mean, you have super voting stock. You’re still, like… I mean, you’re comfortable with the embarrassments at this stage because it’s so important to do this, like…
Sergey Brin: I mean, not, not particularly on the basis of my stock. But I, um…
David Friedberg: But as a, you know, I mean…
Sergey Brin: But I, am I comfortable? I mean, I guess I just think of it is this, something magical we’re giving the world. Yeah. And I think as long as we communicate it properly, like saying like, look, this thing is amazing, and we’ll periodically get stuff really wrong. Then I think we should put it out there, and let people experiment, and see what new ways they find to use it. I just don’t think this is the technology you want to just kind of keep close to the chest, and hidden until it’s like perfect.
David Friedberg: Hmm. Do you think that there’s so many places that AI can affect the world, and so much value to be created that it’s not really a race between Google, and Meta, and Amazon? Like people frame these things as kind of a race. Is there just so much value to be created that you’re working on a lot of different opportunities, and it’s not really about who builds the, the model that score, the LLM that scores the best? That there’s so much more to it. I mean, how do you kind of think about…
David Friedberg: …the world out there, and Google’s place in it?
Sergey Brin: I mean, I think it’s very helpful to have competition in the sense that all these guys are vying, and we were just… We were number one for on LLMs for a couple weeks, by the way, just now. And I think we’re, last time I checked, we’re still beat the top model. There’s just some ego stuff.
David Friedberg: Okay, but you, so, so you do care. Yeah.
Sergey Brin: Yeah. Not saying… Not, not brag. But, and and, you know, we’ve come a long way since, you know, a couple, whatever, years ago when ChatGPT launched, or, you know, we were quite a ways behind. I’m really pleased with all the progress we made. So we definitely pay attention. I mean, I think it’s great that there are all these AI companies out there, be it us, OpenAI, Anthropic, you name it. There’s Mistral. It’s, it’s a… I mean, it’s a big, fast-moving field. But I guess your question is… Yeah, I mean, I think there’s tremendous value to humanity. And I think if you think back, you know, like when I was in college, let’s say, and there wasn’t really a proper internet, or like web, the way that we know it today. Like the amount of effort it would take to get basic information, Yeah. the amount of effort it would take to communicate with people, you know, before cell phones, and things. Like we’ve gained so much capability across the world. But the, sort of, the new AI is another big capability. And pretty much everybody in the world can get access to it in one form or another these days, and I think it’s super exciting.
David Friedberg: It’s awesome. Sorry we have so, such limited time. Sergey, thank you so much for joining us. Please join me in thanking Sergey.
Sergey Brin: Thank you.
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