Sergey Brin | All-In Summit 2024

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 uh that I fell into doing something um that I feel really matter you know getting people [Music] [Applause] [Music] [Applause] information no introduction needed welcome I 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 Ser 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 um but uh 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 um you know you founded Google with Larry in 1998 and um 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 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 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 mindblowing when I went to grad school in the 9s 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 7s 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 is 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 interesting example because maybe there's a model that does Sudoku really well maybe there's a model that like answers information questions for me about facts on the 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 what what what is the evolution of model development and the 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 aesome embarassed 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 there 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 months ago uh and triy to infuse some of the sort of knowledge and ability from the formal prover into our general language models um that's still working progress but I do think the trend is to have a more unified model I don't know if I'd call a god model uh but to have certainly sort of shared architectures and 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 I think like I've 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 uh because we just don't have the compute available uh 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 comput at a fast pace um I just don't know that I would look at the training Trends and extrapolate three orders of magnitude ahe just blindly from where we are today but the Enterprise demand is there out there you know I mean they they want to do lots of

other things for example running inference on all these AI models applying them to all these um new applications um yeah there doesn't seem to be uh 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 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 not particular on the basis of my stock but I 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 the model that score the llm that scores the best that there's so much more to it I mean how do you kind of think about um the world out there and Google's place in it I mean I I think it's very helpful to have competition in the sense that all these guys are vying and um we just we were number one for on olysis for a couple weeks by the way uh just now and I think we're last time I

checked we're still beat the Top Model there's just some El stuff so you do care yeah yeah not saying not but um uh and uh um and I you know we've come a long way since um you know a couple whatever years ago um chat GPT 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 uh US Open AI anthropic um you name it there's um mistol it's it's a I mean it's a big fast moving field but I guess your question is yeah I mean I think there's tremendous

value uh to humanity and I I think if you think back uh 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 um like we've gained so much capability uh ac across the world uh but the sort of the new AI is another big capability uh 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 uh 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