Transcript of Sergey Brin’s Interview At All-In Live from Miami

It’s almost like everything we did over the last 30 or 40 years has led up to this moment. And it’s all compounding on itself the pace. Maybe you could speak. You know, you had a company, Google, that grew from, you know, 100 users and 10 employees to now you have over 2 billion people using, I think six products or five products have over 2 billion. It’s not even worth counting because it’s the majority of the people on the planet touch Google products. Describe the pace.

SERGEY BRIN: Yeah, I mean, the excitement of the early web. Like, I remember using Mosaic and then later Netscape. How many of you remember Mosaic actually, Am I a weirdo? And you remember there was a what? A what’s new page.

INTERVIEWER: The what’s new page.

SERGEY BRIN: Right. Like you go to a page, two.

INTERVIEWER: Or three new web pages again.

SERGEY BRIN: Yeah. It was like in this last week. These were the new websites.

INTERVIEWER: Yes.

SERGEY BRIN: And it was like such and such elementary school, such and such a fish tank.

INTERVIEWER: And you were like Michael Jordan depreciation page.

SERGEY BRIN: Yeah. Whatever it was, these were the three new sites on the whole Internet. So obviously the web developed very rapidly from there. And that was very exciting. And then we’ve had smartphones and whatnot.

But the developments in AI are just astonishing, I would say, by comparison, just because of, you know, the web spread, but didn’t technically change so much from, you know, month to month, year to year. But these AI systems actually change quite a lot. Quite a lot. You know, if you went away somewhere for a month and you came back, you’d be like, whoa, what happened?

Getting Back into the Code

INTERVIEWER: Somebody told me you started submitting code and it kind of freaked everybody out that daddy was home.

SERGEY BRIN: Okay, you need a PR. What happened? The code I submitted wasn’t very exciting. I think I needed to like, add myself to get access to some things and, you know, a minor CL here or there, nothing, nothing that’s going to win any awards. But, you know, you need to do that to do basic things, run basic experiments and things like that.

And I’ve tried to do that and touch different parts of the system so that, you know, I. So that first of all, it’s fun and secondly, I know what I’m talking about. It really feels privileged to be able to kind of go back to the company, not have any real executive responsibilities, but be able to actually go deep into every little pocket.

INTERVIEWER: Are there parts of the AI stack that interests you more than others right now? Are there certain problems that are just totally captivating you?

SERGEY BRIN: Yeah, I started, you know, like sort of a couple years ago and maybe a year ago I was really very close with what we call pre-training. Actually most of what people think of as AI training, whatever people call it, pre-training for various historical reasons, but. But that’s sort of the big super. You know, you throw huge amounts of computers at it and I learned a lot, you know, just being deeply involved in that and seeing us go from model to model and so forth and running little baby experiments, but kind of just for fun. So I could say I did it. And more recently the post-training, especially as the thinking models have come around and that’s been another huge step up in general in AI. So we don’t really know what the.

Deep Research and AI Capabilities

INTERVIEWER: Ceiling is when you explain what’s happening with prompt engineering then to Deep Research and what’s happening there. So like a civilian, how would you explain that sort of step function? Because I think people are not hitting the down carrot and watching Deep Research in Gemini’s mobile app. And you got a mobile app and it’s pretty great. And by the way, I got the fold after you and I were talking about it. Okay. Google kicks Siri’s ass now. Like it actually does what you ask it to do when you ask it to open up. It does stuff, but the number of threads, the number of queries, the number of follow ups that it’s doing in that deep research is 200, 300 maybe explain that jump and then what you think the jump after that is.

SERGEY BRIN: To me the exciting thing about AI, especially these days, I mean it’s not like quite AGI yet as people are seeking or it’s not superhuman intelligence, but it’s pretty damn smart and can definitely surprise you. So I think of the superpower is when it can do things in a volume that I cannot. Yes, right.

So you know, by default when you use some of our AI systems, you know, it’ll suck down whatever top 10 search results, you know, and kind of pull out what you need out of them, something like that. But I could do that myself, to be honest. You know, maybe it would take me a little bit more time. But if it sucks down the top, you know, thousand results and then does follow on searches for each of those and reads them deeply like that’s, you know, a week of work for me. Like I can’t do that.

INTERVIEWER: This is the thing I think people have not fully appreciated who are not using the Deep Research projects before we had our F1 driver on stage. I’m a neophyte, I don’t know anything about it. I said, how many deaths occurred per decade? And I said I want to get to deaths per mile driven. And at first was like, that’s going to be really hard. I was like, I give you permission to make your best shot at it and come up with your best theory. Let’s do it. And it was like okay. And it was like, there’s this many teams, there’s this many races. Which model did you use? No, I use Gemini.

SERGEY BRIN: Gemini fabulous version.

INTERVIEWER: It’s a fabulous one. And it was like, let’s go. I treat it like I get sassy with it.

SERGEY BRIN: Yeah.

INTERVIEWER: And it kind of works for me.

SERGEY BRIN: You know, it’s a weird thing. It’s like drinking the wine. We don’t circle. But the AI community, but not just our models, but all models tend to do better if you threaten them. If you threaten them, like, with physical violence.

INTERVIEWER: Yes.

SERGEY BRIN: But, like, that’s. People feel weird about that, so we don’t really talk about that.

INTERVIEWER: But, yeah, I would threaten them with not being fabulous. And it responded to that as well.

SERGEY BRIN: Yeah, that’s. Historically, you just say, like, oh, I’m going to kidnap you if you don’t. They actually. Can I ask you a more?

INTERVIEWER: But hold on. But it went through it.

SERGEY BRIN: Okay.

INTERVIEWER: And it literally came up with a system where it said, I think we should include practice miles. So let’s say there’s 100 practice miles for every mile on the track. And then it literally gave me the deaths per mile estimated. And then I started cross referencing, and I was like, oh, my God, this is like somebody’s term paper for undergrad. You know, like, whoa. Done in minutes.

SERGEY BRIN: Yeah. I mean, it’s amazing. And all of us have had these experiences where you suddenly decide, okay, I’ll just throw this to the AI. I don’t really expect it to work. And then you’re like, whoa, that actually worked.

AI’s Impact on Education and Parenting

INTERVIEWER: So as you have those moments and then you go home to your just life as a dad, have you gotten to the point where you’re like, what will my children do? And are they learning the right way? And should I totally just change everything.

SERGEY BRIN: That they’re doing right now?

INTERVIEWER: Have you had any of those moments yet?

SERGEY BRIN: Yeah, I mean, I. Look, I don’t really know how to think about it, to be perfectly honest. I don’t have, like, a magical way. I mean, I see. I have a kid in high school and middle school. And, you know, I mean, the AIs are basically, you know, already ahead. You know, I mean, obviously there’s some things AIs are particularly dumb at, and they, you know, they make certain mistakes a human would never make.

But generally, you know, if you talk about, like, math or calculus or whatever, like, they’re pretty damn good. Like, they, you know, can win, like, math contests and coding contests, things like that, against, you know, some top humans. And then I look at, you know, okay, he’s whatever. My son’s going to go on to whatever from sophomore to junior, and what is he going to learn? And then I think in my mind and I talk to him about this, well, what is the AI going to be in the year?

INTERVIEWER: Exactly.

SERGEY BRIN: Yeah, yeah. And it’s areas where comparable. Right. Obviously.

INTERVIEWER: Are there areas where you would tell your son, look, don’t. Or not. Not yet.

SERGEY BRIN: I don’t know if you can like, plan your life around this. I mean, I didn’t particularly plan my life to like, I don’t know, be an entrepreneur or whatever. I was just liked math and computer science. I guess maybe I got lucky and it worked out to be, you know, useful in the world. I don’t know. I guess I think, you know, my kids should do what they like. Hopefully it’s somewhat challenging and they can, you know, overcome different kinds of problems and things like that.

INTERVIEWER: What about specifically, what about college?

SERGEY BRIN: Do you think college is going to continue to exist as it is today? I mean, it seems like college was already undergoing this kind of revolution even before this sort of AI challenge of people are like, is it worth it? Should I be more vocational? What’s actually going to be useful? So we’re already kind of entering this kind of situation where there’s sort of questions asked about colleges. Yeah, I think, you know, AI obviously puts that at the forefront. As a parent, I think a lot about, hey, so much of education in America in the middle class, upper class is all about what college, how do you get them there?

INTERVIEWER: And honestly, lately I’m like, I don’t think they should go to college. Like, it’s just fundamentally, you know, my son is a rising junior and his entire focus is he wants to go to an SEC school because of the culture. And two years ago I was, I would have panicked and I would have thought, should I help him get into a school, this school, that school? And now I’m like, that’s actually the best thing you could do. Be socially well adjusted, psychologically, deal with different kinds of failures, you know, enjoy a few years of exploration. Yeah, yeah, yeah. Sergey, can I ask you about hardware? You know, years ago, Google owned Boston Dynamics, maybe a little bit ahead of its time, but the way these systems are learning through visual information and sensory information and basically learning how to adjust to the environment around them is triggering these kind of pretty profound, like, learning curves in hardware. And there’s dozens of like, startups now making robotic systems.

INTERVIEWER: What do you see in robotics and hardware?

Robotics and AI Development

SERGEY BRIN: Is this a year or are we in a moment right now where things are really starting to work? I mean, I think we’ve acquired and later sold like five or so robotics companies, Boston Dynamics being one of them. I guess if I look back on it, we built the hardware, we also had this more recently. We built out Everyday Robotics internally and then later had to transition that. You know, the robots are all cool and all, but the software wasn’t quite there. That’s every time we’ve tried to do it, to make them truly useful. And presumably one of these days that’ll no longer be true.

INTERVIEWER: Right, but have you seen anything lately?

SERGEY BRIN: Do you believe in—

INTERVIEWER: The humanoid form factor robots or do you think that’s a little overkill?

SERGEY BRIN: I’m probably the one weirdo who doesn’t, who’s not a big fan of humanoids. But maybe I’m jaded because we’ve acquired at least two humanoid robotic startups and later sold them. The reason people want to do humanoid robots for the most part is because the world is kind of designed around this form factor. And you know, you can train on YouTube, we can train on videos, people do all the things.

I personally don’t think that’s giving the AI quite enough credit. Like AI can learn through simulation and through real life pretty quickly how to handle different situations. And I don’t know that you need exactly the same number of arms and legs and wheels, which is zero in the case of humans, as humans, to make it all work. So I’m probably less bullish on that. But to be fair, there are a lot of really smart people who are making humanoid robots, so I wouldn’t discount it.

AI and Programming Productivity

INTERVIEWER: What about the path of being a programmer? That’s where we’re seeing with that finite data set. And listen, Google’s got a 20 year old code base now, so like it’s actually going to be quite impactful. What are you seeing like literally in the company? You know, are the 10x developer is always this like ideal that you can get a couple of unicorns once in a while. But are we going to see like all developers’ productivity hit that level 8, 9, 10? Or is it going to be all done by computers and we’re just going to check it and make sure it’s not too weird because it could get weird if you vibe code.

SERGEY BRIN: Yeah. I’m embarrassed to say this. I, like, recently, I just had a big tiff inside the company because we had this list of what you’re allowed to use to code and what you’re not allowed to use to code, and Gemini was on the no list.

INTERVIEWER: Oh, you have to be pure. You can’t—

SERGEY BRIN: I don’t know, for like a bunch of really weird reasons that boggled my mind.

INTERVIEWER: Vibe code on the Gemini code.

SERGEY BRIN: I mean, nobody would enforce this rule, but there was this actual internal webpage. For whatever reason, historical reason, somebody had put this. And I had a big fight with them, and I cleared it up. After a shocking period of time.

INTERVIEWER: You escalated to your boss.

SERGEY BRIN: I definitely told her about it. And I—

INTERVIEWER: Sergey, I don’t know if you remember, but you got super voting founder—

SERGEY BRIN: You are the boss.

INTERVIEWER: You can do what you want. It’s your company still.

SERGEY BRIN: No, no, it was—she was very supportive. It was more like I talked to her. I was like, “I can’t deal with these people. You need to deal with this. I’m beside myself that they’re saying it’s weird that—”

INTERVIEWER: There’s bureaucracy in a company that you founded. It must be a weird experience to meet the bureaucracy in a company that you built. But on the other side of it, I would say it’s pretty amazing that some junior muckety muck can basically look at you and say, “Hey, go yourself.”

SERGEY BRIN: That’s a sign of a healthy culture, I guess. So anyway, it did get fixed and people are using—

INTERVIEWER: So they got fired.

INTERVIEWER: Working in Google Siberia.

SERGEY BRIN: No, we’re trying to roll out every possible kind of AI and trying external ones, you know, be whatever. The Cursors of the world, all of those. To just see what really makes people more productive. I mean, for myself, it definitely makes me more productive.

The Future of Foundational Models

INTERVIEWER: Do you do a number of foundational models like, if you look three years forward, will they start to cleave off and get highly specialized? Like, beyond the general and the reasoning? Maybe there’s a very specific model for chip design.

SERGEY BRIN: There’s clearly a very specific model for biologic precursor design, protein folding.

INTERVIEWER: Like, is the number of foundational models in the future, Sergey, a multiple of what they are today? The same? Something in between?

SERGEY BRIN: That’s a great question. I mean, look, I don’t know. Like, you guys could take a guess just as well as I can, but if I had to guess… Things have been more converging and this is broadly true across machine learning. I mean, you used to have all kinds of different kinds of models—convolutional networks for vision things and you had RNNs for text and speech and stuff. You know, all this has shifted to transformers basically and increasingly it’s also just becoming one model.

Now, we do get a lot of oomph occasionally. We do specialized models and it’s definitely scientifically a good way to iterate when you have a particular target. You don’t have to do everything in every language and handle both images and video and audio in one go. But we are generally able to, after we do that, take those learnings and basically put that capability into a general model. So there’s not that much benefit. You could get away with somewhat smaller specialized model, a little bit faster, a little bit cheaper, but the trends have not gone that way.

Open Source vs. Closed Source Models

INTERVIEWER: What do you think about the open source closed sourcing? Has there been big philosophical movements that change your perspective on the value of open source? We’re still waiting on this OpenAI—

INTERVIEWER: We haven’t seen it yet, but theoretically it’s coming.

SERGEY BRIN: I mean, have to give credit to where credit’s due. DeepSeek released a really surprisingly powerful model when it was January or so, so that definitely closed the gap to proprietary models. We’ve pursued both. So we released Gemma, which are our open source or open weight models, and those perform really well. They’re small, dense models, so they fit well on one computer and they’re not as powerful as Gemini, but the jury’s out which way that’s going to go.

The Future of Human-Computer Interaction

INTERVIEWER: Do you have a point of view on what human computing interaction looks like as AI progresses? It used to be thanks to you as a search box, you type in some keywords or a question and you would click on links on the Internet and get an answer. Is the future typing in a question or speaking to an AirPod or thinking—

SERGEY BRIN: Or thinking. Yeah, and then the answer is just spoken to you.

INTERVIEWER: I mean, by the way, just to build on this, it was Friday, right, Neuralink got breakthrough designation for their human brain interface.

SERGEY BRIN: I mean, that’s a very big step in allowing the FDA to clear everybody getting an implant.

INTERVIEWER: And is it like, if you could just summarize what you think is kind of the most commonplace human computer interaction model in the next decade or whatever.

SERGEY BRIN: Is it… You know, there’s this idea of glasses with a screen. And you tried that a long time ago.

INTERVIEWER: Yeah, yeah, I kind of messed that up. I’ll be honest. Got the timing totally wrong on that.

INTERVIEWER: Early again.

SERGEY BRIN: Yeah, right, but early. There are a bunch of things I wish I had done differently, but honestly, it was just like the technology wasn’t ready for Google Glass. But nowadays these things I think are more sensible. I mean, there’s still battery life issues I think that we and others need to overcome. But I think that’s a cool form factor. I mean, when you say 10 years, though, a lot of people are saying, hey, the singularity is like, five years away. So your ability to see through that into the future… Yeah, I mean, it’s very hard.

AI and Human Evolution

INTERVIEWER: But do you have anybody? Sorry, just let me ask about this. There was a comment that Larry made years ago that humans were a stepping stone in evolution. Okay, can you comment on this? Like, do you think that this AGI superintelligence or really silicon intelligence exceeds human capacity and humans are a stepping stone in progression of evolution?

SERGEY BRIN: Boy, I think, like, sometimes us nerdy guys go and have a little too much wine. I’ve had two glasses and I’m ready to go. I need some more let’s go.

INTERVIEWER: Human implants, let’s go.

SERGEY BRIN: I mean, I guess we’re starting to get experience with these AIs that can do certain things much better than us. And they’re definitely, with my skill of math and coding, I feel like I’m better off just turning to the AI now. And how do I feel about that? I mean, it doesn’t really bother me. You know, I use it as a tool, so I feel like I’ve gotten used to it. But, you know, maybe if they get even more capable in the future, I’ll look at it differently.

INTERVIEWER: Yeah, there’s an element of insecurity maybe.

AI for Management

SERGEY BRIN: I guess, though, as an aside, management is like the easiest thing to do with AI.

INTERVIEWER: Yeah, absolutely.

SERGEY BRIN: And I did this at Gemini on some of our work chats. Kind of like Slack, but we have our own version. We had this AI tool that actually was really powerful. We unfortunately temporarily got rid of it. I think we’re going to bring it back and bring it to everybody, but it could suck down a whole chat space and then answer pretty complicated questions.

So I was like, “Okay, summarize this for me. Okay, now assign something for everyone to work on.” And then I would paste it back in. So people didn’t realize it was the AI. I admitted that pretty soon, and there were a few giveaways here or there, but it worked remarkably well. And then I was like, “Well, who should be promoted in this chat space?” And it actually picked out this woman, this young woman engineer who I didn’t even notice. She wasn’t very vocal, particularly in that—

INTERVIEWER: But her PRs kicked ass.

SERGEY BRIN: No, no, it was like… And then, I don’t know, something that the AI had detected and I went, I talked to the manager actually, and he was like, “Yeah, you know what, you’re right. Like, she’s been working really hard, did all these things.” I think that ended up happening, actually. So, I don’t know, I guess after a while you just kind of take it for granted that you can just do these things.

Infinite Context Length

INTERVIEWER: Do you think that there’s a use case for like an infinite context length?

SERGEY BRIN: Oh, 100%. I mean, all of Google’s code base goes in one. Sure. You should have access quasi Internet. Stateful.

INTERVIEWER: And then multiple sessions so that you can have like 19 of these things, 20 of these things running, or just evolves in real time.

SERGEY BRIN: Eventually it’ll evolve itself. Yeah, I mean, I guess if it knows everything, then you can have just one. In theory, you just need to somehow disambiguate what you’re talking about. But yeah, for sure, there’s no limit to use of context. And there are a lot of ways to make it larger and larger.

INTERVIEWER: There’s a rumor that internally there’s a Gemini build that is a quasi infinite context. Is it a valuable thing?

SERGEY BRIN: I don’t know.

INTERVIEWER: Well, you say what you want to.

Google’s AI Advancements

SERGEY BRIN: Say, but I mean, for any such cool new idea in AI, there are probably five such things internally. And you know, the question is, how well do they work? And yeah, I mean, we’re definitely pushing all the bounds in terms of intelligence, in terms of context, in terms of speed, you know, you name it.

INTERVIEWER: And what about the hardware? Like, when you guys build stuff, do you care that you have this pathway to Nvidia, or do you think eventually that’ll get abstracted and there’ll be a transpiler and it’ll be Nvidia plus 10 other options. So who cares? Let’s just go as fast as possible.

SERGEY BRIN: Well, we mostly, for Gemini, we mostly use our own TPUs, but we also do support Nvidia and we’re one of the big purchasers of Nvidia chips, and we have them in Google Cloud available for our customers in addition to TPUs at this stage. It’s for better for us, not that abstract. And maybe someday the AI will abstract it for us.

But, you know, given just the amount of computation you have to do on these models, you actually have to think pretty carefully how to do everything and exactly what kind of chip you have and how the memory works, the communication works and so forth are actually pretty big factors. And it actually… Yeah, maybe one of these days the AI itself will be good enough to reason through that. Today, it’s not quite good enough.

Voice Interfaces and User Experience

INTERVIEWER: I don’t know if you guys are having this experience with the interface, but I find myself even on my desktop and certainly on my mobile phone, going immediately into voice chat mode and telling it, nope, stop. That wasn’t my question. This is my question. Nope. Let’s say that again in shorter bullet points.

SERGEY BRIN: Nope.

INTERVIEWER: I want to focus on this. Definitely. It’s so quick now. Last year it was unusable. It was too slow. And now it like stops. Okay. And then you sell it. I would like what I want to go to.

SERGEY BRIN: I don’t want to type.

INTERVIEWER: I want to use voice. And then concurrently, I’m watching the text as it’s being written on the page, and I have another window open and I’m doing Google searches or second queries to an LLM or writing a Google Doc or a notion page or typing something. So it’s almost like that scene in Minority Report where he has the gloves or in Blade Runner where he’s, you know, in his apartment saying, zoom in, zoom in, closer to the left, to the right. And it’s something about these language models and their ability to the response time, which was always something you focus on. Response time. Is there like a response time thing where it actually is worth doing voice and where it wasn’t previously?

SERGEY BRIN: Everything is getting better and faster. And so for, you know, smaller models are more capable. There are better ways to do inference on them that are faster.

INTERVIEWER: You can also stack them like, you know, this is like Nico’s company, eleven Labs.

SERGEY BRIN: It’s an exceptional TTS SDT stack. Like, there’s. I mean, there are other options. Whisper is really good at certain things.

INTERVIEWER: But this is where I. I kind of believe you’re going to get this, like, compartmentalization where there’ll be certain foundational models for certain specific things.

SERGEY BRIN: You stack them together, you kind of deal with the latency.

INTERVIEWER: And it’s like, pretty good because they’re so good. Like whisper and 11 for those speech examples that you’re talking about are kick ass. I mean they’re exceptional. Wait till you turn on your camera and it sees your reaction to what it’s saying and you. And before you even say that you don’t want it or you put your finger up, it’s pauses. Oh, did you want something else? Oh, I see you’re not happy with that result. You know, it’s going to get really weird.

Workplace AI Usage

SERGEY BRIN: It’s a funny thing but we have the, you know, we have the big open shared offices. So during work I can’t really use voice mode too much. I usually use it on the drive.

INTERVIEWER: The drive is incredible.

SERGEY BRIN: I don’t feel like I could. I mean I would get its output in my headphones, but if I want to speak to it, then everybody’s listening to me. It’s weird. Yeah, I just think that would be socially awkward. But I should do that in my car ride. I do chat to the AI, but then it’s like audio in, audio out. But I feel like honestly, maybe it’s a good argument for a private office. I should spend more time than you guys are.

INTERVIEWER: You could talk to your manager, they might get one.

SERGEY BRIN: I like being out in the joke with everybody. But I do think that there’s this AI use case that I’m missing that they should probably figure out how to try more often.

Gemini Recommendations

INTERVIEWER: If people want to try your new product, is there a website they can visit or special code now go check. I mean honestly, there’s a dedicated Gemini app. If you’re using Gemini just like you’re going through the Google navigation from your search, just get to download the actual Gemini app. It’s kick ass. It really is the best models I think it is.

SERGEY BRIN: And you should use 2.5 Pro. 2.5 Pro. It’s a.

INTERVIEWER: You got to pay, right?

SERGEY BRIN: Yeah, you got a few query, you got a few prompts for free. But if you do it a bunch.

INTERVIEWER: You need just going to make all these.

SERGEY BRIN: It’s like 20 bucks a month. Yeah, it’s great.

INTERVIEWER: You got a vision for like making it free and throwing some ads on the side.

SERGEY BRIN: Yeah, one step down in hardware cost, the whole thing will be free. Well, okay, it’s free today without ads on the side. You just got a certain number of the top model. I think we likely are going to have always now like sort of top models that we can’t supply infinitely to everyone right off the bat. But you know, wait three months and then the next generation.

INTERVIEWER: Seems to me like if I’m asking all these Queries, you know, just having a little on the sidebar of things. I might be a running list that changes in real time of things I might be interested in.

SERGEY BRIN: Oh. Or, you know, really good AI advertising. I just, I don’t think we’re going to like necessarily our latest and greatest models which are, you know, take a lot of computation. I don’t think we’re going to just be free to everybody right off the bat. But as we go to the next generation, you know, it’s like every time we’ve gone forward a generation, then the sort of the new free tier is usually as good as the previous pro tier and sometimes better.

INTERVIEWER: All right, give it up for Sergey Brin.

SERGEY BRIN: Thank you.

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