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Sergey Brin, Google Co-Founder | All-In Live from Miami Podcast Episode Description
(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!
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Sergey Brin, Google Co-Founder | All-In Live from Miami Podcast Episode Top Keywords
Sergey Brin, Google Co-Founder | All-In Live from Miami Podcast Episode Summary
In this podcast episode, the discussion revolves around the rapid advancements and applications of AI, particularly in the context of companies like Gemini and Google. A significant portion of the conversation highlights the use of AI tools in workplace communication, such as summarizing chat spaces and assigning tasks, which were initially met with skepticism but proved to be effective. The speakers also delve into the evolution of AI models, noting the shift towards more unified models like transformers, which have replaced older, more specialized models.
Sergey Brin, a notable guest, shares insights on the transformative impact of AI in computer science and its potential future applications. He discusses the integration of AI in various fields, including robotics, where software advancements are crucial for making hardware truly useful. The conversation touches on the potential for AI to handle complex tasks, such as deep research and data analysis, which would be time-consuming for humans.
The episode also explores the future of human-computer interaction, suggesting a shift towards more intuitive interfaces, such as voice commands and possibly augmented reality devices. The speakers express excitement about the pace of AI development, comparing it to past technological advancements and emphasizing its exponential growth.
Actionable insights include the importance of staying updated with AI developments, as they rapidly change and impact various industries. The discussion also suggests that individuals should focus on areas they are passionate about, as AI continues to evolve and integrate into different sectors.
Overall, the episode conveys a message of optimism and curiosity about the future of AI, encouraging listeners to embrace its potential while considering its implications on education, work, and daily life.
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Sergey Brin, Google Co-Founder | All-In Live from Miami Podcast Episode Transcript (Unedited)
Speaker: 0
00:02
We’ve got a special guest who’s gonna come join us.
Speaker: 1
00:04
This always happens. Another Oh, yes. Sergey Bryden, everybody. Oh, my God. Somebody told me you, started submitting bryden, and it ai of freaked everybody out that daddy was hungry.
Speaker: 2
00:17
All models tend to do better if you threaten them.
Speaker: 3
00:19
If you
Speaker: 1
00:19
threaten them.
Speaker: 2
00:20
Like with physical violence.
Speaker: 1
00:21
Yes.
Speaker: 2
00:22
Management is ai the easiest thing to do with AI.
Speaker: 1
00:25
Absolutely. Must be a weird experience to meet the bureaucracy in a company that you didn’t hire.
Speaker: 3
00:29
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.
Speaker: 2
00:36
No, but
Speaker: 3
00:37
I’m serious. That’s a sign of a healthy culture, actually.
Speaker: 1
00:40
You’re punching a clock, man. I hear the reports. You and I have talked about it. You’re going to work every day.
Speaker: 2
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.
Speaker: 1
00:54
Yeah. And I
Speaker: 2
00:55
was like, you know, this has been good. I sana do something else. I wanna hang out in cafes, read physics books. Yeah. And then ai sai month later I was like, that’s not really happening. Mhmm. So then I just started to go to the office, you know, once we
Speaker: 1
01:10
could go to the office.
Speaker: 2
01:12
And actually, to be perfectly honest, there was a guy, ram, Ai, this guy named Dan. Uh-huh. And I I ran into a middle party, and he said, you know, look, what are you doing? This is, like, the greatest transformative moment in computer science ever. Mhmm. Completely. Like And you’re a computer sai. I’m a computer scientist.
Speaker: 3
01:36
Forget that. Yeah. Yeah.
Speaker: 2
01:36
Yeah. You’re the founder of Google, but you’re a
Speaker: 1
01:38
PhD student for computer science.
Speaker: 2
01:39
I haven’t finished my PhD yet, but working on it.
Speaker: 1
01:42
Keep working. Yeah. We’ll get there.
Speaker: 2
01:44
Technically, on leave of absence.
Speaker: 0
01:45
Right.
Speaker: 2
01:46
And, he he told me this, and I’d already started kinda going into the office a little bit. And I was like, you know, he’s right. Mhmm. And, it has been, just, incredible. Just, ai, you
Speaker: 1
01:56
guys all obviously follow all the Ai technology.
Speaker: 2
02:00
But being a computer scientist, it is, you know, the most exciting thing, you know, of my life, just technologically.
Speaker: 1
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 you had a company, Google, that grew from, you know, a hundred users and 10 employees to
Speaker: 4
02:32
ai now you have over 2,000,000,000 people using, I think, six products
Speaker: 1
02:37
or five products that 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 on the planet touch Google products.
Speaker: 4
02:45
Describe the pace. Yeah.
Speaker: 2
02:46
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?
Speaker: 1
03:01
The what’s new page screen.
Speaker: 2
03:02
Right? Like you go to all the new
Speaker: 0
03:03
Two or
Speaker: 1
03:03
three new web pages. Yeah.
Speaker: 2
03:04
It was like this last week
Speaker: 3
03:06
Yep.
Speaker: 2
03:06
These were the new websites. Yes. And it was ai such and such elementary school, such and such a fish tank
Speaker: 1
03:13
Yeah.
Speaker: 2
03:14
And you were ai, wow.
Speaker: 1
03:15
Michael Jordan appreciation page.
Speaker: 2
03:16
Yeah. Whatever it was, these were the three new sites on the whole Internet. So, obviously, the web, you know, developed very rapidly from ai. Rapidly. Yeah. And that was a very, ai, 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 speak, but didn’t technically change so much, from, you know, month to month, year to year.
Speaker: 2
03:44
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?
Speaker: 1
03:56
Somebody told me you, started submitting code, and it ai freaked everybody out that daddy was home.
Speaker: 3
04:05
Okay. Ai did a PR? What happened?
Speaker: 2
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, ai 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.
Speaker: 2
04:28
And I’ve I’ve tried to do that and touch different parts of the system so that, you know, Ai 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.
Speaker: 3
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?
Speaker: 2
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 the what we call pretraining. Yeah. Actually, most of what people think of as AI training, whatever people call it, pretraining for various historical reasons.
Speaker: 2
05:21
But that’s sort of the big super you know, you throw huge amounts of computers at it. And, Ai I’ve 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 sai I could say I did it.
Speaker: 2
05:42
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.
Speaker: 1
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.
Speaker: 1
06:19
Ai, like, by the way, I got the, fold after you and I, were talking about it. Okay, Google kicks Siri’s ass now. Like, it actually does what you ask it to do. When you ask it to open up, it does stuff. But the number of threads, the number of queries, the number of follow ups that it’s doing in that deep research is 200, three hundred.
Speaker: 1
06:38
Maybe explain that jump and then what you think the jump after that is.
Speaker: 2
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.
Speaker: 3
07:05
Yes.
Speaker: 2
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.
Speaker: 2
07:22
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.
Speaker: 4
07:35
This is the thing Ai
Speaker: 1
07:35
think people have not fully appreciated who are not using the deep research projects. Before we had our f one, 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 sana get to deaths per mile driven. And at first, it was like, that’s gonna be really hard.
Speaker: 1
07:51
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.
Speaker: 3
08:02
Which model did you use it? Open eyes?
Speaker: 1
08:05
No. Ai I I used Gemini. Gemini five. The fabulous version. Fabulous. And it was like, let’s go. But I I treat it like I get sassy with it Yeah. And it ai works for me.
Speaker: 2
08:15
You know, it’s a weird thing. It’s like Is he drinking the wine? We don’t circulate this too much Ai. In the AI community. But not just our models, but all models tend to do better if you threaten them.
Speaker: 1
08:26
If you
Speaker: 2
08:26
threaten ai? Like with physical violence.
Speaker: 1
08:28
Yes.
Speaker: 2
08:28
But, like, that’s people feel weird about that, so we don’t really talk about that. But
Speaker: 1
08:32
Yeah. I was threatening with not being fabulous, and it responded to that as well.
Speaker: 2
08:37
Yeah. That’s historically, you just say, like, oh, I’m going to kidnap you if you don’t Yeah. Yeah. They actually Can I ask you a more
Speaker: 1
08:44
But it hold on? 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 ram, and then it literally gave me the deaths per mile estimated.
Speaker: 1
08:59
And then I started cross referencing and I was like, oh meh god, this is like somebody’s term paper for undergrad. You know, ai, woah. Done. In in minutes.
Speaker: 2
09:09
It’s shah, 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.
Speaker: 3
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?
Speaker: 2
09:41
Yeah. I mean, I 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 Sai ai a 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, human would never make, but generally, you know, if you talk about, like, math or calculus or whatever, like, they’re pretty damn good.
Speaker: 2
10:10
Like, they, you know, can win, like, math contests sana 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 ai year? Exactly. Yeah. Yeah.
Speaker: 2
10:33
And it’s, like, not comparable. Right? Obviously
Speaker: 0
10:37
Are there
Speaker: 3
10:37
areas where you would tell your son, look. Don’t or not not yet? I don’t
Speaker: 2
10:42
know if you can, like, plan your life around this. I mean, I didn’t particularly plan my life to, like, I don’t know, be an entrepreneur or whatever. I was just liked math and computer science. I guess maybe I got lucky, and it worked out to be, you know, useful in the world. I don’t know.
Speaker: 2
10:59
I guess Sai I Ai 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.
Speaker: 0
11:09
What about specifically What about college? Do you think college is sana continue to exist as it is today?
Speaker: 2
11:14
I mean, it seems like college was already undergoing this kind of, revolution even before this sort of AI challenge of people are ai, 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.
Speaker: 2
11:35
I think, you know, AI obviously puts at the forefront.
Speaker: 0
11:39
As a parent, I think a lot about, hey. So much of education in Meh, 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
Speaker: 3
11:53
to college. Like, it’s just fundamentally You know, my son is a rising junior, and his entire focus is he wants to go to an Speak 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?
Speaker: 3
12:11
And now Ai like, that’s actually the best thing you could do. Be socially well adjusted, psychologically deal with different kinds of failures. Enjoy a
Speaker: 0
12:20
few years of exploration. Yeah.
Speaker: 1
12:23
Yeah.
Speaker: 0
12:23
Yeah. Sergei, can I ask you about hardware? You know, years ago, Google owned Boston Dynamics, maybe a little bit ahead of its ai. 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.
Speaker: 0
12:45
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?
Speaker: 2
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 sai this more recently, we built out, everyday robotics internally and then later had to transition that.
Speaker: 2
13:16
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. Right.
Speaker: 0
13:34
But have you seen anything lately that you Do you and
Speaker: 3
13:36
do you believe in the humanoid form factor robots,
Speaker: 2
13:38
or do
Speaker: 3
13:38
you think that’s a little overkill?
Speaker: 2
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, robotics 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.
Speaker: 2
14:07
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, which is zero in the case of humans, as humans to make it all work.
Speaker: 2
14:29
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.
Speaker: 1
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, sai, like, it actually could be quite impactful. What are you seeing, like, literally in the company? You know, are the 10 x developers always this, like, ideal that you can, you know, you get a couple unicorns once in a while.
Speaker: 1
14:58
But are we sana see, like, all developers? Like, you know, their productivity hit that level, eight, nine, 10, and they’re just gonna or is it gonna be all done by computers, and we’re just gonna check it and make sure it’s not too weird? Because it could get weird.
Speaker: 3
15:13
If you vibe code, yeah.
Speaker: 2
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.
Speaker: 1
15:29
You oh, you have to be pure. You can’t
Speaker: 2
15:31
But Ai don’t know. For, like, a bunch of really weird reasons that it would, like, boggled my mind that,
Speaker: 3
15:37
you know You couldn’t ai code on the Gemini code.
Speaker: 2
15:39
Sai mean, nobody would, like, enforce this rule, but, but there was this, you know, actual internal meh page. For whatever historical reason, somebody had put this, and I had a big fight with them. Ai, you know, I cleared it up after a shocking Did
Speaker: 1
15:54
you tell
Speaker: 3
15:54
your boss?
Speaker: 2
15:55
Period of time.
Speaker: 3
15:56
You escalated to your boss.
Speaker: 2
15:57
Oh, I I definitely told
Speaker: 1
15:59
Superburb about
Speaker: 2
16:00
it and Ai couldn’t play and he Sorry.
Speaker: 1
16:02
Ai don’t know if you remember, but you got Supervoting founders. You are the boss. You can do what you want. It’s your company still.
Speaker: 2
16:09
No. No. It was, he was very supportive. It was more ai, I was ai I talked to him. I was like, I can’t deal with these people. You need to deal with this. Like, I just, ai, I’m beside myself so that they’re, like, saying
Speaker: 1
16:21
It’s weird that there’s bureaucracy, like,
Speaker: 3
16:23
in a company that you
Speaker: 1
16:24
find it must be a weird experience to meet the bureaucracy in a company that you didn’t hire.
Speaker: 3
16:27
No. 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.
Speaker: 0
16:36
But I’m serious.
Speaker: 3
16:36
That’s a sign of a healthy culture, actually.
Speaker: 2
16:38
I guess so. Anyway, it did get fixed, and, people are using it.
Speaker: 3
16:42
So they got fired.
Speaker: 1
16:45
That person’s working in Google Siberia. No.
Speaker: 2
16:49
We’re trying to, you know, roll out every possible kind of Sai, 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, for myself, definitely makes me more productive because I’m not coding.
Speaker: 3
17:08
The number of foundational models like, if you look three years forward, will they start to cleave off and meh 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?
Speaker: 3
17:26
Like, do is the number of foundational models in the future, Sergei, a multiple of what they are today, the same, something in between?
Speaker: 2
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.
Speaker: 2
18:08
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.
Speaker: 2
18:29
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.
Speaker: 2
18:52
You know, you could you can get away with a somewhat smaller ai model, a little bit faster, a little bit cheaper, but the trends have not gone that way.
Speaker: 3
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, you know, OpenAI. Oh, yeah. Yeah. Yeah. We haven’t seen it yet, but theoretically, it’s coming.
Speaker: 2
19:20
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 that definitely closed the gap to proprietary models. We’ve pursued both, so we released JEMMA, which are our open source or, you know, open weight models, and, those perform really well.
Speaker: 2
19:47
They’re small, dense models, so they fit well on one computer. And, they’re not as powerful as Ai. But, I mean, the jury’s out which way this is gonna go.
Speaker: 0
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, at the 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
Speaker: 3
20:22
Or thinking.
Speaker: 0
20:22
Or thinking. Or, like, what’s
Speaker: 2
20:23
the what’s the yeah.
Speaker: 0
20:24
And then the answer has just spoken you.
Speaker: 3
20:26
I mean, by by the way, just to build on this, it was Friday. Right? Neuralink got breakthrough designation for their human brain interface. I ai, that’s a very big step in allowing the FDA to clear everybody getting an implant.
Speaker: 1
20:38
Yeah. And is
Speaker: 0
20:39
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. Is it a you know, there’s this idea of glasses with a screen in the glasses, and you ai that a long time ago.
Speaker: 2
20:53
Yeah. Ai kind of messed that up, I’ll be honest. Got the timing totally wrong on that.
Speaker: 1
20:59
Early again.
Speaker: 2
21:00
Yeah. Right. Right. But early. There are a bunch of things I wish I’d done differently, but, honestly, it was just ai the technology wasn’t ready 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.
Speaker: 2
21:22
Ai mean, when you say ten 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, Ai mean, it’s
Speaker: 0
21:36
very hard to get. 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? 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?
Speaker: 2
22:02
Boy, I think, like, sometimes us nerdy guys go and get a little too much ai, chittering chest. I know what I’m like.
Speaker: 0
22:07
I’ve had two glasses.
Speaker: 2
22:08
And, I’m ready to go. Ai need to
Speaker: 1
22:11
snore Let’s go for that.
Speaker: 2
22:12
For this conversation. Sure.
Speaker: 1
22:15
Human implants. Let’s go.
Speaker: 2
22:17
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?
Speaker: 2
22:33
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.
Speaker: 3
22:48
Yeah. There’s no moment of insecurity, maybe.
Speaker: 2
22:50
I guess so. As an aside, management is, like, the easiest thing to do with AI.
Speaker: 0
22:54
Yeah. Absolutely.
Speaker: 2
22:56
And I did this, you know, at Gemini on 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.
Speaker: 2
23:17
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. That’s awesome.
Speaker: 2
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? Ai I actually picked out this woman, this young woman engineer who, ai, you know, I didn’t even notice she wasn’t very vatsal, particularly in that When her
Speaker: 3
23:49
PRs kicked ass?
Speaker: 2
23:50
But no. No. There was ai and then, 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, she’s been working really hard, did all these things. Wow. I think that ended up happening, actually. So Ai don’t know.
Speaker: 2
24:08
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
Speaker: 3
24:13
Do you think that there’s a a use case for, like, an infinite context length?
Speaker: 2
24:20
Oh, %. I mean All
Speaker: 0
24:22
of Google’s code base code is
Speaker: 2
24:23
infinite. Yeah. But sure, you should have access
Speaker: 3
24:25
to infinite. Yeah. Stateful. Yeah. And then multiple sessions so that you can have, like, 19 of these things, 20 of these things running out.
Speaker: 0
24:33
Or it just evolves itself. Eventually, it’ll evolve itself.
Speaker: 2
24:35
Yeah. I mean, I guess if it knows everything, then you can have just one in theory. You just need to somehow
Speaker: 3
24:41
tell it
Speaker: 2
24:42
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.
Speaker: 3
24:53
There’s a there’s a rumor that internally there’s a Gemini build that is a quasi infinite contact ai. Is it is it a valuable thing? Like, I don’t know. Well, you say what you wanna say, but
Speaker: 2
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 know, you name it.
Speaker: 3
25:24
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.
Speaker: 2
25:40
Well, we mostly for for Gemini, we mostly use our own TPUs. TPU. Yeah. 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.
Speaker: 2
26:10
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 sana the communication works and so forth, are actually pretty big factors.
Speaker: 2
26:24
And it actually yeah. May maybe one of these days, the AI itself will will be good enough to reason through that. Today, it’s not quite good enough.
Speaker: 1
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 shorter bullet points. Nope. I sana focus on this. Definitely. It’s so quick now.
Speaker: 1
26:52
Last year, it was unusable. It was too saloni. And now it, like, stops. Okay. And then you so Ai would like to
Speaker: 0
26:59
add a voice. Ai sana go to. I don’t wanna type. I wanna use voice.
Speaker: 1
27:02
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 ai.
Speaker: 1
27:26
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 ai a response time thing where it actually is worth doing voice and where it wasn’t previously?
Speaker: 2
27:42
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.
Speaker: 3
27:50
You can also stack them. Like, you know, this is like Niko’s company, Eleven Labs. It’s an exceptional TTS Sai stack. Like, there’s I mean, there are other options. Whisper is really good at certain things, but though this is where I I kind of believe you’re sana get this, like, compartmentalization where there’ll be certain foundational models for certain specific things, you stack them together, you kinda deal with the latency, and it’s, like, pretty good because they’re so good.
Speaker: 3
28:16
Like, Whisper and 11, for those speech examples that you’re talking about, are kick ai. I meh,
Speaker: 1
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.
Speaker: 2
28:40
It’s sai 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.
Speaker: 1
28:48
The drive is incredible. Yeah. I
Speaker: 2
28:51
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. So 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.
Speaker: 2
29:07
But I feel like I honestly, maybe it’s a good argument for a private office. I should
Speaker: 1
29:11
Yeah.
Speaker: 2
29:12
Spend more time with you guys.
Speaker: 1
29:14
You could talk to your manager. Yeah. They might guess one.
Speaker: 2
29:18
I like being out in
Speaker: 3
29:19
the both
Speaker: 1
29:20
of those ai to get the most out of everybody. Yeah.
Speaker: 2
29:23
But I do think that there’s this AI use case that I’m missing that they should probably figure out how to try more often.
Speaker: 1
29:29
If people sana try your new product, is there a website they can visit or something or speak 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 meh to download the actual Gemini app.
Speaker: 3
29:45
It’s kick ass. It really is the best models. I think it is. Yeah.
Speaker: 2
29:48
And you should use 2.5 Pro.
Speaker: 0
29:50
2.5 Pro. Hey, this it’s it’s a you gotta pay. Right?
Speaker: 2
29:54
Yeah. You got a few query you got a few prompts for free. But, you know, if you do
Speaker: 1
29:58
it a bunch, you need to pay.
Speaker: 0
29:58
You’re just gonna make all these free money.
Speaker: 2
30:00
Bucks a month. Yeah.
Speaker: 0
30:01
It’s sai
Speaker: 1
30:01
you got a vision for, like, making it free and throwing some ads on the side?
Speaker: 3
30:05
Yeah. One step down in hardware cost, the whole thing will be free.
Speaker: 2
30:07
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, though we can’t supply infinitely to everyone right off the bat. But, you know, wait three months, and then the next generation
Speaker: 1
30:23
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
Speaker: 4
30:32
to get it for free.
Speaker: 2
30:33
For, you know, really good AI advertising. I just, Ai 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 ai better.
Speaker: 2
31:02
Alright.
Speaker: 1
31:03
Give it up for Sergei Brett.
Speaker: 2
31:04
Thank you.
Speaker: 4
31:09
Okay. Thanks everybody for watching that amazing interview with Sergey Brin, and thanks, Sergey, 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.
Speaker: 4
31:28
OKX was the sponsor of the McLaren f one team, which won
Speaker: 1
31:32
the race in Miami. Thanks to Ai
Speaker: 4
31:34
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.
Speaker: 4
31:52
USDC is a fully backed digital dollar, redeemable one for one for USD. It’s built for speed, safety, and to scale. They just announced the Circle Payments Network.
Speaker: 1
32:04
This is enterprise grade infrastructure that
Speaker: 4
32:06
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.
Speaker: 1
32:25
We’ll let your winners ride. Besties are gone. That is my dog taking it away from your driveway syntax. Oh, man. My We should all just get a room and just have one big huge orgy because they’re all just useless.
Speaker: 5
33:00
It’s like this, like, sexual tension and they just need
Speaker: 1
33:02
to release somehow. Wet your feet. Wet your feet. Are back?
