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

Summary

Podcast Summary: All-In Live from Miami Featuring Sergey Brin, Google Co-Founder

Release Date: May 20, 2025

In this compelling episode of the All-In Podcast, industry veterans Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg engage in an insightful conversation with Sergey Brin, co-founder of Google. Filmed live in Miami, the discussion delves deep into advancements in artificial intelligence (AI), the future of education, robotics, and the evolving landscape of human-computer interaction. Below is a detailed summary capturing the key points, discussions, insights, and conclusions from the episode.

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1. Sergey Brin’s Return to Google and AI Enthusiasm

Sergey Brin’s Re-engagement with AI: Sergey Brin shares his renewed passion for AI, emphasizing its transformative potential in computer science. After retiring shortly before the COVID-19 pandemic, Brin found his desired tranquility disrupted and returned to active involvement at Google.

Sergey Brin [00:54]: "And I was like, you know, that's not really happening. So then I just started to go to the office... what are you doing? This is like the greatest transformative moment in computer science ever, completely."

Excitement Over AI Developments: Brin highlights the exponential growth and rapid pace of AI advancements, comparing them to the early days of the web but noting that AI evolves much faster.

Sergey Brin [03:56]: "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?"

2. AI’s Impact on Productivity and Human Roles

AI as a Productivity Tool: Brin discusses how AI can handle vast amounts of information and tasks far beyond human capacity, enhancing productivity significantly.

Sergey Brin [07:05]: "They suck down the top thousand results and then do follow-on searches... that's a week of work for me. I can't do that."

Integration of AI in Management: The conversation touches on using AI for managerial tasks, such as summarizing chats and assigning tasks, which can lead to recognizing employee contributions that might otherwise go unnoticed.

Sergey Brin [22:56]: "I was like... summarize this for me. Okay, now assign something for everyone to work on... it's awesome."

3. The Future of Education and College

AI’s Influence on Educational Choices: Brin reflects on how AI advancements are prompting questions about the value of traditional college education versus vocational training.

Sergey Brin [11:14]: "College was already undergoing this kind of revolution even before this sort of AI challenge... AI obviously puts that at the forefront."

Parental Perspectives on Education: Hosts discuss the shifting mindset among parents regarding college, with some questioning its traditional value in the AI era.

Jason Calacanis [11:39]: "Honestly, lately I'm like, I don't think they should go to college... it's just fundamentally, you know."

4. Robotics and Hardware Advances

Challenges in Robotics Development: Brin shares Google’s foray into robotics, including the acquisition and subsequent sale of companies like Boston Dynamics. He acknowledges the current limitations where software advancements outpace hardware capabilities.

Sergey Brin [13:33]: "We built the hardware... the robots are all cool and all, but the software wasn't quite there."

Skepticism About Humanoid Robots: Brin expresses skepticism about the necessity of humanoid form factors for robots, suggesting that alternative designs might be more efficient.

Sergey Brin [13:40]: "I don't think that you need exactly the same number of arms and legs and wheels... to make it all work."

5. Coding and AI-Assisted Development

AI Enhancing Developer Productivity: The discussion explores how AI tools like Gemini can elevate developer productivity, potentially reaching levels previously unattainable without assistance.

Chamath Palihapitiya [14:40]: "Are we going to see all developers... hit that level 8, 9, 10... or is it going to be all done by computers?"

Internal Challenges with AI Tools: Brin recounts an internal conflict at Google regarding the use of AI for coding, highlighting the organizational adjustments needed to integrate AI effectively.

Sergey Brin [15:38]: "But it did get fixed and people are using, so they got fired."

6. Human-Computer Interaction and Future Interfaces

Evolving Interaction Models: Brin and hosts discuss the transition from traditional search boxes to more intuitive interfaces like voice commands and potential future innovations such as brain-computer interfaces.

Jason Calacanis [19:59]: "What's the most commonplace human-computer interaction model in the next decade?"

Challenges with Voice Interfaces: They acknowledge current limitations with voice interfaces, such as social awkwardness in shared spaces and the need for faster, more accurate responses.

Sergey Brin [28:40]: "During work I can't really use voice mode too much. I usually use it on the drive."

7. Open Source vs. Closed Source in AI Development

Balancing Open and Closed Models: Brin discusses Google’s approach to AI models, balancing the release of open-source models like Gemini with proprietary advancements to maintain competitive edge.

Sergey Brin [19:20]: "We've released Gemini, which are our open source or open weight models... but the jury's out which way this is going to go."

Impact of Open-Source Models: He acknowledges the strides made by open-source models, noting that while they are growing in capability, proprietary models still hold significant advantages.

Sergey Brin [19:20]: "They perform really well. They're small, dense models... but they're not as powerful as Gemini."

8. Management and AI in the Workplace

AI-Assisted Management Decisions: Brin illustrates how AI can aid in managerial tasks, such as identifying and promoting talented employees based on performance analytics.

Sergey Brin [23:48]: "It literally came up with a system... estimated deaths per mile... like somebody's term paper for undergrad... done in minutes."

Cultural Shifts Due to AI: The conversation highlights how AI is reshaping workplace dynamics, promoting a healthier culture by enabling more objective decision-making.

David Sacks [16:28]: "That's a sign of a healthy culture, actually."

9. The Path Forward for AI and Human Roles

AI as a Collaborative Tool: Brin emphasizes viewing AI as a tool that complements human abilities, enhancing what individuals can achieve rather than replacing them.

Sergey Brin [22:17]: "I use it as a tool, so I feel like I've gotten used to it."

Future Prospects and Ethical Considerations: The hosts and Brin ponder the ethical implications and future trajectory of AI, including concerns about superintelligence and the role of humans in an AI-dominated landscape.

Jason Calacanis [21:39]: "Larry made years ago that humans were a stepping stone in evolution... Do you think that this AGI superintelligence exceeds human capacity?"

Sergey Brin [22:15]: "We've had these experiences where you suddenly decide, okay, I'll just throw this AI at it... it's like drinking the wine."

10. Conclusion and Final Thoughts

The episode wraps up with Brin encouraging listeners to engage with Google’s Gemini AI through dedicated apps, hinting at future accessibility and integration.

Sergey Brin [29:29]: "You should use 2.5 Pro."

The hosts express enthusiasm for ongoing AI developments and their potential to revolutionize various aspects of technology and daily life.

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Notable Quotes:

• Sergey Brin [00:54]: "It's been some of the most fun I've had in my life, honestly."
• Sergey Brin [03:56]: "These AI systems actually change quite a lot. Quite a lot."
• Sergey Brin [07:05]: "If it sucks down the top thousand results and then does follow-on searches... that's a week of work for me. I can't do that."
• Sergey Brin [13:40]: "I don't think that you need exactly the same number of arms and legs and wheels... to make it all work."
• Sergey Brin [19:20]: "We've released Gemini, which are our open source or open weight models... but the jury's out which way this is going to go."
• Sergey Brin [22:17]: "I use it as a tool, so I feel like I've gotten used to it."
• Sergey Brin [29:29]: "You should use 2.5 Pro."

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This episode offers a profound glimpse into Sergey Brin's perspective on the rapid advancements in AI, their implications for productivity, education, and the future of human-computer interaction. His insights, combined with the hosts' probing questions, provide listeners with a comprehensive understanding of where AI stands today and where it might be headed in the near future.

Transcript

[Jason Calacanis]

We've got a special guest who's gonna come join us.

[Chamath Palihapitiya]

This always happens.

[Jason Calacanis]

Another Sergey Brin, everybody.

[Chamath Palihapitiya]

Oh, my God. Somebody told me you started submitting code and it kind of freaked everybody out that Daddy was hung.

[Sergey Brin]

All models tend to do better if you threaten them. If you threaten them, like, with physical violence, yes. Management is, like, the easiest thing to do with AI.

[Chamath Palihapitiya]

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

[David Sacks]

But on the other side of it, I would say it's pretty amaz 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.

[Chamath Palihapitiya]

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]

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.

[Chamath Palihapitiya]

Yeah.

[Sergey Brin]

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, 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, completely.

[Chamath Palihapitiya]

And you're a computer scientist.

[Sergey Brin]

I'm a computer scientist.

[Chamath Palihapitiya]

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

[Sergey Brin]

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

[Chamath Palihapitiya]

Keep working, we'll get there.

[Sergey Brin]

Technically on leave of absence.

[Chamath Palihapitiya]

Right.

[Sergey Brin]

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. Well, you guys all obviously follow all the AI technology, but being a computer scientist, it is the most exciting thing of my life, just technologically.

[Chamath Palihapitiya]

And the exponential nature of this, the pace of 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 had a company, Google, that grew from 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's new page. The what's new page is great, right? Like, you go to two or three.

[Chamath Palihapitiya]

New web pages again.

[Sergey Brin]

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

[Bill Ackman]

Yes.

[Sergey Brin]

And it was like such and such elementary school, such and such a fish tank. And you were like Michael Jordan appreciation page. 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, the, like, if you went away somewhere for a month and you came back, you'd be like, whoa, what happened?

[Chamath Palihapitiya]

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

[David Sacks]

Okay, Daddy did a pr. What happened?

[Sergey Brin]

The code I submitted wasn't very exciting. I think I needed to add myself to get access to some things and a minor CL here or there. Nothing that's going to win any awards, but you need to do that to do basic things, run basic experiments and things like that. 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 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.

[David Sacks]

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 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 that's sort of the big super. You throw huge amounts of computers at it. And I learned a lot 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 ceiling is.

[Chamath Palihapitiya]

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 by the way, I got the fold after you and I were talking about it. Ok. Google kicks Siri's ass now. Like it actually does what you ask it to do. When you ask it to open it 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.

[Jason Calacanis]

Yes, right.

[Sergey Brin]

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

[Chamath Palihapitiya]

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.

[David Sacks]

Which model did you use?

[Chamath Palihapitiya]

No, I use Gemini.

[David Sacks]

Gemini. Fabulous version.

[Chamath Palihapitiya]

The fabulous one.

[Jason Calacanis]

Fabulous.

[Chamath Palihapitiya]

And it was like, let's go I treat it like I get sassy with it.

[Sergey Brin]

Yeah.

[Chamath Palihapitiya]

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 too much in 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. Yes, but like, that's. People feel weird about that, so we don't really talk about that.

[Chamath Palihapitiya]

But yeah, I was threatening 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.

[David Sacks]

Can I ask you a more?

[Chamath Palihapitiya]

Hold on. 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. 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. The AI. I don't really expect it to work. And then you're like, whoa, that actually worked.

[David Sacks]

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?

[Sergey Brin]

Yeah, I mean, look, I don't really know how to think about it, to be perfectly honest. I don't have a magical way. I mean, I see I have a kid in high school and middle school, and I mean, the AIs are basically already ahead. Obviously, there's some things AIs are particularly dumb at, and they make certain mistakes human would never make. But generally, if you talk about math or calculus or whatever, they're pretty damn good. They can win math contests and coding contests, things like that, against 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 one year?

[David Sacks]

Exactly.

[Sergey Brin]

Yeah, yeah. And it's. Are there areas where you comparable. Right. Obviously.

[David Sacks]

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 plan your life or 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 useful in the world. I don't know. I guess I think my kids should do what they like. Hopefully it's somewhat challenging and they can overcome different kinds of problems and things like that.

[Chamath Palihapitiya]

What about, specifically, what about college?

[Jason Calacanis]

Do you think college should. Is going to continue to exist as it is today?

[Sergey Brin]

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 AI obviously puts that at the forefront.

[Jason Calacanis]

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, you know, my.

[David Sacks]

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 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, enjoy a few years of exploration.

[Jason Calacanis]

Yeah, Sergey, can I ask you about hardware? 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 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]

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

[Jason Calacanis]

Right, but have you seen anything lately? Yeah.

[David Sacks]

Do you believe in 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 at least acquired, at least to humanoid robotic startups and later sold them. 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 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. 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.

[Chamath Palihapitiya]

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 it actually could be quite impactful.

[Bill Ackman]

What are you seeing?

[Chamath Palihapitiya]

Like literally in the company, the 10x developer is always this ideal that you get a couple of unicorns once in a while. But are we going to see all developers, their productivity hit that level 8, 9, 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 if you vibe code.

[David Sacks]

Yeah.

[Sergey Brin]

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.

[Chamath Palihapitiya]

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

[Sergey Brin]

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

[David Sacks]

Vibe code on the Gemini code.

[Sergey Brin]

I mean nobody would like enforce this rule. 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 him and I You know, I cleared it up. After a shocking period of time, you.

[David Sacks]

Escalated to your boss.

[Sergey Brin]

Oh, I definitely told Subduer about it. And I played Sergey.

[Chamath Palihapitiya]

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

[David Sacks]

You are the boss.

[Chamath Palihapitiya]

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

[Sergey Brin]

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 it's weird that there's bureaucracy.

[Chamath Palihapitiya]

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

[David Sacks]

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.

[Sergey Brin]

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

[Chamath Palihapitiya]

That person's working in Google Siberia.

[Sergey Brin]

No, we're trying to roll out every possible kind of AI and trying external ones, 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.

[David Sacks]

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?

[Chamath Palihapitiya]

Maybe.

[David Sacks]

There's a very specific model for chip design. There's clearly a very specific model for biologic precursor design. Protein folding. 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 kind of, I mean, look, I don't know. 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 and whatever convolutional networks for vision things and 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 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 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.

[David Sacks]

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

[Sergey Brin]

I mean, 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 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 I mean, the jury's out which way this is going to go.

[Jason Calacanis]

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 or thinking. Or thinking.

[Sergey Brin]

Or like, what's the. Yeah.

[Jason Calacanis]

And then the answer is just spoken to you.

[David Sacks]

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.

[Sergey Brin]

Yeah.

[Jason Calacanis]

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 and glasses and you tried that a long time ago.

[Sergey Brin]

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

[Chamath Palihapitiya]

Early again.

[Sergey Brin]

Yeah, right, right.

[Jason Calacanis]

But early.

[Sergey Brin]

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 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 years though, you know, a lot of people are Saying, hey, the singularity is like five years away. So your ability to see through that into the future.

[Jason Calacanis]

Yeah, I mean, it's very hard to. Sorry, just let me ask about this.

[David Sacks]

Do you.

[Jason Calacanis]

There was a comment that Larry made years ago that humans were a stepping stone in evolution. Okay, can you comment on this? 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 sometimes us nerdy guys go and have a little too much wine. I've had two glasses.

[Jason Calacanis]

I'm ready to go.

[Sergey Brin]

More for this conversation.

[Chamath Palihapitiya]

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, 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 maybe if they get even more capable in the future, I'll look at it differently.

[David Sacks]

Yeah, there's a moment of insecurity, maybe.

[Sergey Brin]

I guess. So, as an aside, management is, like, the easiest thing to do with the AI.

[Chamath Palihapitiya]

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, 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 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. That's awesome. 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 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.

[David Sacks]

But her PRs kicked ass.

[Sergey Brin]

No, no, there 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. She's been working really hard, did all these things.

[David Sacks]

Wow.

[Sergey Brin]

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. I don't know, it hasn't really.

[David Sacks]

Do you think that there's a use case for like an infinite context link?

[Sergey Brin]

Oh, 100%. I mean, all of Google's code base goes quasi infinite, but sure, you should have access to quasi infinite. Yeah.

[David Sacks]

Stateful.

[Sergey Brin]

Yeah.

[David Sacks]

And then multiple sessions so that you can have like 19 of these things, 20 of these things running.

[Jason Calacanis]

Or it just evolves itself in real time. Eventually it'll evolve itself.

[Sergey Brin]

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.

[David Sacks]

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

[Sergey Brin]

Say, but I mean, for any such cool new idea in AI, there are probably five such things internally and 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.

[David Sacks]

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, 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 or for worse, not that abstract. And maybe someday the AI will abstract it for us. But 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, maybe one of these days the AI itself will be good enough to reason through that. Today it's not quite good enough.

[Chamath Palihapitiya]

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. Nope. I want to focus on this.

[Jason Calacanis]

Definitely.

[Chamath Palihapitiya]

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 go to.

[Sergey Brin]

I don't want to type.

[Jason Calacanis]

I want to use voice.

[Chamath Palihapitiya]

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. 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?

[Sergey Brin]

Everything is getting better and faster and so forth. You know, smaller models are more capable. There are better ways to do inference on them that are faster.

[David Sacks]

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 kind of believe you're going to get this 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 pretty good because they're so good. Whisper N11, for those speech examples that you're talking about are kick ass. I mean, they're exceptional.

[Chamath Palihapitiya]

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'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.

[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.

[Chamath Palihapitiya]

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.

[Chamath Palihapitiya]

It's weird.

[Sergey Brin]

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 like you guys are.

[Chamath Palihapitiya]

You could talk to your manager. They might guess one.

[Sergey Brin]

I like being out in the dope with everybody, but I do think that there's this AI use case that I'm missing that she should probably figure out how to try more often.

[Chamath Palihapitiya]

If people want to try your new product, is there a website they can visit or something or special code or 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.

[David Sacks]

It really is the best models.

[Chamath Palihapitiya]

I think it is the best models.

[Sergey Brin]

And you should use 2.5 Pro.

[Jason Calacanis]

2.5 Pro. You got to pay, right?

[Sergey Brin]

Yeah, you get a few queries, you get a few prompts for free. But if you do it a bunch.

[Chamath Palihapitiya]

You need, you're just gonna make all these.

[Sergey Brin]

It's like 20 bucks a month.

[Jason Calacanis]

Yeah, it's fine.

[Chamath Palihapitiya]

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

[Sergey Brin]

Yeah.

[David Sacks]

One step down in hardware costs, the whole thing will be free.

[Sergey Brin]

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.

[Chamath Palihapitiya]

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, I'm all for, 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.

[Chamath Palihapitiya]

All right, give it up for Sergey Brin.

[Sergey Brin]

Thank you.

[Bill Ackman]

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 all in.comevents 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 us. 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. 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 reality. 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.

[Sergey Brin]

We'll let your winners ride.

[Chamath Palihapitiya]

Rain Man David Sack.

[Jason Calacanis]

And it said.

[Sergey Brin]

We open source it to the fans and they've just gone crazy with it.

[Chamath Palihapitiya]

Love you Queen of k.

[Bill Ackman]

Besties are gone.

[Sergey Brin]

13 that is my dog taking a notice in your driveway. Oh man my will meet me at.

[David Sacks]

We should all just get a room and just have one big huge orgy cuz they're all just useless. It's like this like sexual tension but they just need to release somehow.

[Chamath Palihapitiya]

We need to get merch.