谢尔盖·布林：AI 的下一跃与谷歌的自我重塑

David Friedberg (00:00:02): We've got a special guest who's gonna come join us. Jason Calacanis (00:00:04): This always happens. Another Oh, yes. Drinking bread, everybody. Oh, my god. Somebody told me you started submitting code, and it kind of freaked everybody out that daddy was home. Sergey Brin (00:00:17): All models tend to do better if you threaten them. Chamath Palihapitiya (00:00:19): If you Jason Calacanis (00:00:20): threaten them. Sergey Brin (00:00:20): Like with physical violence. Yes. Management is like the easiest thing to do with AI. Jason Calacanis (00:00:25): Absolutely. It must be a weird experience to meet the bureaucracy in a company that you Chamath Palihapitiya (00:00:29): didn't hire. But on the other side of it, 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. Jason Calacanis (00:00:37): That's a Chamath Palihapitiya (00:00:38): sign of a healthy culture, actually. Jason Calacanis (00:00:39): You're punching a clock, man. I hear the reports. You and I have talked about it. You're going to work every day. Sergey Brin (00:00:45): 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. Jason Calacanis (00:00:54): Yeah. Sergey Brin (00:00:55): And I was like, you know, this has been good. I wanna do something else. I wanna hang out in cafes, read physics books. Yeah. And then, like, a month later, I was like, 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 I ran into him at a party and he said, you know, look, what are you doing? This is like the greatest transformative moment in computer science ever. Completely. Like And Jason Calacanis (00:01:34): you're a computer scientist. Sergey Brin (00:01:35): I'm computer scientist. Speaker 5 (00:01:36): Forget that. Yeah. Jason Calacanis (00:01:37): The founder of Google, you were a PhD student for computer science. Sergey Brin (00:01:39): I haven't finished my PhD yet, but working on it. Jason Calacanis (00:01:42): Keep working. Sergey Brin (00:01:43): Yeah. Jason Calacanis (00:01:43): We'll get there. Sergey Brin (00:01:44): Technically on leave of absence. David Friedberg (00:01:45): Right. Sergey Brin (00:01:46): And, he 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, 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. Jason Calacanis (00:02:07): 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 thirty or forty 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 a 100 users and 10 employees to, Speaker 5 (00:02:32): right now you have over 2,000,000,000 people using I think six products Jason Calacanis (00:02:37): or five products have over 2,000,000,000. It's it's not it's not even worth counting because it's the majority of the people in the planet touch Google products. Sergey Brin (00:02:45): Describe the pace. 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? Jason Calacanis (00:03:01): The what's new page is great. Sergey Brin (00:03:02): Right? Like, you go to home page Jason Calacanis (00:03:03): Two or three new web pages David Friedberg (00:03:04): again. Yeah. Sergey Brin (00:03:05): It was like, in this last week Jason Calacanis (00:03:06): Yep. These were the new websites. Yes. And it was like such and such elementary school, such and such a fish tank. Sergey Brin (00:03:14): Yeah. And you were like, Michael Jason Calacanis (00:03:15): Jordan appreciation page. Yeah. Well, whatever it was, these were the three new sites on the Sergey Brin (00:03:19): whole Internet. So obviously, the web, you know, developed very rapidly from Quite rapidly. And that was a very exciting and then we've had 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 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, the like, if you went away somewhere for a month and you came back, you'd be like, woah. What happened? Jason Calacanis (00:03:56): Somebody told me you started submitting code, and it kinda freaked everybody out that daddy was home. Chamath Palihapitiya (00:04:05): Dad needed PR? What happened? Sergey Brin (00:04:07): 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 And I've I've tried to do that and touch different parts of the system so that, you know, I so that well, first of all, it's fun. And secondly, I know what I'm talking about. It's really feels privileged to be able to kinda go back to the company, not have any real executive responsibilities, but be able to actually go deep into every little pocket. Chamath Palihapitiya (00:04:53): 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? Sergey Brin (00:05:01): Yeah. I started, you know, like, sort of, I don't know, a couple years ago and maybe a year ago. I was really very close with the what we call pre training. Yeah. 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've 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 kinda 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, you know, another huge step up in general in AI. So, you know, we don't really know what the ceiling is. Jason Calacanis (00:05:58): 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. By the way, I got the fold after you and I were talking about it. Google, kick serious ass now. 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, three hundred. Maybe explain that jump and then what you think the jump after that is. Sergey Brin (00:06:42): 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 so I think of the superpower is when it can do things in a volume that I cannot. Jason Calacanis (00:07:05): Yes. Sergey Brin (00:07:05): 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. Jason Calacanis (00:07:35): 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 neo if I don't know anything about it. I said, how many deaths occurred per decade? And I said, 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. Chamath Palihapitiya (00:08:02): Which model did you use? Jason Calacanis (00:08:04): OpenAI's? No. I use Gemini. Gemini five. The Chamath Palihapitiya (00:08:07): fabulous version. Thank Jason Calacanis (00:08:07): you. The fabulous one. Fabulous. And it Speaker 5 (00:08:09): was like, let's go. I treat it like I get sassy with it Sergey Brin (00:08:13): Yeah. Jason Calacanis (00:08:13): And it kinda works for me. Sergey Brin (00:08:15): You know, it's a weird thing. It's like You drinking the wine? We don't circulate this too much but these generally exist. In the AI community. But not just our models, but all models tend to do better if you threaten them. Jason Calacanis (00:08:26): If you threaten them. Sergey Brin (00:08:26): Like with physical violence. Jason Calacanis (00:08:28): Yes. But, like, that's people feel weird about that, so we don't really talk about But Yeah. I was threatening with not being fabulous, and it responded to that as well. Sergey Brin (00:08:37): Yeah. Historically, you just say like, Oh, I'm going to kidnap you if you don't fuck Yeah. Actually Can Chamath Palihapitiya (00:08:43): I ask you Jason Calacanis (00:08:44): a more specific But it went through it? Okay. And it literally came up with a system where it said, I think we should include practice miles. So let's say there's a hundred 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. Woah, done in minutes. Sergey Brin (00:09:10): 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 AI. I don't really expect it to work. And then you're like, woah, that actually worked. Chamath Palihapitiya (00:09:21): So as you 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? Sergey Brin (00:09:41): Yeah. I mean, look, I 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 and then I look at, you know, okay, he's whatever. My son's gonna go on to, whatever, from sophomore to junior, and what is he gonna learn? And then I think in my mind, and I talked to him about this, well, what is the AI going to be in Chamath Palihapitiya (00:10:31): one more year? Exactly. Sergey Brin (00:10:32): Yeah. Yeah. And it's, like, not comparable. Right? Obviously, Chamath Palihapitiya (00:10:37): the areas where you would tell your son, look, don't or not not yet? Sergey Brin (00:10:42): 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 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. David Friedberg (00:11:09): What about specifically What about college? Do you think college should is gonna continue to exist as it is today? Sergey Brin (00:11:14): I mean, it seems like college was already undergoing this kind of revolution even before this sort of AI challenge of what 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 where there's sort of questions asked about colleges. Yeah. I think AI obviously puts that at the forefront. David Friedberg (00:11:39): As a parent, think a lot about, hey, so much of education in America and 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 Chamath Palihapitiya (00:11:55): 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. David Friedberg (00:12:20): Enjoy a few years of exploration. Jason Calacanis (00:12:21): Yeah. Yeah. Sergey, can I David Friedberg (00:12:24): ask you about hardware? Years ago, 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 pretty profound learning curves in hardware, and there's dozens of 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? Sergey Brin (00:12:57): 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. David Friedberg (00:13:33): Right. But have you seen anything lately that Yeah. Chamath Palihapitiya (00:13:36): Do and do you believe in the humanoid form factor robots or do you think that's a little overkill? Sergey Brin (00:13:40): 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 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 and I don't know that you need exactly the same number of arms and legs and wheels, is zero in the case of humans, as humans to make it all work in it. 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. Jason Calacanis (00:14:40): What about the path of being a programmer? That's where we're seeing with that finite dataset and listen, Google's got a twenty year old code base now, so it actually could be quite impactful. What are you seeing literally in the company? Are the 10x developers always this ideal that you get a couple of unicorns once in a while, but are we going to see 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 and make sure it's not too weird? Because it could get weird. Chamath Palihapitiya (00:15:13): If you vibe code, yeah. Sergey Brin (00:15:15): I'm embarrassed to say this. Okay. I like, recently, I just had a big tiff inside the company because we have 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. Jason Calacanis (00:15:29): You oh, you have to be pure. You can't Sergey Brin (00:15:31): I don't know. For, like, a bunch of really weird reasons that it would, like, boggled my mind that, you You Chamath Palihapitiya (00:15:37): couldn't vibe code on the Gemini code list. Sergey Brin (00:15:40): I mean, nobody would, like, enforce this rule, but but there was this, you know, actual internal webpage. For whatever reason, historical reason, somebody had put this and I had a big fight with them. I, you know, I cleared it up after a shocking Did you your boss? Period of time. Chamath Palihapitiya (00:15:56): You escalated to your boss. Sergey Brin (00:15:57): Oh, I definitely told Superb about it and I complained. Jason Calacanis (00:16:02): Sorry. I don't know if you remember, but you got super voting founders. Sergey Brin (00:16:05): You are the boss. Jason Calacanis (00:16:06): You can do what you want. It's your company still. Sergey Brin (00:16:09): No. No. He was very supportive. I more like I talked to him. I was like, I can't deal with these people. You need to deal with this. I'm beside myself that they're saying Jason Calacanis (00:16:21): It's weird that there's bureaucracy like in a company that you it must be a weird experience to meet the bureaucracy in a company that you didn't hire. Chamath Palihapitiya (00:16:28): But but on the other side of it, 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. Sergey Brin (00:16:38): I guess so. Anyway, it did get fixed and people are using Chamath Palihapitiya (00:16:42): So they got fired. David Friedberg (00:16:45): That person working at Google Siberia? No. Sergey Brin (00:16:49): We're to, you know, roll out every possible kind of AI and and trying external ones, you know, be whatever the cursors of the world, all all of those to just see what really makes people more productive. I mean, myself definitely makes me more productive because I'm not you you Chamath Palihapitiya (00:17:08): think the number of foundational models, like if you look three years forward, will they start to cleave off and gets 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, Sergei, a multiple of what they are today, the same, something in between? Sergey Brin (00:17:34): 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. 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 whatever RNNs for text and speech and stuff and, 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 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 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 know, you could you can get away with a somewhat smaller specialized model, a little bit faster, a little bit cheaper, but the trends have not gone that way. Chamath Palihapitiya (00:19:01): 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? We're still waiting on this OpenAI. Sergey Brin (00:19:16): Oh, yeah. Yeah. I Chamath Palihapitiya (00:19:17): we haven't seen it yet, but theoretically it's coming. Sergey Brin (00:19:20): I mean, I have to give credit to where credit's due. I mean, DeepSeek released a really surprisingly powerful model when it was January or so. So that that definitely closed the gap to proprietary models. We've pursued both. So we released Gemma, which are our open source or, you know, 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, I mean, the jury's out which way this is going to go. David Friedberg (00:19:59): 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 a AirPod talking? Or thinking or or like what's the, yeah, and then the answer is just spoken to you. Chamath Palihapitiya (00:20:26): 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 Right. That's a very big step in allowing the FDA to clear everybody getting an implant. Yeah. David Friedberg (00:20:38): And is it like, if if you could just summarize what you think is kind of the most commonplace human computer interaction model in the next decade or whatever. There's this idea of glasses with a screen and glasses and you tried that a long time ago. Sergey Brin (00:20:53): Yeah. I messed that up, I'll be honest. Got the timing totally wrong on that. Jason Calacanis (00:20:59): Early again. Sergey Brin (00:21:00): Yeah. Right. Right. But early. There are a bunch of things I wish I'd done differently, but honestly, it was just like the technology wasn't ready for for for Google Class. 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 though, you know, a lot of people are saying, hey, the singularity is, like David Friedberg (00:21:28): Right. Sergey Brin (00:21:28): Five years. Five years away. So your ability to see through that into the future Yeah. I mean, it's David Friedberg (00:21:36): very hard guess. 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. Can you comment on this? Do you think that this AGI Super Intelligence or really Silicon Intelligence exceeds human capacity and humans are a stepping stone in progression of evolution. Sergey Brin (00:22:02): Boy, I think sometimes us nerdy guys go and get have all too much wine I know what David Friedberg (00:22:07): I like. I've had two glasses. Sergey Brin (00:22:09): I'm ready to go. I I need just more Chamath Palihapitiya (00:22:12): Let's go. For this Sergey Brin (00:22:12): conversation. Sure. Jason Calacanis (00:22:15): Human implants. Let's go. Sergey Brin (00:22:17): I mean, guess we're starting to get experience with these AIs that can do certain things, you know, much better than us. 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, I'll look at it differently. Yeah. There's a Chamath Palihapitiya (00:22:48): moment of insecurity, maybe. Sergey Brin (00:22:50): I guess so. As an aside, management is like the easiest thing to do with AI. Yeah. Absolutely. And I did this, you know, at Gemini on some of our, you know, work chats, 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 gonna bring it back and bring it to everybody. But it 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 and then I would paste it back in so people didn't realize it was the AI. David Friedberg (00:23:27): That's awesome. Sergey Brin (00:23:28): I 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 I actually picked out this woman, this young woman engineer who like, you know, I didn't even notice she wasn't very vocal particularly in that group. Chamath Palihapitiya (00:23:49): But her PR's kicked ass. But Sergey Brin (00:23:51): no. No. There was, like and then, I don't know, something that the AI had detected. I went I talked to the manager actually, and and he was, yeah. You know what? You're right. Like, she's been working really hard, did all these things. Wow. I think that ended up happening actually. So I don't know. I guess after a while, just kind of take it for granted that you can just do these things. Jason Calacanis (00:24:12): I don't know. It hasn't really Chamath Palihapitiya (00:24:13): Do you think that there's a a use case for like an infinite context link? Sergey Brin (00:24:20): Oh, 100%. Mean, I David Friedberg (00:24:22): All of Google's code base codes Sergey Brin (00:24:23): in one Exactly. Sure, you should have access Chamath Palihapitiya (00:24:25): to Quasi infinite. Yeah. Stateful. Yeah. And then multiple sessions so that you can have like 19 of these things, 20 of these things running. David Friedberg (00:24:33): Or just evolve itself. In real time. Eventually it'll evolve itself. Sergey Brin (00:24:35): Yeah. Mean, I guess if it does everything, then you can have just one in theory. You just need to somehow Disambiguate it how it's what you're talking about. But, yeah, for sure, there's no limit to use of context and there, you know, there are a lot of ways to make it larger and larger. There's a Chamath Palihapitiya (00:24:53): there's a rumor that internally there's a Gemini build that is a quasi infinite context line. Is it is it a valuable thing? Like, I don't know. Well, you say what you wanna say, bud. Sergey Brin (00:25:04): 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 name it. Chamath Palihapitiya (00:25:24): 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 will 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 (00:25:40): Well, we mostly for for Gemini, we mostly use our own TPUs. Chamath Palihapitiya (00:25:44): TPU. Yeah. Sergey Brin (00:25:45): 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. 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 and the communication works and so forth are actually pretty big factors. And it actually yeah. May maybe one of these days, the AI itself will be good enough to reason through that. Today, it's not quite good enough. Jason Calacanis (00:26:32): 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 of bullet points. Nope. I want to focus on this. Definitely. It's so quick now. Last year was unusable. It was too slow and now it like stops. Okay. And then you sell it. I would like What I want to David Friedberg (00:27:00): go to. I don't want to type. Jason Calacanis (00:27:01): Well, 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 in his apartment saying zoom in, zoom in closer to the left to the right. 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? Sergey Brin (00:27:42): Everything is getting better and faster. Smaller models are more capable. There are better ways to do inference on them that are faster. Chamath Palihapitiya (00:27:51): You can also stack them. This is Nico's company, eleven Labs. It's an exceptional TTS, STT stack. There are other options. Whisper is really good at certain things, but this is where I kind of believe you're gonna 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 kick ass. I mean Yeah. Jason Calacanis (00:28:23): They're exceptional 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. Sergey Brin (00:28:40): 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 Jason Calacanis (00:28:48): the drive. The drive is Sergey Brin (00:28:49): incredible. Yeah. Don't feel like I could I mean, I would get its output in my headphones, but if I wanna speak to it, then everybody's listening to me. It's weird. Yeah. 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. Yep. But I feel like I honestly, maybe it's a good argument for a private office. I should spend more time than Jason Calacanis (00:29:13): you guys are. Could talk to your manager. Yeah. Sergey Brin (00:29:17): They might guess I like being out in No. Jason Calacanis (00:29:20): That's the kind Sergey Brin (00:29:20): of stoke. Like to get them hot. Everybody. Yeah. But I do think that there's this AI use case that I'm missing, Vishay should probably figure out how to try more Jason Calacanis (00:29:29): often. If people want to try your new product, is there a website they can visit or something or special code? Check out. 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. Chamath Palihapitiya (00:29:46): It really is the best models. I think it is. Those are the best. Sergey Brin (00:29:48): Yeah. And you should use 2.5 Pro. Jason Calacanis (00:29:50): 2.5 Pro. Pay the David Friedberg (00:29:51): It's it's a you gotta pay. Right? Sergey Brin (00:29:54): Yeah. You got a few query you got a few prompts for free, but, you know, if you do it a bunch, you need Chamath Palihapitiya (00:29:58): to pay. Jason Calacanis (00:29:58): Yeah. It's gonna make all David Friedberg (00:29:59): these Sergey Brin (00:30:00): free It's like $20 a month. Yeah. David Friedberg (00:30:01): It's great. Jason Calacanis (00:30:02): You got a vision for, like, making it free and throwing some ads on the side? Chamath Palihapitiya (00:30:05): Yeah. One step down and harder, costs the whole thing will be free. Sergey Brin (00:30:08): Well, 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 that we can't supply infinitely to everyone right off the bat. But, you know, wait three months and then the next generation Jason Calacanis (00:30:23): 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. I'm not Sergey Brin (00:30:33): you know, really good AI advertising. I just 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 the new free tier is usually as good as the previous pro tier and sometimes better. Jason Calacanis (00:31:03): Alright. Give it up for Sergei Brad. Sergey Brin (00:31:04): Thank you. Speaker 5 (00:31:09): Okay. Thanks everybody for watching that amazing interview with Sergei Brin, and thanks Sergei for joining us in Miami. If you wanna 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 f one team, which won the race in Miami. Thanks to Haider 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 US. If you love All In, Chamath Palihapitiya (00:31:44): go Speaker 5 (00:31:44): 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. 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 realms. Go check out USDC for all your stablecoin needs. And special thanks to my friends, including Shane over at Polymarket, Google Cloud, Solana, and EVNK. We couldn't have done it without Gov. Thank you so Sergey Brin (00:32:24): much. We'll let your winners ride. Jason Calacanis (00:32:56): We should all just get a room and just have one big huge orgy because they're all just useless. It's like this, like, sexual tension that they just need to release somehow.