Sergey Brin's Insights on AI's Impact and Future

Sergey Brin believes AI's rapid progress is revolutionizing how we retrieve information and interact with technology.

### The Evolution of Google and the Future of AI
* 🌐 On September 15th, 1997, Google was registered as a website, sparking a **revolution in how information is accessed online**.
* 🤖 Sergey Brin, one of the co-founders, has returned to Google to focus on advancing the company's efforts in **artificial intelligence**, emphasizing its **transformative potential**.
* 💡 Brin expressed excitement about the rapid advancements in AI, noting that he dedicates **nearly every day** to technical work in this area, which he finds **incredibly thrilling**.
* 📊 He highlighted the evolution of AI from being a **minor part of computer science** to a field that now drives **significant innovations**, including **programming efficiencies** and **problem-solving capabilities**.
* 🧩 Brin shared insights on the development of various AI models, suggesting a trend towards **unified architectures** that could **enhance their effectiveness** across different applications.
* ⚡ He acknowledged the **substantial demand for computing power** to support AI advancements, noting that Google is **rapidly expanding its infrastructure** to meet this need.
* 🔍 Brin also discussed the importance of **balancing innovation with caution**, advocating for the release of AI technologies even if they come with **risks and imperfections**.
* 🌍 Ultimately, he believes that AI holds **immense value for humanity**, likening its impact to the internet's **transformative role in communication and information access**.

They wondered if there was a better way to find information on the web. On September 15th, 1997, they registered Google as a website. One of the greatest entrepreneurs of our times, someone who really wanted to think outside the box. If that sounds like it's impossible, let's try it. He took a backseat in recent years to other Google leaders. Brin is now back, helping Google's efforts in artificial intelligence. I feel lucky that I fell into doing something that I feel really matters, you know, getting people [Music] [Applause] [Music] [Applause] information. No introduction needed. Welcome. I just agreed to this last minute. As you know, I don't know where you pulled up that clip so fast. You guys' team is amazing. Kind of amazing. This is kind of amazing. Yeah, I thought Serge just well. He asked to come check out the conference, and I was like definitely like come hang out. I didn't actually understand to be perfectly honest. I thought you guys just kind of had a podcast and like a little

get together or something, but yeah, this kind of mind-blowing congratulations. Thank you. Well, I'm glad you came out. I'm feeling a little bit shy, but yeah, wow. But thanks for agreeing to chat for a little bit. We're going to talk for a little bit. So, this was not on the schedule, but I thought it'd be great to talk to you, given where you sit in the world as AI is on the brink of and is actively changing the world, obviously. You know, you founded Google with Larry in 1998, and you know, recently it's been reported that you've kind of spent a lot more time at Google working on AI. I thought maybe, and a lot of industry analysts and pundits have been kind of arguing that LLMs and conversational AI tools are kind of an ex-potential threat to Google search. That's one of the, and I think a lot of those people don't build businesses or they have competitive investments, but you know, we'll leave

that to the side um but there's this big kind of narrative on what's going to happen to Google and and where's Google sitting with AI and I know you're spending a lot of time on it so thanks for coming to talk about it how much time are you spending at Google what are you working on yeah um honestly like pretty much every day I mean like I'm missing today which is you know one of the one of the reasons I was a little reluctant but I'm glad I came um but I think as a computer scientist I've never seen anything as exciting as all of the AI progress that's happened the last few years thanks um no but it's it's kind of mind-blowing when I went to grad school in the 90s you know AI was like kind of like a footnote in the curriculum almost like you like oh maybe you have to do this one little test on AI we tried all these different things they don't really work that's it that's all you need to

know um and then somehow miraculously all these people who are working on neural nets, which was one of the big discarded uh approaches to AI in like the '60s, '70s, and so forth um, just started to make progress. A little bit more compute, a little more data, a few clever algorithms um, and the thing that's happened in this last decade or so is just amazing. As a computer scientist, like every month um, you know, well, all of you I'm sure use all of the AI tools out there, but like every month there's like a new amazing capability, and I'm like probably, you know, doubly wowed as everybody else is that computers can do this um, and so yeah, for me, I really got back into the technical work um, because I just don't want to miss out on this um. As a computer scientist, it's an extension of search or a rewriting of how people retrieve information. I mean, I just think that the AI touches so many different elements of day-to-day life, and sure, search is one.

of them uh but it kind of covers everything um for example programming itself right. Like the way that I think about it is very different now. Like you know writing code from scratch feels really hard compared to just asking the AI to do it right. Um yeah sorry um so what do you do then um actually I've written a little bit of code myself just for Just for kicks just for fun uh and then sometimes I've had the AI write the code for me um uh which was which was fun um I mean just one example. I wanted to see how good our AI models were at Sudoku. So I had the AI model itself write a bunch of code that would automatically generate Sudoku puzzles and then feed them to the AI itself and then score it and so forth right um. But it could just write that code and I was like talking to the engineers about it and you know whatever we had some debate back and forth like I came back half an hour later it's done and they they were kind of impressed because they don't honestly

use the AI tools for their own coding as much as I think they ought to, right? Um, so that's an interesting example because maybe there's a model that does Sudoku really well. Maybe there's a model that answers information questions for me about facts in the world. Maybe there's an AI model that designs houses. Um, a lot of people are working towards these ginormous general-purpose LLMs. Is that where the world goes? Some people, I think, refer—I don't know who wrote this recently—said there's a God model, like there's going to be a God model, and that's why everyone's investing so much, is if you can build the God model, you're done, you got AGI, whatever terms you want to use. There's this one thing to rule them all, or is the reality of AI that there are lots of smaller models that do application-specific things, maybe work together like in an agent system? Like, what's the evolution of model development and how models are ultimately used to do

all these cool things. Um, yeah, I mean, I think like if you looked 10, 15 years ago, there were different AI techniques that were used for different problems altogether. Like, uh, you know, the chess-playing AI was very different than image generation, which was, you know, very different um than like recently the graph neural net at Google that like outperformed every physics forecasting model. I don't know if you know this, but you guys publish this pretty awesome. Embarrassed I but it was like a totally different Arch. It was a different system. It was trained differently, and it ended up in that particular. So, there historically have been different systems, and even recently um like the international math Olympiad that we participated in. We got um silver metal as an AI. Actually one point away from gold um but we actually had three different AI models in there. There was one very uh formal theorem proving model that actually did basically the best. There was one uh specific to Geometry problems, believe it or not, that was just a special kind of AI uh and then there

was a general-purpose language model. Um, but, uh, since then, we've tried to take the learnings from that. That was just a couple of months ago, uh, and try to infuse some of the sort of knowledge and ability from the formal prover into our general language models. Um, that's still a work in progress, but I do think the trend is to have a more unified model. I don't know if I'd call it a god model, uh, but to have certainly sort of shared architectures and ultimately even shared models. Um, right, so if that's true, you need a lot of compute to train and develop that model, that big model. Uh, yeah, yeah, I mean, you definitely need a lot of compute. I think, like, I've read some articles out there that just like extrapolate. They're like, you know, it's like 100 megawatt and a gwatt and 10 gwatt and 100 gwatt, and I don't know if I'm quite a believer in, you know, that level of extrapolation. Um, partly because

also the algorithmic improvements that have come over the course of the last few years, uh, maybe are actually even outpacing the increased compute that's put into these models. So, is it irrational the buildout that's happening, everyone talking about the Nvidia Revenue, the Nvidia profit, the Nvidia market cap supporting all of what people call the hyperscalers and the growth of the infrastructure needed to build these very large-scale models using the techniques of today. Is this irrational or is it rational because if it works, it's so big that it doesn't matter how much you, well, first of all, I'm not like an economist or like a market watcher the way that you guys very carefully um watch companies. So, I just want to disclaim my abilities in the space um. I think that I know uh for us, we're kind of building out compute as quickly as we can and we just have a huge amount of demand. I mean, for example, our Cloud customers just want a huge amount of tpus, gpus, you name it um, you

know we just can't. We have to turn down customers because we just don't have the compute available. And we use it internally to train our own models, to serve our own models, and so forth. So, I guess I think there are very good reasons that companies are currently building out compute at a fast pace. I just don't know that I would look at the training trends and extrapolate three orders of magnitude ahead just blindly from where we are today. But the enterprise demand is there out there, you know. I mean, they want to do lots of other things, for example, running inference on all these AI models, applying them to all these new applications. Yeah, there doesn't seem to be a limit right now. And where have you seen the greatest success, surprising success in the application of models, whether it's in robotics or biology? What are you like seeing that you're like, Wow, this is really working, and where are things going to be more challenging and take longer than I think some people might be

expecting um yeah I mean uh now that you mentioned those well I I would say in biology you know we've had Alpha fold for quite a while um and I'm not personally a biologist but when I talk to biologists out there like everybody uses it and it's more recent uh variants um uh and that is I guess a different kind of AI but like I said I do think all these things tend to converge um you know robotics for the most part I see in this sort of wow stage like wow you could make a robot do that with just you know this general purpose language model or just a little bit of fine tuning this way or that and it's like amazing uh but maybe not for the most part yet at the level of robustness that would make it like day-to-day useful but you see a line of sight to it um yeah yeah I mean it would be it's I don't see any particular Google the robotics business and then spun it out or sold it. We've had like had aot five.

or six robotics businesses. They just weren't. The timing wasn't right. Yeah. Um. Yeah. Unfortunately. I don't know. I guess. Yeah. I think that was just a little too early to be perfectly honest. I mean. There was like Boston Dynamics. Um. What was called. Um. Start stamp. I don't even remember all the ones we had. Anyway. We've had like five or six. Embarrassingly. Yeah. Um. But they're very cool. Um. And they're very impressive. Um. It. Yeah. It just feels kind of silly having done all of that work. Uh. And seeing now how capable these General language models are. That include. For example. Vision and image. And they. Multimodal. And they can understand the scene and everything. And not having had that at the time. Uh. Yeah. It just feels like you were sort of on a treadmill that wasn't going to get anywhere without the modern AI technology. You spend a lot of time on core technology. Do you also spend a lot of time on product visioning? Where things going and what like the human computer interaction modalities are going to be in the future?

in a world of AI everywhere, like what's our life going to be like? I mean, I guess there's water cooler chitchat about things like that. Um, sh care to share any? I um trying to think of things that aren't embarrassing. Um, struggling but uh friends. I I guess it's like just really hard to you know just forecast like you know to think five years out because you know based on the base technical capability of the AI is what enables the applications. Um, and then sometimes you know somebody will just whip up a little demo that you just didn't think about. Um, and it'll be kind of mind-blowing. Yeah um uh and uh and of course then from demo to actually making it real in production so forth takes time. Um, I don't know if you've played with like uh the Astra model but it's just sort of live video and audio and you can chat with the AI about what's going on in your environment. You'll give me access right? Uh yeah I'll get well once I have

access um I mean I'm I'm sort of sometimes the slowest to get some of these things um but it's um yeah there's like a moment of wow uh and you're like oh my God this is amazing and then you're like okay well it does a correctly like 90% of the time but am I really like is that then worth it if 10% of the time it's kind to make a mistake or taking too long or whatever and then you have to work work work work work work work to get to Perfect all those things make it responsive make it available whatever and then you actually end up with something kind of amazing. I heard a story that you went in you were on site I should have mentioned this to you before you came on stage see if you were cool about talking about here we are um and there like a bunch of Engineers showed you that you could like use AI to write code and it was like well we haven't pushed it in Gemini yet um because we

want to make sure it doesn't make mistakes, and there was this like hesitation culturally at Google to do that, and you were like, No, if it writes code, push it. And you really, and a lot of people have told me this story because they said, and um, or you know, I've heard this, that it was really important to hear that from you, the founder, in being really clear that Google's conservatism, you know, can't rule the day today, and we need to kind of see Google push the envelope. Is that accurate? Is that kind of, huh, how you've spent some time, or I don't remember the specific, in just to be honest, but uh, but I'm not surprised. Um, I mean, I guess that's the question for me, is like, as Google's gotten so big, there's more to lose. I think there's like this um, yeah, I think there's a little bit of fearful. I mean, language models to begin with, like we invented them basically with a Transformer paper that was um, whatever, six, eight years ago, something like that.

um and uh oh no one by the way is back at Google now, which is awesome. Cong um and um yeah, we were we were too timid uh to deploy them um and you know for a lot of good reasons like whatever they some make mist mistakes they say embarrassing things whatever you know um they're you know sometimes they're just like kind of embarrassing how dumb they are. Even today's like latest and greatest things like make really stupid mistakes people would never make um and at the same time like they're incredibly powerful and they can help you do things you never would have done and um you know like I've like programmed really complicated things with my kid like they'll just program it because they just ask the AI using all these really complicated apis and all kinds of things that would take like a month to like learn. So I just think that that capability is Magic and uh you need to be willing to have some embarrassments uh and take some risks and um and I think we've gotten better

At that, and well, you guys have probably seen some more embarrassments, um, but you're comfor. I have super voting. You're still like, I mean, you're comfortable with the embarrassments at this St. It's so to do this like. I mean, not particular on the basis of my stock, but I um, but as a, you know, I mean, but am I comfortable um. I mean, I guess I just think of it is this something magical we're giving the world. Yeah, and I think as long as we communicate it properly like saying like look this thing is amazing and we'll periodically get stuff really wrong uh, then I think we should put it out there and let people experiment and see what new ways they find to use it um. I just don't think this is the technology you want to just kind of keep close to the chest and hidden until it's like perfect. Do you think that there's so many places that AI can affect the world and so much value to be created that it's not really a race between Google.

and meta and Amazon. Like, people frame these things as kind of a race. Is there just so much value to be created that you're working on a lot of different opportunities, and it's not really about who builds the model that scores the LLM that scores the best, that there's so much more to it? I mean, how do you kind of think about the world out there and Google's place in it? I mean, I think it's very helpful to have competition in the sense that all these guys are vying, and we just, we were number one for analysis for a couple weeks, by the way, just now, and I think we're, last time I checked, we're still beat the top model. There's just some L stuff, so you do care. Yeah, yeah, not saying not, but um, and uh, um, and I, you know, we've come a long way since um, you know, a couple whatever years ago um, ChatGPT launched, or, and we were quite a ways behind uh, I'm really pleased with all

the progress we made, so we definitely pay attention. I mean, I think it's great that there are all these AI companies out there, be it US Open AI, Anthropic, you name it, there's mistol. It's a big, fast-moving field, but I guess your question is, yeah, I mean, I think there's tremendous value to humanity. And I think if you think back, you know, like when I was in college, let's say, and there wasn't really a proper internet or like web the way that we know it today, like the amount of effort it would take to get basic information, the amount of effort it would take to communicate with people, you know, before cell phones and things. Like, we've gained so much capability across the world, but the sort of the new AI is another big capability, and pretty much everybody in the world can get access to it in one form or another these days, and I think it's super exciting. It's awesome. Sorry, we

have so such limited time. Sergey, thank you so much for joining us. Please join me in thanking Sergey. Thank [Applause] you. Thanks, yeah.

In conversation with Sergey Brin | All-In Summit 2024

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All-In Podcast

Sergey Brin co-founded Google in 1998 and has recently returned to focus on the company's efforts in artificial intelligence.

He believes the progress in AI over the last decade has been remarkable and is excited about its potential to change the way people retrieve information.

Brin is spending almost every day at Google, working on AI tools and being amazed by the rapid advancements in the field.

The speaker's perspective on programming has shifted, finding it easier to use AI for coding tasks.

They shared an example of using AI to generate and score Sudoku puzzles, which impressed their engineering team.

The discussion highlights the potential for both general-purpose and application-specific AI models, with a trend towards more unified architectures and shared models in the future.

The Impact of Algorithmic Improvements on Compute Demand

Algorithmic improvements in AI are outpacing the increased compute being 投入 into models, raising questions about the rationality of the current infrastructure buildout.

There is a huge demand for compute resources, with companies struggling to meet customer needs.

The application of AI models in areas like biology and robotics is showing surprising success, but achieving day-to-day robustness in robotics is still a challenge.

The Challenges of Early Robotics Ventures

The speaker reflects on their early involvement with multiple robotics businesses, noting that the timing was not right for success.

They express frustration at the impressive capabilities of modern AI and multimodal models, which were not available during their earlier efforts.

The speaker also discusses the difficulty of forecasting the future of AI and its applications, highlighting the impact of unexpected demos and the challenging process of turning them into real-world solutions.

Pushing the Envelope: Google's Approach to AI

The founder of Google emphasized the importance of pushing boundaries and taking risks with AI, despite the company's conservatism.

Google was initially timid in deploying language models, but their powerful capabilities and potential for magic were recognized.

The founder believes in openly communicating the technology's imperfections and allowing experimentation to unlock its full potential.

Google sees the competition in the AI space as beneficial, with multiple companies vying for leadership and creating value.

The rapid progress in AI, especially since the launch of ChatGPT, has been significant for Google, and they are pleased with their advancements.

The new AI capabilities are seen as transformative, providing access to information and communication for people worldwide, which is an exciting development.