Full Transcript for Google CEO Sundar Pichai: AI’s Infinite Possibilities, Compute Scarcity Dilemmas, Future AI Products

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Visit chargebee.com slash Alex to learn more. That's C-H-A-R-G-E-B-E-E dot com slash Alex. We are at early stages of the most consequential technology ever. This is not like fiber. It's not dark. We are using it. AI researchers, for them, it's like the bigger the computer, the better the AI. You give the researchers the biggest possible computer, even if it is to the detriment of today's profits like the cloud business.

So the compute we have today is a function of decisions I made in 2024. Today, obviously, when you sit and look at anything, everyone is short, not just us, other companies, and everyone wishes You had 2x of what you had planned for. Where's Google's Muse or its DOTS product? Users both in consumer and enterprise will have access to kind of some multi-agent framework for them working all the time doing things.

And so, you know, it feels like a moment of infinite possibilities. Alphabet CEO Sundar Pichai joins us to talk about Google's plan to compete on the frontier of AI, the balance between cutting edge AI research and the company's cloud business, and what the future of the interface looks like. And Sundar is here with us in studio in Mountain View today.

Sundar, great to see you. Welcome to the show. Thanks, Alex. Great to be here. So Google is back on the frontier with Gemini 4 Argonne. In plain speak, that means it is a world leading model. It's benchmark breaking. It beats OpenAI and Anthropix public models on a variety of benchmarks, including on coding, math, science, and knowledge work.

Now, the conventional wisdom had been that Google had basically given up on the frontier, that it was going to make these fast models that were good enough and use them in its products, and it wasn't pursuing frontier AI anymore. But that was wrong.

I mean, very wrong. Look, I think we are so committed to being at the frontier. I think it's in our DNA. We won't do it any other way.

We've been thinking about the AI frontier for well over a decade now. We definitely, the frontier is very dynamic. It's very competitive. It's very exciting. I couldn't be more pleased with how Argonne has come together. We took our time with it. You know, we made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities.

But I think across the board, be it for agentic work, knowledge work, coding, creative tasks. I think Argon is one of the really good frontier models. And in the first 72 hours since we launched, it's almost up to 25 trillion tokens used, which is about 3x what Gemini 3.0 had in the first 72 hours. So

and we are capacity constrained as we are serving it. So excited, but there are always areas, both the frontier is just last,

it's very dynamic and you have to keep evolving the models. And there are areas which we can see we need to improve as well. So we'll be at work on all of that as well. Which areas?

Look, on any given model, you know, Oregon will be across the board better at everything if you just look even two months back. But the space is so evolving. But even on coding, for example, there are areas, pockets where we know we need to do better and, you know, there's clear feedback and so we'll make it better. let me at least make the argument for why standard models might be good enough for Google.

I'm gonna channel what people say about this. And that is that the frontier of AI

today is getting so good and the baseline of AI is getting so good that the frontier is really only valuable for a specific set of tasks. And if you have a standard model, you could actually build great products with a standard model. Take Metamuse, for example. Meta built Muse on a good model, but not the best models. If you need to do biological research, maybe the frontier is really necessary.

But the people who would say, Google doesn't really need the frontier, They would say, you just need those standard models and you can put them in products like Maps and Gmail and in search. And, you know, again, take that meta example. That would be sufficient. What do you think about that?

Look, it's definitely not how I have thought about it at all.

I think we are deeply committed to driving the state of the art and actually push what this technology can go, where it can go,

all the way from transformers. That's a journey we've been on. So you will see us continue to do that. I think when you're at the frontier, you get an earlier look into the kind of emerging capabilities earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise.

Some of our cloud customers for sure want the model at the frontier. I think the frontier models will be better at developing the next set of models in an era of recursive self-improvement. So I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Orgon to our developers. It definitely tackles problems which our previous models could not do.

And so being at that frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. We've always, that's why we did flash, flashlight, flash, et cetera. And I agree with you that you can develop very good product experiences with not necessarily the most frontier model because they tend to be slower and more expensive too.

I think, but we care about the parade of frontier, and so we are committed to doing all of that well. You said that developing the frontier gives you a look into what's coming. Yeah. What do you see on the horizon?

You know, the exciting, what makes this moment so exciting is you...

you build something which you think is a culmination of like months of work and you put it out,

you know, you already see clear headroom ahead, right? I think, I mean, the frontier is going to, we've all been working on these agentic long horizon capabilities. but clearly the frontier is going to have multiple of these agents working together to do things, right? And so that's an example of an exciting paradigm underway. And it's also clear to me that users both in consumer and enterprise will have access to kind of some multi-agent framework for them working all the time doing tanks.

And so, you know, it feels like a moment of infinite possibilities. You mentioned recursive self-improvement. This is the models effectively making themselves better. Yeah. Maybe without humans in the loop. People call it like an intelligence explosion or expect to hear this sound like foom. And then all of a sudden AI is just like this explosion.

superhuman capability. And in fact, I think Sergey was doing this round table with researchers at DeepMind, and he talked about with Argonne that one of the core innovations for Argonne was relying more on the model to help develop the model, right? We've seen what's gone on with AI models recently. They are well aligned, but they're not perfectly aligned.

And the imperfections are a big problem. Obviously the tech world, and now the world more broadly knows about the Hugging Face incident where open AIs, bots broke containment and hacked into Hugging Face and did some crazy stuff. Google has also had a scenario where its bots got access. I think they were guessing passwords and they got access to a site they shouldn't have.

They stopped, but these models there's enough evidence that they're not doing exactly what we want them to be doing, that this idea of pursuing this recursive self-improvement or even

making the models better much faster, it's sort of freaky. It makes me scared.

Don't you think there's reasonable worry here and there should be something done to have the tech industry chill out about this?

Well, maybe three parts to your question. First, let's talk about RSI itself. And you're right, Sergey mentioned

RSI being critical contributor as we are developing Argonne. And, but to be very clear today, you know, in a continuum of, we are pretty far from, you know, the end state of RSI or something like that, right? So today you have the right way to describe it as you have RSI loops along with AI researchers in various aspects of model development, right? So, you know, I would say, You almost have agents helping AI researchers in some automated way work together to build these models.

So I think it's good to at least understand where we are today, and it's super exciting, and I expect that continuum to keep making progress as we go into next year.

The second question is around safety. Look, the way we are, I mean, security comes from a difference in depth. You're talking about alignment. Obviously, we all are doing a lot of work to make sure the models are as aligned as possible, but it's an active area of research and there are clear gaps, but that's not the only thing we rely on, right? So you have the work you do on alignment, But beyond that, we put a lot of effort into real-time monitoring as these models are working.

So making sure you have good telemetry into what's happening and so that you can intervene in real time if you see unexpected behaviors. And how you do that, the architecture with which you do that, how do you make sure it's all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring. These are all areas of investment we are doing.

And obviously, when we deploy these models, giving customers, enterprises control, both with real-time monitoring, but their ability to decide where they deploy, what type of data these models ever have access to, et cetera. So all that work is very, very important, and we are doing a lot of that with Argonne.

So I think as an industry, we need to constantly up our game and take the time to do all this well, including dedicating the compute required to make sure you can safely deploy these models. The third part is, is it all enough? So I think,

I think this is why I think there's a lot of conversations in industry and I think it's reasonable to be worried, but

the right way to approach something you worry about it is early on thinking about what more we can do. I think all of us are,

I think as an industry, I think you're definitely seeing an increased levels of work on alignment,

testing for safety, et cetera, as we deploy these models. I think the next step would be developing better standards so that across industry standards of how we do these things, including ability to evaluate using independent evaluators. You know, if you look at areas like financial controls, we have like accounting standards, we have independent auditors, et cetera, right? So those are all the next natural steps for us as an industry to work towards this, and regulation will end up playing a role as we go through all of that as well.

Okay, technology question for you. Google is somewhat unique in that you research the language or the LLM side of the equation, which started with the transformer language, and then you also pursue world models. And, you know, there are some AI research efforts out there like OpenAI that have basically determined that the LLM is the path to AGI.

And they've actually subset part of their world model research, which is like the AI model that tries to understand the physical world. But you're pursuing both. So talk a little bit about why you find the world model side to be important.

You know, I mean, as humans, you obviously perceive the world around you and, you know, you're taking all those inputs, you know, making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. And I think as you're working We are working across all possible applications of this.

You know, take an area like robotics. It's not an accident Orgon, if you look at, you know, embodied reasoning benchmarks, Orgon is state of the art, right? I think it literally sought on many of these benchmarks. And that all comes from, you know, our experience building world models as well. So... Inherently, I would expect there will be architectures which will converge all of this in the future, but I think as far as we view this as today in

this jagged frontier that is intelligence, We want to be at the frontier of all the possible approaches. And I think that will help us both drive better research, better insights, and develop a much better AGI

model is how we think about it. Yeah, I looked at your Twitter after the Argonne announcement, and it's all just like real-world application. It's not like text or a model generating text. Okay, I want to talk to you about the way that you balance allocating compute, which is your most precious resource, between cloud and AI research. This is a big topic about Google.

I've had people suggest to me that Google should get out of the AI research game and take all of its cloud computing resources and its chips and just sell that. And you look at the world of business right now. And NVIDIA, which is doing that effectively, a $5 trillion company, there's a push and pull between cloud that wants to sell this computing resource at a high markup, and AI research that needs a very big computer to make the best AI.

Now, you've said a big part of your job is figuring out how to allocate compute.

How do you do this, and how do you balance these two?

Look, I think we are... the ground zero for us is making sure we are investing in the compute that's needed to be at the AI frontier. So that is You know, in 2015, I said the company to be AI first. So AI is that key horizontal ingredient across everything we do, all the way from search to cloud to Waymo to everything we do. So I think that is ground zero for us, making sure both for research and developing the models that we have enough compute needed.

Beyond that, look, we have these independent businesses which each have their own long range plans. And so they're using those long range plans to project forward and we plan for compute for all of them. And so the cloud team, they have a business and they have a projection and they have demand signals, they've committed contracts. So with that help, we can plan on a long term compute trajectory for them.

I think we are fortunate to have this wide set of opportunities. I think there's no company in the world which isn't making trade-offs at any given moment in time. But the first principles we operate on is to making sure we have enough compute to invest in the frontier. And that's non-negotiable and everything else works on top of it. I think what you're seeing in these conversations is if you sit now, remember, we are building our own compute, our own data centers.

So there's a long lead times. So the compute we have today is a function of decisions I made in 2024.

Even if you think back to a year from today, last year, the agent recording takeoff hadn't happened. So today, obviously, when you sit and look at anything, everyone is short, not just us, other companies, and everyone wishes you had two X of what you had planned for. I think that comes with the territory, right? And at any given moment in time, we are planning for what we would need two to three years out in the future.

I think you're going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don't spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access.

After that, we plan for all our businesses. I think it's great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well.

Briefly, there was a podcast where Ben Horowitz relayed that Andy Jassy had said something like, computers sold out till 2028. Are you seeing the same thing? Um, you know, I think it's a, yeah, it's a reasonable, like, you know, if, if today a customer is coming out and asking us for compute in, uh, 27, that is, uh, incredibly difficult to do.

Um, you know, there, there's, uh, part of the reason even last year we went out and leased some short-term compute was to accommodate some customers and help them out. But yes, I think we are all dealing with similar timelines. One bubble question. There's talk of how every technology shift has seen overbuilding. Railroads, fiber. Do you think there's overbuilding here?

It depends on the timeframes you're looking at. Are we, you

know, this level of build, you know, if I take a long enough timeframe, it makes complete sense to me. We are at early stages of the most consequential technology ever. And this is not like fiber. It's not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly, right? But

Running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you're getting in the forms of contracts. You balance that with the underlying technical capability, our internal evals, how we are using it internally, the model trajectories. And based on all that, you take a disciplined approach, a confident, aggressive, but a disciplined approach to plan out compute.

But when you talk about the broader economy as a whole, you're having people operate with various levels of

risk. And so it's tough for me to, I focus on what I can control as a company and plan through it. If your question is, can there be an O build? Yes, because you can have some constrained components. And remember, what matters is a square set of everything you need to build out that compute. Memory, electricity, electricians, land. Yeah, there are like 2,000 components or more, right? And so you could get constrained at some point.

There could be things which cause bottlenecks, and then you could have O builds. There could be external shocks to the economy, et cetera. So there are macro risks. All that is possible at any given time. But

am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. Okay, one more on this.

You know, if you're going to do both, there's been a line of thinking out there that AI researchers, for them, it's like the bigger the computer, the better the AI. And if you're someone, I was in the room when you said that AI would be as profound as fire and electricity, and we all laughed at the time, and I kind of regret that laugh now.

But if you're someone that actually believes that, then the thinking would be you give the researchers the biggest possible computer, even if it is to the detriment of today's profits like the cloud business. Your thoughts.

we're all doing some form of that, right? I think

our cloud business is, at any given moment in time, foregoing short-term revenues because we are making compute trade-offs to support frontier AI development. So I think what you're saying is true already, right? But you want to be thoughtful, right? Even our AI researchers...

You know, constraints are also good. That's what inspires creativity. You know, that's what helps you come with a more efficient algorithmic breakthrough, maybe to use less memory, et cetera. So, you know, so what we are committed is to invest what's needed to stay at the frontier, right? That's how I think about it. I wonder what you think the future of the interface looks like, the way that we use computing in the future as AI starts to roll out.

I was speaking with Greg Brockman, the president of OpenAI, about this a couple months ago. He had a very interesting quote. He said, what is the interface that you want? What is the product that you want? The answer is that you want no interface. You want no product. You want this to be what's the interface between you and me, basically saying, just talking.

That if AI is that smart, there shouldn't be like, you know, maybe a screen or just so many buttons and things that you have to touch. What is your view of what the interface looks like in the age of AI?

The way I think about it is I do feel, you know, computing historically has been humans adapting to computing. With AI, it'll be the other way about. The technology will adapt seamlessly to how humans live and go about their day-to-day lives. So there's an aspect of it. You know, you want that AI to be able to speak to it when you want it to.

It has context. It can be persistently running somewhere, give you continuity, you're not dealing with a lot of complexity, it's natural, all that makes sense to me. That doesn't mean it won't have an interface, however, to me. Look, today, for example, I mean, nowadays, I just, even in my phone, like, Gemini voice models are so good, I can just press the microphone button I can just ramble.

That's why we call it Rambler, right? I can ramble. It kind of intelligently corrects with my personal context. So even my friends' names, like weird names, it kind of gets it right, even if it first guesses it wrong. It's amazing. I'm not going and

backspace editing once I finish saying it, right? It's remarkable how good it's gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. So you may ask your AI to create something, and you'll want to watch it, consume it. So I think the way I think about it is you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is.

The technology is adapting to how you're working at any given moment in time. Yeah. I'm just gonna, um, play the open AI hits because, um, you know, last year you sent them into a code red. Yeah. I'm sure, you know, um, Gemini, uh, was doing very well and open AI batten down the hatches. Um, and I was speaking with Sam Altman about the challenge from Google.

What he said was he thought OpenAI would fend you off because they're building AI native. And I don't know if he said it specifically like this, but he implied Google is bolting on AI to existing products. What is your view of whether a bolt-on, I wonder if you would accept that framing, can compete with an AI native product?

Look, first of all, I mean, it's a very dynamic moment. We are all pushing each other. I think OpenAI has pushed us hard as a company too. So this is not just, you know, we are doing something to them alone, et cetera. Far from it. Yeah, you went into CodeRed too. I mean, I know it's disputed about whether it was, but a version of this. I mean, it's been the AI moment.

All of us had to respond with varying levels of urgency at any given moment in time. Look, I think, you know, we are constantly building new products as well. I mean, Gemini was built from the ground up, like as a product, the Gemini app I'm talking about. Or, you know, tomorrow as all of us are adapting to agents,

are you bolting agents onto ChatGPD or are they building it natively? How would you think about that? So you can always flip these questions back. So I don't think of it that way. I mean, I think of it as you want to build world-class product experiences

that are AI native and they are winning.

And some of it is adapting products. Some of it is rethinking new experiences. So they will all be part of how you approach this moment. Talk a little bit about the way that you see the business of this AI interface in the future. So for instance, if I'm asking my agent to go out and do something for me and it suggests a product, should it, I mean, maybe even if disclosed, does it get a cut of the sale if I make the sale? Or when you think about the way that you're going to make money from these products, what's the plan?

I think You know, obviously, I'm going to put enterprise aside. Okay, yeah, consumer question. So, you know, if it's consumer...

Look, I think there are consumers are going to increasingly value this technology. They're going to meaningfully, I mean, take software developers today. I think developers are willing to pay for these coding models, right? And because it really makes them more productive. Nobody would want to code the way they were coding three years ago, or even like same time last year.

So I think when you're delivering value, there's value to be had.

I think history of consumer product shows if you build a consumer product, I've never seen examples where consumer products really scale to billions of users where people are using it intensely and hasn't had monetization to go along with it. If you're providing value to people, there is inherent value to be had. Totally. And so the nature of it may change, right? I think you're asking a scenario in which agents are doing stuff for you.

At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. And there's going to be some mechanism by which it gets fleshed out. That's what an efficient system does. But I think what matters is that you're building those world-class consumer experiences, right? And so, am I

answering your question? I just wanna make sure. Yeah, definitely. Yeah. I promised myself one product feature request and one only. Yes. Here it goes.

Is it possible to make search in Gmail powered by Gemini as good as search in Gmail that you access when you plug Gmail into your chat GPT interface? What's going on here?

I think we have One of the exciting things we announced recently is in a unified Gemini agent for enterprise, which we have announced and we're bringing that to consumers, right? So these are

persistent agents which run 24 seven in the cloud. Like you're spinning up a micro VM effectively for every query. And from the runtime, the harness, the model, it's all a headless service, right? And it'll work everywhere. And it has your context with you. You can address this agent, whether you're in Workspace, in Surge, in Gemini, all of it.

So I think agents are going to drive this unification from a customer experience, which people are looking for. And you are right. When you have a broad product portfolio and you're moving forward fast, There are always areas where you're moving forward faster than some other areas. That's valid feedback, right? But we are putting a lot of time and effort into making sure as we build out our agentic experience, that unification and that capability works well across all these surfaces.

Okay. That's a good answer to the interface question too. Yes, it is. When people have thought about where Google, uh, plays next, a question comes up, you know, where's Google's Muse or it's Dot's product. Uh, those have kind of come out of nowhere recently and, uh, they've taken off. You have a version of that. Um, do you, do you anticipate creating a new product that competes with, let's just say Muse for instance?

Look, we already today, so the way we have envisioned this, in fact, we described Gemini, our goal with Gemini app to be a personal, proactive, and powerful assistant, right? That's what we are working towards. And you saw us describe the early versions of it at Google I.O. this year with Spark. But effectively, already, you know, If you open the Gemini app as a pro or an ultra user, you see chat and task, and they start giving you that agent experience.

With Argonne now, that capability is dramatically accelerated. And what we announced recently in our enterprise event, we are bringing that to consumers as well. But over time, so you will soon in Gemini, you will be able to ask something And it's a continuum. You don't need to think about whether you're asking a chat or an agentic task. It'll just kind of automatically do the right thing.

And it can work persistently on the background securely and get things done for you. Already empirically, when I look at what's coming into Gemini in our app, about last year you would have tagged them and most of them would have been chats, but a steadily growing percentage is already agentic tasks in Gemini. And so, yes, we are going to bring personal agents to our consumers.

It'll work in the Gemini app, it'll work in Google search, it'll work in Gmail. You can address it, call it, et cetera. But we want to make sure we're getting it right. It'll be powered by Argon, and so we're very excited to bring it to our users. I've got that pro subscription. I'm in tasks, so I'm looking forward to using it more. All right, a couple of random questions.

Google's committed and invested and has committed tens of billions of dollars to Anthropic. What do you see in Dario that makes you want to have such a deep commitment and investment to him? Very early on, when we looked at the founding team of Anthropic, it was very clear to us,

this was a world-class team which was involved in some of the earliest GPD models. And so we were quickly able to assess that they would be at the frontier. And so... We've always tried to identify and partner with companies, which would be at the cutting edge of technology. We've done that with SpaceX, we've done that with Anthropic, and that's what we saw.

We had a chance to partner. Some of the partnerships turned in the construct of our TPUs as well. So I think that's how we approach it. Okay. With Apple, you had a partnership with them to license Gemini to them to help improve Siri. Is that partnership expanding, contracting, standing still? I mean, we are,

for both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. And, you know, I thought Apple has done a phenomenal job of incorporating, they customize these models and build it into Siri. And it's been great to see the reception. And so we are committed to doing more. You're going to do more with them.

There's nothing specific to comment, but it's a partnership that's working well. Data centers in space, you've announced that you're doing more there. What have you learned so far about the feasibility of that project?

I mean, it's a very exciting project. You know, projects like this remind me, I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car, you know, compete in it, which ended up being the basis for Waymo, right? And so... You're always, with all these technologies, you're at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it's actually working in a meaningful way.

I feel the same with space. We've always believed in betting big on these technologies. Quantum has been a great example of it. We are probably 10 years into the journey. Waymo, we are about almost 20 years into the journey. space, we are two to three years into the journey, right? And

it's clear to me that it's an approach which will make a lot of sense in the future. Being in sun-synchronous orbits with 24-7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. But having said that, I mean, this is a challenging project.

The team has disciplined milestones. And so it was a super exciting milestone to send the TPUs up to space. And sometime as we are speaking, we are expecting a hello world moment from them. And so I look forward to

seeing that happen and we'll continue investing here too.

You recently, was it maybe last year, you said that 25% of code written within Google was written by AI. What's the number today? I mean, we are far, I think we've almost, that number in our world today is like meaningless in the sense that every part of code we now touch is influenced by AI deeply. What's now, what we are tracking with Argon, for example, we are now able to see.

It's been a point of inflection. People are using Oregon to really go tackle complex projects, right? And including projects which affects millions of lines of code. So we have definitely in the four weeks since we've launched Oregon, we have seen a 30% increase in

CLs and more importantly, almost a similar increase, one third decrease in like rollbacks, right? And so I think people are writing code more productively and better code with it. So we are tracking it with newer metrics now.

You know, talking about the opposition to data centers and how do you feel that? And if you can't build data centers in the US the way that you want to, where else do you go?

We're very committed to building our data centers very responsibly. I think when I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who would come and tell us their kids have gone to college because of our investments in these communities. And so- And we've grown in those communities.

And so I think, to me, this is about doing it right, engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% the cost of our power needs for these projects. It's understanding the impact on on neighborhoods and making sure you're working early to address them, et cetera.

So I think done rightly, I think you can continue making progress, but this is obviously, we are constantly taking feedback into account and so on. I do worry not everyone operates the same way. Yeah, people have been surprised by data centers showing up in their backyard. So I think those are all important to, as an industry, we should continue to do better.

But part of the reason in a 20-year timeframe we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. Is there an unproven discipline in AI right now that Google is working hardest to crack? So people have talked about continuous learning, getting these AIs to continue to update after they get released as opposed to forget through every session.

Look, there are multiple parallel bats. I mean, you saw with Oregon, we have a million output tokens, right? With our Gemini models early on, we had a one million context window on the input side, right? These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about content or learning? There are,

how do you think about more efficient architectures? So part of, we do dedicate a meaningful percentage of our compute to these research bets too. And so it's always, you

know, it's always goes hand in hand, including our world models, et cetera, right? So those are all bets we are placing on. Sometimes as part of these bets, there are ideas that emerge, which just make their way into the mainline models. And so maybe, you know,

You don't fully talk about it, but I think we have multiple bets in these areas. Okay, I want to end with this. To be involved in the tech industry the way that you are, there's a certain inherent optimism to it, right? A belief that if you build, you will improve the world. And this is something deep within the tech industry. Now, there are sides to that, right? It requires betting, oftentimes like betting a lot and hoping that the thing that you're working on is going to come to fruition in the way that you want it to be.

But it doesn't always work that way. I'll give you an example. I was just visiting El Salvador for the first, well, I went in a five-year bracket and I saw in this one town, just a amazing amount of enthusiasm for Bitcoin and the blockchain. And there was a construction happening and it was booming and then it didn't happen the way that folks expected.

And this visit was basically a ghost town with a lot of unfinished construction projects.

I think that to do what you do, it requires a type of optimism that says it's worth going for, even if the bets don't pan out and there could be consequences there. So I'd just love to hear your perspective on, you spoke about this at your Stanford commencement address about the need for optimism. But it's a nuanced form of optimism, right? So I'd love to hear your perspective on sort of how you maintain that.

And is it like a pure optimism or is it a couched optimism in a way?

Look, I think I genuinely have a deep conviction that, you know, that

you know, improving technology and improving access to technology makes people's lives better. It's been my personal experience in my life. So out of which comes that deep conviction optimism. But with that, I've always felt, you know, humanity's journey is always about working hard to harness that technology to make it work well for society.

So the optimism is not an optimism that you just build whatever you want. You work hard to make sure you're building technology in the service of humanity to make the human condition better. And that's got to be the defining goal as we are approaching it. And with that framework, I'm very, very optimistic. I think we are in very early stages of the kind of progress that we are going to see on many areas.

We are all talking about areas like healthcare and stuff, but I think a lot of the progress is ahead. And the ability to make things in a fundamentally cheaper way on many, many things where things have been getting expensive for a long, long time. discovering new materials, et cetera, which really bend the curve. I think it's tough to step today and understand the impact of all that, but they're going to be very profound.

So I'm very optimistic, but, you know, we have to be rightfully concerned about all the ways this technology can go wrong. And that's how you make sure

you solve those concerns. So I think they both go hand in hand. Well, Sundar, I know you don't do many of these. I really appreciate you spending the time with me today. And it was great to get a chance to see you and speak with you again. Thanks, Alex. I appreciate it. Thanks. Hi, everybody. Thanks so much for listening and watching. And we'll see you next time on Big Technology Podcast.

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