Sergey Brin, Google Co-Founder | All-In Live from Miami

(0:00) The Besties welcome Sergey Brin! (0:40) Sergey on his return to Google, and how an OpenAI employee played a role! (5:58) AI's true superpower and the next jump (12:23) AI robotics: humanoids and other form factors (17:07) Future of foundational models and open-source (19:59) Human-computer interaction in the age of AI (31:09) Partner shoutouts: Thanks to OKX, Circle, Polymarket, Solana, BVNK, and Google Cloud! Check out OKX: https://www.okx.com Check out Circle: https://www.circle.com Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffec

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

• 0:02Jason Calacanis?

  We've got a special guest who's gonna come join us. This always happens. Here he is, Sergey Brin, everybody. Oh my god. Somebody told me you started submitting code, and it kind of freaked everybody out that daddy was home.

• 0:17Speaker 01

  All models tend to do better if you threaten them.

• 0:19Jason Calacanis?

  If you threaten them.

• 0:20Speaker 01

  Like with physical violence.

• 0:21Jason Calacanis?

  Yes.

• 0:22Speaker 01

  Management is like the easiest thing to do with AI. Absolutely.

• 0:26Jason Calacanis?

  It must be a weird experience to meet the bureaucracy in a company that you didn't hire.

• 0:29Chamath Palihapitiya?

  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."

• 0:36Jason Calacanis?

  No, but I'm serious. That's a sign of a healthy culture, actually. You're punching a clock, man. I hear the reports. You and I have talked about it. You're going to work every day.

• 0:45Speaker 01

  Yeah, it's been, you know, some of the most fun I've had in my life, honestly. And I retired like a month before COVID hit, in theory.

• 0:54Jason Calacanis?

  Yeah.

• 0:55Speaker 01

  And I was like, you know, this has been good. I want to do something else. I want to hang out in cafes, read physics books. And then like, a month later, I was like, eh, that's not really happening. So then I just started to go to the office, you know, once we could go to the office. And actually, to be perfectly honest, there was a guy from OpenAI, this guy named Dan. And I ran into him at a little party, and he said, you know, "Look, what are you doing? This is like the greatest transformative moment in computer science ever."

• 1:34Jason Calacanis?

  And you're a computer scientist.

• 1:35Speaker 01

  I'm a computer scientist.

• 1:36Jason Calacanis?

  Forget that. You're the founder of Google, but you were a PhD student for computer science.

• 1:39Speaker 01

  I haven't finished my PhD yet, but working on it.

• 1:42Jason Calacanis?

  Keep working. You'll get there.

• 1:44Speaker 01

  Technically on leave of absence.

• 1:45Jason Calacanis?

  Right.

• 1:46Speaker 01

  And he told me this, and I'd already started kind of going into the office a little bit. And I was like, you know, he's right. And it has been just incredible, just to, well, you guys all obviously follow all the AI technology, but being a computer scientist, it is, you know, the most exciting thing of my life, just technologically.

• 2:07Jason Calacanis?

  And the exponential nature of this, the pace of it... It dwarfs anything we've seen in our career. 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 that touch Google products. Describe the pace.

• 2:46Speaker 01

  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's New page?

• 3:01Jason Calacanis?

  The What's New page is great.

• 3:02Speaker 01

  Right, like, you go to homepage.

• 3:03Jason Calacanis?

  Two or three new webpages a day.

• 3:04Speaker 01

  Yeah, it was like, in this last week, these were the new websites.

• 3:08Jason Calacanis?

  Yes.

• 3:08Speaker 01

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

• 3:13Jason Calacanis?

  Yeah.

• 3:14Speaker 01

  And you were like, wow.

• 3:15Jason Calacanis?

  Michael Jordan appreciation page.

• 3:16Speaker 01

  Yeah, whatever it was, these were the three new sites on the whole internet. So, obviously, the web, you know, developed very rapidly from there. And that was a very exciting, and then we found smartphones and whatnot. But, you know, this, the developments in AI are just astonishing, I would say, by comparison. Just because of, you know, the web's bred, but didn't technically change so much from, you know, month to month, year to year. But these AI systems actually changed quite a lot. You know, the, like, if you went away somewhere for a month and you came back, you'd be like, whoa, what happened?

• 3:56Jason Calacanis?

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

• 4:05Chamath Palihapitiya?

  Daddy did a PR? What happened?

• 4:07Speaker 01

  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 gonna win any awards. But, but I, you know, you need to do that to, to do basic things, run basic experiments and things like that. And I've, I've tried to do that and touch different parts of the system so that, you know, so that, first of all, it's fun and secondly, I know what I'm talking about. It's 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

• 4:50Speaker 01

  go deep into every little pocket.

• 4:53Chamath Palihapitiya?

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

• 5:01Speaker 01

  Yeah, I started, you know, like sort of, I don't know, a couple of years ago and maybe a year ago, I was really very close with the, 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 that's sort of the big, super, you know, you throw huge amounts of computers at it. And I, 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,

• 5:45Speaker 01

  especially as the thinking models have come around. And that's been, you know, another huge step up in general in AI. So, you know, we don't really know what the ceiling is.

• 5:58Jason Calacanis?

  When you explain what's happening with prompt engineering then to deep research and what's happening there to 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 like, by the way, I got the fold after you and I were talking about it. Okay, Google. Kick 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.

• 6:42Speaker 01

  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.

• 7:05Jason Calacanis?

  Yes.

• 7:06Speaker 01

  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, 1,000 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.

• 7:34Jason Calacanis?

  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, it 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 just many teams, there's just many races." Which model did you use? I used Gemini. Gemini Fabulous version? The fabulous one. And it was like, "Let's go." But I treat it like, I get sassy with it, and it kind of works for me.

• 8:15Speaker 01

  You know, it's a weird thing. It's like...

• 8:17Jason Calacanis?

  Is he drinking the wine?

• 8:18Speaker 01

  We don't circulate this too much in the AI community. But not just our models, but all models tend to do better if you threaten them.

• 8:26Jason Calacanis?

  If you threaten them.

• 8:26Speaker 01

  Like with physical violence.

• 8:28Jason Calacanis?

  Yes.

• 8:29Speaker 01

  But like that's, people feel weird about that, so we don't really talk about that.

• 8:32Jason Calacanis?

  Yeah, I was threatening with not being fabulous, and it responded to that as well.

• 8:37Speaker 01

  Yeah, historically, you just say like, "Oh, I'm going to kidnap you if you don't blah, blah, blah." Yeah, they actually...

• 8:43Jason Calacanis?

  Can I ask you a more... But it went through it, 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.

• 9:09Speaker 01

  It's, 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."

• 9:21Jason Calacanis?

  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 that they're doing right now?" Have you had any of those moments yet?

• 9:40Speaker 01

  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 are 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, 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 gonna go on to, whatever,

• 10:23Speaker 01

  from sophomore to junior, and what is he gonna learn? And then I think in my mind, and I talk to him about this, well, what is the AI going to be in one more year?

• 10:32Jason Calacanis?

  Exactly, exactly.

• 10:32Speaker 01

  Yeah. Yeah, and it's like not comparable, right? Obviously...

• 10:37Jason Calacanis?

  Are there areas where you would tell your son, "Look..."

• 10:40Chamath Palihapitiya?

  Don't or not yet?

• 10:42Speaker 01

  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 like 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, I think, you know, my kids should do what they like. Hopefully it's somewhat challenging and they can, you know, overcome uh different kinds of problems and things like that.

• 11:09Chamath Palihapitiya?

  What about specifically?

• 11:10David Friedberg?

  What about college? Do you think college is gonna continue to exist as it is today?

• 11:14Speaker 01

  I mean, it seems like college was already undergoing this kind of uh revolution even before this sort of AI challenge of people are like, is it worth it? Should I be more vocational? What's actually gonna be useful? So we're already kind of entering this kind of situation uh where there's sort of questions asked about colleges. Yeah, I think, you know, AI obviously puts at the forefront.

• 11:39David Friedberg?

  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? And honestly lately, I'm like, I don't think they should go to college. Like, it's just fundamentally.

• 11:55Chamath Palihapitiya?

  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?

• 12:20David Friedberg?

  Enjoy a few years of exploration.

• 12:21Chamath Palihapitiya?

  Yeah.

• 12:22David Friedberg?

  Yeah.

• 12:22Chamath Palihapitiya?

  Yeah.

• 12:23David Friedberg?

  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. What do you see in robotics and hardware? Is this a year or are we in a moment right now where things are really starting to work?

• 12:57Speaker 01

  I mean, I think we've, you know, acquired and later sold like five or so robotics companies and, you know, Boston 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, you know, to make them truly useful. And presumably, one of these days, that'll no longer be true.

• 13:33Chamath Palihapitiya?

  Right. But have you seen anything lately? Yeah. And do you believe in the humanoid form factor robots? Or do you think that's a little overkill?

• 13:39Speaker 01

  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, you know, we at least acquired at least two humanoid robotic startups and later sold them. But, but the reason is, I mean, 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 given the AI quite enough credit. Like, AI can learn, you know, through simulation and through real life, pretty quickly, how to handle different situations.

• 14:21Speaker 01

  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. And so I'm, 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.

• 14:39Jason Calacanis?

  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 code base now, so like, it actually could be quite impactful. What are you seeing, like, literally in the company? You know, are the ten X developers always this like ideal that you can, you know, you get a couple of unicorns once in a while? But are we going to see like all developers, like, you know, their productivity hit that level, eight, nine, 10, and they're just going to? Or is it going to be all done by computers? And we're just going to check it, make sure it's not too weird?

• 15:10Speaker 01

  Um.

• 15:11Jason Calacanis?

  Because it could get weird.

• 15:13Chamath Palihapitiya?

  If you vibe code, yeah.

• 15:15Speaker 01

  I'm embarrassed to say this. Okay, 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.

• 15:29Jason Calacanis?

  You oh, you have to be pure. You can't.

• 15:31Speaker 01

  I don't know, for like a bunch of really weird reasons that it was like boggled my mind that.

• 15:37Chamath Palihapitiya?

  You couldn't vibe code on the Gemini code.

• 15:39Speaker 01

  I mean, nobody would like enforce this rule, but but there was this, you know, actual internal webpage, for whatever is historical reason, somebody had put this and I had a big fight with them like, you know, I cleared it up after a shocking long period of time.

• 15:56Chamath Palihapitiya?

  You escalated to your boss.

• 15:57Speaker 01

  Oh, I I definitely told Sundar about it.

• 16:02Jason Calacanis?

  Sorry, I don't know if you remember, but you got super voting founder.

• 16:05Chamath Palihapitiya?

  You are the boss.

• 16:06Jason Calacanis?

  You can do what you want. It's your company still.

• 16:09Speaker 01

  No, no, it was. He was very supportive. It was more like I was like, I talked to him. I was like, I can't deal with these people. You need to deal with this like I just like I'm beside myself that they're like saying.

• 16:21Jason Calacanis?

  It's weird that there's bureaucracy like in a company that you find must be a weird experience to meet the bureaucracy in a company that you didn't hire.

• 16:28Chamath Palihapitiya?

  But 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. No, but I'm serious. That's a sign of a healthy culture, actually.

• 16:38Speaker 01

  I guess so. Anyway, it did get fixed and people are using it.

• 16:42Jason Calacanis?

  So they got fired.

• 16:45Jason Calacanis?

  That person's working in Google Siberia?

• 16:48Speaker 01

  No, we're trying to, you know, 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, definitely makes me more productive because I'm not.

• 17:07Chamath Palihapitiya?

  Do you do you think the 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. There's clearly a very specific model for biologic precursor design, protein folding, like is the number of foundational models in the future, Sergey? A multiple of what they are today, the same, something in between?

• 17:34Speaker 01

  That's a great question. I kind of. If I, 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, you know, things have been more converging. Uh, and this is sort of broadly true across machine learning. I mean, you used to have all kinds of different kinds of models and whatever. Convolutional networks for vision things and, you know, you had, um, whatever R N Ns for text and speech and stuff. And, uh, you know, all this has shifted to transformers, basically. Uh, and increasingly it's also just becoming one model. Uh, now we do get a lot of oomph.

• 18:19Speaker 01

  Occasionally we do specialized models, uh, and it's, it's definitely scientifically a good way to iterate when you have a particular target. You don't have to like do everything in every language and handle whatever, both images and video and audio and, uh, in one go. Um, 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. Um, you know, you could, you can get away with somewhat smaller specialized model, a little bit faster, a little bit cheaper, but the trends have not gone that way.

• 19:01Chamath Palihapitiya?

  What do you think about the open source, closed source thing? Has there been big philosophical movements that change your perspective on the value of open source?

• 19:11Speaker 01

  Um.

• 19:12Chamath Palihapitiya?

  We're still waiting on this, oh, you know, open A I.

• 19:16Speaker 01

  Oh, yeah, yeah, yeah.

• 19:17Chamath Palihapitiya?

  I mean, we haven't seen it yet, but theoretically it's coming.

• 19:20Speaker 01

  I mean, have to give credit, uh, to where credits do. I mean, DeepSeek released a really surprisingly powerful model, uh, when it was January, uh, or so. So that, that definitely closed the gap to proprietary models. We've pursued both, so we released Gemma, uh, which are our, our open source or, you know, open to wait models. And, um, those perform really well. They're small, dense models, so they fit well on one computer. Um, and.

• 19:52Speaker 01

  They're not as powerful as Gemini, but I mean, the jury's out which way that's gonna go.

• 19:59David Friedberg?

  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?

• 20:21Chamath Palihapitiya?

  Or thinking.

• 20:22David Friedberg?

  Or thinking, or like, what's the, what's the, yeah, and then the answer is just spoken to you.

• 20:26Chamath Palihapitiya?

  I mean, by the way, just to build on this, it was Friday, right? Neuralink got breakthrough designation for their human brain interface. I mean, that's a very big step in allowing the FDA to clear everybody getting an implant.

• 20:38David Friedberg?

  Yeah, 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? Is it a, you know, there's this idea of glasses with a screen in the glasses, and you tried that a long time ago?

• 20:53Speaker 01

  Yeah, I kind of messed that up, I'll be honest.

• 20:57Speaker 01

  Got the timing totally wrong on that.

• 20:59Chamath Palihapitiya?

  Early again.

• 21:00Speaker 01

  Yeah.

• 21:01Chamath Palihapitiya?

  Right, right, but early.

• 21:02Speaker 01

  There are a bunch of things I wish I'd 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, you know, we and others need to overcome. But I think that's a cool form factor. I mean, when you say 10 years, though, you know, a lot of people are saying, hey, the singularity is like

• 21:28Chamath Palihapitiya?

  Right, five years away.

• 21:28Speaker 01

  Five years away, so your ability to see through that into the future.

• 21:36David Friedberg?

  Yeah.

• 21:36Speaker 01

  I mean, it's very hard to guess.

• 21:37David Friedberg?

  But do you have a, you, 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, do you think that this AGI, superintelligence, or really silicon intelligence, exceeds human capacity and humans are a stepping stone in, you know, progression of evolution?

• 22:02Speaker 01

  Boy, I think like, sometimes those nerdy guys go and get, have a little too much wine and chitter chat.

• 22:07David Friedberg?

  I've had two glasses.

• 22:08Speaker 01

  And, um.

• 22:09David Friedberg?

  I'm ready to go.

• 22:11Speaker 01

  I need just more.

• 22:12David Friedberg?

  Let's go there.

• 22:12Speaker 01

  For this conversation.

• 22:15Speaker 01

  Human implants, let's go. I mean, I guess we're starting to get experience with these AIs that can do certain things, you know, much better than us. Um, and they're definitely, you know, 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, um, I'll look at it differently. Yeah, there's a moment of insecurity, maybe. I guess so. As an aside, management is like the easiest thing to do with AI. Yeah, absolutely.

• 22:56Speaker 01

  And I did this, you know, uh, at Gemini on some of our, you know, work chats, um, kind of like Slack, but we have our own version. We had this AI tool that actually was really powerful. We unfortunately, anyway, 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 a pretty complicated questions. So I was like, okay, summarize this for me. Okay, now assign something for everyone to work on. And, uh, and then I would paste it back in so people didn't realize it was the AI. That's awesome. I admitted it pretty soon. Um, 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?

• 23:40Speaker 01

  Uh, and I actually picked out this woman, this young woman engineer who like, you know, I didn't even notice she wasn't very vocal, uh, particularly in that group. But her PR is kicked ass. No, no, it was like, and then, uh, I don't know, something that the AI had detected and I went, I talked to the manager actually, and, and he was like, yeah, you know what? You're right. Like, uh, she's been working really hard, did all these things. Wow. I think that ended up happening, actually. Uh, 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. I don't know. It hasn't really. Do you think that there's a, a use case for?

• 24:17Chamath Palihapitiya?

  Like an infinite context length?

• 24:20Speaker 01

  Oh, 100%. I mean,

• 24:21Chamath Palihapitiya?

  All of Google's code base goes in one day.

• 24:24Speaker 01

  Exactly infinite. But sure, you should have access to...

• 24:26Chamath Palihapitiya?

  Quasi-infinite.

• 24:27Speaker 01

  Yeah.

• 24:27Chamath Palihapitiya?

  Stateful.

• 24:28Speaker 01

  Yeah.

• 24:29Chamath Palihapitiya?

  And then multiple sessions, so that you can have like 19 of these things, 20 of these things running. Or it just evolves itself. In real time. Eventually, it'll evolve itself.

• 24:35Speaker 01

  Yeah, I mean, I guess if it knows everything, then you can have just one in theory. You just need to somehow...

• 24:41Chamath Palihapitiya?

  Disentangement.

• 24:42Speaker 01

  Tell it what you're talking about. But yeah, for sure, there's no limit to use of a context. And there, you know, there are a lot of ways to make it larger and larger.

• 24:52Chamath Palihapitiya?

  There's a rumor that internally, there's a Gemini build that is a quasi-infinite context length. Is it? Is it a valuable thing? Like, I don't know. Well, you say what you want to say, bud.

• 25:04Speaker 01

  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.

• 25:24Chamath Palihapitiya?

  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.

• 25:40Speaker 01

  Well, we mostly, for Gemini, we mostly use our own TPUs. So, but we also do support Nvidia and we were one of the big purchasers of Nvidia chips. And we have them in Google Cloud, available for our customers in addition to TPUs.

• 26:01Speaker 01

  At this stage, it's for better or 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.

• 26:32Jason Calacanis?

  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 short, a bullet point, nope, I want to focus on this.

• 26:50Speaker 01

  Definitely.

• 26:51Jason Calacanis?

  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 bullet point.

• 26:59Speaker 01

  It's what I want to go to. I don't want to type, I want to use voice.

• 27:02Jason Calacanis?

  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 there's something about these language models and their ability to the response time, which was always something you focused on, response time. Is there like a response time thing where it actually is worth doing voice and where it wasn't previously?

• 27:41Speaker 01

  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.

• 27:51Chamath Palihapitiya?

  You can also stack them, like, you know, this is like Nico's company, Eleven Labs. It's an exceptional TTS SDT stack. Like, there's, I mean, there are other options. Whisper is really good at certain things, 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, you stack them together, you kind of deal with the latency, and it's like pretty good because they're so good. Like Whisper and Eleven, for those speech examples that you're talking about are fucking kick ass. I mean, they're exceptionally

• 28:24Jason Calacanis?

  Well, wait till you turn on your camera and it sees your reaction to what it's saying, and you go, and before you even say that you don't want it, or you put your finger up, it pauses, "Oh, did you want something else? Oh, I see you're not happy with that result," you know? It's gonna get really weird.

• 28:40Speaker 01

  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.

• 28:48Jason Calacanis?

  The drive is incredible.

• 28:50Speaker 01

  Well, 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.

• 28:58Jason Calacanis?

  It's weird.

• 28:59Speaker 01

  I just think that would be socially awkward. But I should, I should do that. In my car ride, I do chat to the AI, but then it's like audio in, audio out.

• 29:07Jason Calacanis?

  Yep.

• 29:07Speaker 01

  But I feel like I, honestly, maybe it's a good argument for a private office. I should spend more time like you guys are.

• 29:13Jason Calacanis?

  You could talk to your manager.

• 29:15Speaker 01

  Yeah.

• 29:16Jason Calacanis?

  They might get one.

• 29:18Speaker 01

  I like being out in the bullpen, so to speak. I like being with everybody.

• 29:22Jason Calacanis?

  Yeah.

• 29:23Speaker 01

  But I do think that there's this AI use case that I'm missing, which I should probably figure out how to try more often.

• 29:29Jason Calacanis?

  If people want to try your new product, is there a website they can visit? Or something, 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 the, download the actual Gemini app. It's kick ass.

• 29:46Chamath Palihapitiya?

  It really is the best models.

• 29:47Jason Calacanis?

  I think it is.

• 29:48Speaker 01

  And you should use 2.5 Pro.

• 29:50Chamath Palihapitiya?

  2.5 Pro. It's, it's a, you gotta pay, right?

• 29:53Speaker 01

  Uh, yeah, you get a few query, you get a few prompts for free, but, uh, you know, if you do it a bunch, you need to pay.

• 29:59Jason Calacanis?

  You're just gonna make all this free, right?

• 30:00Speaker 01

  It's like 20 bucks a month.

• 30:01Chamath Palihapitiya?

  Yeah, it's great.

• 30:01Jason Calacanis?

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

• 30:05Chamath Palihapitiya?

  Yeah, one step down in hardware cost, the whole thing will be fine.

• 30:08Speaker 01

  Okay, it's free today, without ads on the side. You just got a certain number of the top model. I think we're likely are going to have always now like sort of top models, though we can't supply infinitely to everyone right off the bat, but. You know, wait three months and then the next generation.

• 30:23Jason Calacanis?

  It 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.

• 30:32Speaker 01

  Oh, I'm all for, you know, really good AI advertising. I just, um, uh, I don't think we're gonna like necessarily our latest and greatest models, which are, you know, take a lot of computation. I don't think we're gonna 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, uh, the new free tier is usually as good as the previous pro tier, uh, and sometimes better.

• 31:03Jason Calacanis?

  All right, give it up for Sergey Brin.

• 31:04Speaker 01

  Thank you.

• 31:09Jason Calacanis?

  Okay, thanks, everybody, for watching that amazing interview with Sergey Brin. And thanks, Sergey, for joining us in Miami. If you want to come to our next event, it's the All In Summit in Los Angeles. Fourth year for All In Summit. Go to allin.com/events to apply. A very special thanks to our new partner, OKX, the new money app. OKX was the sponsor of the McLaren F1 team, which won the race in Miami. Thanks to Hyder and his team, an amazing partner and an amazing team. We really enjoyed spending time with you. And OKX launched their new crypto exchange here in the U.S. If you love All In, go check them out. And a special thanks to our friends at Circle. They're the team behind USDC, yes, your favorite stablecoin in the world.

• 31:52Jason Calacanis?

  USDC is a fully backed digital dollar redeemable one for one for USD. It's built for speed, safety, and scale. They just announced the Circle Payments Network. This is enterprise-grade infrastructure that bridges the gap between the digital economy and outdated financial rails. Go check out USDC for all your stablecoin needs. And special thanks to my friends, including Shane over at Polymarket, Google Cloud, Solana, and BVNK. We couldn't have done it without y'all. Thank you so much.

• 32:42Speaker 05

  What your winners want?

• 32:46Speaker 05

  Besties are gone. That's my dog taking a piss in your driveway.

• 32:56Chamath Palihapitiya?

  We should all just get a room and just have one big you, Georgie, because they're all just... It's like this sexual tension that we just need to release somehow.

• 33:04Speaker 05

  What your... B B. What your... What your B? B? What? We need to get merch. Besties are back.