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

Sundar Pichai is the CEO of Google and Alphabet. Pichai joins Big Technology Podcast to discuss Google's return to the frontier of AI, its strategy for competing with OpenAI, and the enormous computing resources required to build the next generation of models. Tune in to hear why Google is sacrificing short-term Cloud revenue to support AI research, how demand for compute is stretching into 2027, and why its new Gemini 4 Argon model is seeing explosive adoption. We also cover recursive self-improvement, AI agents, the future of Google's products, the possibility of an AI bubble, data centers in space, and Google's partnerships with Apple and Anthropic. Hit play for an in-depth conversation about Google's AI strategy, the fierce competition shaping the industry, and why Pichai believes we're entering a moment of infinite possibilities. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large scale Agentic CX transformation, including the business decisions, trade offs and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Speaker 00

  Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network/alex for free consultation. That's akash.network/alex for free consultation.

• 1:03Speaker 01

  We are at early stages of the most consequential technology ever. This is not like fiber, it's not dark, we are using it.

• 1:09Speaker 00

  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.

• 1:22Speaker 01

  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.

• 1:40Speaker 00

  Where's Google's Muse or its Dots product?

• 1:44Speaker 01

  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.

• 1:58Speaker 00

  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.

• 2:15Speaker 01

  Thanks, Alex. Great to be here.

• 2:17Speaker 00

  So Google is back on the frontier with Gemini 4 Argon. In plain speak, that means it is a world-leading model, it's benchmark breaking, it beats OpenAI and Anthropic's 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.

• 2:50Speaker 01

  I mean, very wrong. Look, I think we are so committed to being at the frontier. I think it's in our DNA. We wouldn'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 Argon 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.

• 3:34Speaker 01

  But I think across the board, be it for Agentic work, knowledge work, coding, creative tasks, I think Argon is, you know, one of the really good frontier models. And, you know, in the first 72 hours since we launched, it's, you know, 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, you know, 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.

• 4:19Speaker 01

  So we'll be at work on all of that as well.

• 4:21Speaker 00

  Which areas?

• 4:23Speaker 01

  Look, on any given model,

• 4:30Speaker 01

  you know, Argon 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.

• 4:51Speaker 00

  Let me at least make the argument for why standard models might be good enough for Google.

• 4:55Speaker 01

  Yeah.

• 4:55Speaker 00

  I'm going to channel what people say about this.

• 4:58Speaker 01

  Yeah.

• 4:59Speaker 00

  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 Meta Muse, for example. Meta built Muse on a good model, but not the best models. You know, if you need to do like 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.

• 5:44Speaker 00

  What do you think about that?

• 5:47Speaker 01

  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.

• 6:03Speaker 01

  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, 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.

• 6:23Speaker 01

  We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers for sure won the model of 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 Argon 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.

• 7:09Speaker 01

  There is room for models of all sizes and capabilities. We have 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. So I think, I think, but we care about the parade of frontier. And so we are committed to doing all of that well.

• 7:38Speaker 00

  You said that developing the frontier gives you a look into what's coming.

• 7:42Speaker 01

  Yeah.

• 7:42Speaker 00

  What do you see on the horizon?

• 7:45Speaker 01

  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,

• 8:30Speaker 01

  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.

• 8:43Speaker 00

  You mentioned recursive self-improvement. This is the models effectively making themselves better.

• 8:48Speaker 01

  Yeah.

• 8:49Speaker 00

  Maybe without humans in the loop.

• 8:52Speaker 00

  People call it like an intelligence explosion or expect to hear this sound like boom. And then all of a sudden, AI is just like this superhuman capability. And in fact, I think Sergey was doing this roundtable with researchers at DeepMind.

• 9:06Speaker 01

  Yeah.

• 9:07Speaker 00

  And he talked about with Argon, that the core, one of the core innovations for Argon 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 not, 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 OpenAI's 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.

• 9:50Speaker 00

  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.

• 10:12Speaker 00

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

• 10:22Speaker 01

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

• 10:31Speaker 01

  You know, RSI being, you know, critical contributor as we are developing Argon. 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 the right way to describe it is 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 in some automated way, work together to build these models. So I think I think it's good to at least understand where we are today, and it's

• 11:12Speaker 01

  super exciting, and I expect that continuum to keep making progress as we go into next year. Uh, the second question is around safety. Look, I the way we are, I mean, so safe, security comes from a difference in depth. You're talking about alignment, obviously, you know, 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, you know, we we put a lot of effort into uh real time monitoring as these models are working.

• 11:57Speaker 01

  So making sure you have good telemetry into what's happening and so that you can intervene in real time if they are 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, uh, 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 with

• 12:41Speaker 01

  our own. 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 to make sure you can safely deploy these models. The third part is, you know, is it all enough? And, you know, so I think, I think this is why I think, you know, there's a lot of conversations in industry, and I think it's reasonable to be worried, but, you know, I won't. The right way to approach something you worry about it is early on thinking about what more we can do. And I think all of us are, you you know, I think as an industry, I think you're definitely

• 13:25Speaker 01

  uh seeing an increased levels of work on alignment, uh, 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.

• 14:01Speaker 00

  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, you know, the transformer language, uh, 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 make why you find the world model side to be important.

• 14:42Speaker 01

  You know, I mean, uh, as humans, you obviously perceive the world around you, and, you know, you're taking all those inputs, um, 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 and I think I see a working, we are working across all possible applications of this, uh, you know, take an area like robotics. It's not an accident, or gone, if you look at, uh, you know, embodied reasoning benchmarks, are going to state of the art, right? I think literally sold on many of these benchmarks. And that all comes from, you know, our experience building world models as well.

• 15:26Speaker 01

  So inherently, I would expect, you know, there will be architectures which will converge all of this in the future. But I think it's for us, we view this as today, um, you know, in this jagged frontier that is intelligence, we want to be at the frontier of all the all the possible approaches. And I think that will uh help us both drive better research, better insights, and develop a much better, uh, you know, AGI model, uh, you know, is how we think about it.

• 16:00Speaker 00

  Yeah, I looked at your your Twitter after the Argon uh announcement, and it's all just like real world applications. It's not like text or, uh, you know, 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. That's a big topic about Google. I've had people suggest to me that Google should uh get out of the AI research game and, um, you know, take all of its cloud computing resources and its chips and just sell that. And you look at the world of business right now, Nvidia, which has like, just doing that effectively, uh, five trillion dollar company.

• 16:44Speaker 00

  There's a push and pull between cloud that wants to, you know, sell these this computing resource at a high markup, uh, 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. Um, how do you do this? And how do you balance these two?

• 17:03Speaker 01

  Look, I think we are, um, look, uh, 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 set 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 way more to everything we do. So I think that is ground zero for us, uh, 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

• 17:47Speaker 01

  range plans, and so they they're using those long range plans to project forward, and we plan for compute for all of them. And so the cloud team, you know, 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 uh works on top of that.

• 18:29Speaker 00

  Right.

• 18:30Speaker 01

  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 according 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 like, you know, at any given moment in time, we are planning for what we would need two to three years out in the future.

• 19:14Speaker 01

  So I think I think you're going to find some decisions you make may look suboptimal two years out, right? Depending on where the state of the art is. So so that, you know, I don't spend too much time thinking about it. The way I think about computer allocation is making sure Google DeepMind, both for research and frontier model development, is a is cutting edge in terms of compute access, right? And 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 we have done. It allows us to scale that up, get real test for it in the widest application set possible, you know, test it in the real world and make sure we are at the cutting edge of technology

• 19:58Speaker 01

  at every layer of our AI stack as well.

• 20:01Speaker 00

  You know, briefly, there was a podcast where Ben Horowitz relayed that Andy Jassy had said something like, computer sold out till 2028. Yeah. Are you seeing the same thing?

• 20:12Speaker 01

  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 incredibly difficult to do. You know, there there's a 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, yeah, we I think we are all dealing with similar similar timelines.

• 20:41Speaker 00

  One bubble question. There's talk of how every technology shift has seen overbuilding. Railroads, fiber. Do you think there's overbuilding here?

• 20:56Speaker 01

  It depends on what 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.

• 21:19Speaker 01

  The applications, the underlying technology is improving rapidly, right? But running a company, I, the way I work through the moment is you look at your demand signals, you look at your like committed revenue opportunities that you're getting in the forms of contracts. You balance that with like 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 you know, a disciplined approach to plan out, plan out compute.

• 21:56Speaker 01

  But when you talk about the broader economy as a whole, you're, you're having people operate with very various levels of risk and, and so, you know, so it's tough for me to, I focus on what I can control as a company and, and plan through it. If your question is, is the, can there be an overbuild? Yes, because you know, you can have some constrained components and remember what matters is a squireset of everything you need to build out that compute.

• 22:28Speaker 00

  Memory, electricity, electricians.

• 22:30Speaker 01

  Yeah, like there are like 2,000 components or more, right? And so, so you could be, you could get constrained at some point. There could be things which cause bottlenecks and you know, and then you could have overbells. There could be external shocks, right, to the economy, et cetera. So there are macro risks. All, all, all that is possible at any given time. But you know, you, am I confident that the compute we are planning for, we will have uses for it, given enough time? Absolutely, yes.

• 23:04Speaker 00

  Okay, one more on this.

• 23:07Speaker 00

  You know, if you're gonna 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?

• 23:44Speaker 01

  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, is, is true already, right? And, and, but you, you know, you want to be thoughtful, right? Even our AI researchers, you know, some constraints are also good. That's what inspires creativity, you know? That's what helps you come with the more efficient algorithmic breakthrough, maybe to use less memory, et cetera.

• 24:26Speaker 01

  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.

• 24:38Speaker 00

  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 of 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 a, 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?

• 25:16Speaker 01

  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 how it were to me.

• 26:00Speaker 01

  Look, today, for example, I mean, nowadays, I just, even in my phone, like, you know, Gemini voice models are so good, I can just press, 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, it's, I'm not going and backspace editing once, 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 know, you may want

• 26:43Speaker 01

  to be, entertain yourself, you may want to watch a video, like, you know, so you may ask your AI to create something and you want to watch it, consume it, right? 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.

• 27:05Speaker 00

  Yeah, I'm just gonna play the OpenAI hits. Because, you know, last year you sent them into a code red. I'm sure you know, Gemini was doing very well and OpenAI buttoned down the hatches. 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, you know, a bolt-on, I wonder if you would accept that framing, can compete with an AI-native product?

• 27:45Speaker 01

  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, we are doing something to them alone, et cetera, far from it.

• 27:59Speaker 00

  Yeah, you went into code red too? I mean, I know it's disputed about whether it was, but.

• 28:02Speaker 01

  We've all, I mean, it's been a, the AI moment, all of us had to respond with varying levels of urgency at any given moment in time.

• 28:10Speaker 01

  Look, I, I think, you know, we are constantly building new products as well. I mean, Gemini was built from the ground up, like, I guess, 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 ChatGPT or are they building it natively? How would you think about that, right? So you can always flip these questions back, right? Right? So I don't think, 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, 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.

• 28:55Speaker 00

  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?

• 29:21Speaker 01

  I think, you know, obviously, I'm going to put enterprise aside.

• 29:27Speaker 00

  Okay, yeah, consumer question.

• 29:29Speaker 01

  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 helps, 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 freely scale to billions of users, where people are

• 30:10Speaker 01

  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.

• 30:21Speaker 00

  Totally.

• 30:21Speaker 01

  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.

• 30:55Speaker 01

  Am I answering your question? I just want to make sure.

• 30:56Speaker 00

  Yeah, definitely.

• 30:57Speaker 01

  Yeah.

• 30:58Speaker 00

  I promised myself one product feature request and one only.

• 31:02Speaker 01

  Yes.

• 31:02Speaker 00

  Here it goes.

• 31:06Speaker 00

  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 ChatGPT interface? What's going on here?

• 31:21Speaker 01

  I think.

• 31:24Speaker 01

  You know, we, 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 24/7 in the cloud. Like you're spinning up a microVM effectively for every query.

• 31:49Speaker 01

  And you know, 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.

• 32:01Speaker 01

  You can address this agent, whether you're in workspace, in search, you know, in Gemini, all of it. So I think agents are going to drive this unification from a customer experience, which, which people are looking for.

• 32:17Speaker 00

  Yep.

• 32:17Speaker 01

  And you are right, when, 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, that's, that's a valid feedback, right? And, uh, 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.

• 32:41Speaker 00

  Okay, that's a good answer to the interface question, too.

• 32:44Speaker 01

  Yes, it is, yeah.

• 32:45Speaker 00

  When people have thought about where Google plays next, a question comes up, you know, where's Google's Muse? Or its Dots product? Those have kind of come out of nowhere recently and they've taken off. You have a version of that. Do you, do you anticipate creating a new product that competes with, let's just say Muse, for instance?

• 33:12Speaker 01

  Look, we already today, so the way we have envisioned this, in fact, we, we, we described Gemini, our goal with Gemini app to be a personal, proactive and powerful assistant, right? That's all, that's what we are working towards. And you saw us describe the early versions of it at Google I/O, uh, 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, and, and, and they start giving you that agentic experience. With Org on now, that capability is dramatically ax rated.

• 33:54Speaker 01

  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, uh, you will be able to ask something. And that will, it's a continuum, you know, you don't need to think about whether you're asking a chat or a 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.

• 34:34Speaker 01

  But it's, you know, a steadily growing percentage is already agentic tasks in Gemini. And so, so yes, we are going to bring, um, you know, 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, you can address it, call it, et cetera. But we will, you know, we, we want to make sure we're getting it right. It'll be powered by Org on, and so we are very excited to, uh, bring it to our users.

• 35:02Speaker 00

  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, invested and has committed, um, 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?

• 35:23Speaker 01

  Very early on, when we looked at the founding team of Anthropic, it was very clear to us. You know, this was a world-class team, which was involved in some of the earliest GPT models. And so we were quickly able to assess that, you know, they, they would be at the frontier. And so we've always tried to identify, uh, 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, uh, you know, that's what we saw. And, uh, we had a chance to partner. Some of the partnerships turned in the construct of our, uh, TPUs as well. So I, I, so I think that that's how we approach it.

• 36:07Speaker 00

  Okay, with, with Apple, you had a partnership with them to license Gemini to them, uh, to help improve Siri. Is that partnership expanding, contracting, standing still?

• 36:18Speaker 01

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

• 36:46Speaker 00

  You're going to do more with them.

• 36:47Speaker 01

  There's nothing specific to comment, but you know, I mean, it's a, it's a partnership that's working well.

• 36:53Speaker 00

  Data centers in space, uh, you've announced that you have, uh, you're doing more there. What have you learned so far about the feasibility of that project?

• 37:03Speaker 01

  I mean, it's, it's a very exciting project, you know, projects like this remind me, I still recall that moment in, uh, in the desert, uh, for the DARPA Grand Challenge, watching the Stanley car, you know, compete in it, uh, which ended up being the basis for Waymo, uh, right? And so you're always, with all these technologies, you're, you're at a moment in time where it looks a bit like sci-fi, but you know, 10 to 20 years down the line, it's actually working, uh, in a meaningful way. I feel the same with space, you know, we've always believed in betting big on these technologies. Quantum has been a great example of it. Uh, we are probably 10 years into the journey, Waymo, we are about almost 20 years into the journey.

• 37:48Speaker 01

  Space, we are two to three years into the journey, right? And, uh, it's, it's clear to me that it, it's an approach which will make a lot of sense in the future, uh, being in sun-synchronous orbits with, uh, universe, I mean, 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, uh, from them.

• 38:28Speaker 01

  And so I look forward to, uh, you know, to seeing that happen and we'll continue investing here, too.

• 38:36Speaker 00

  You've, you recently, was it maybe last year, you said that 25% of code, uh, written within Google was written by AI? What's the number today?

• 38:44Speaker 01

  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, uh, influenced by AI deeply. What's now, what we are, we are tracking with Org on, for example, we are now able to see, it's been a point of inflection, people are using Org on 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 have launched Org on, we have seen a 30% increase in sales and, and, and more importantly, uh, almost a similar increase, you know, one third,

• 39:29Speaker 01

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

• 39:45Speaker 00

  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?

• 39:57Speaker 01

  We're, you know, we're very committed to building our data centers very responsibly. I think if, when I look at our largest data centers in some of the communities where we have been the longest, you know, I, I visit them, I talk to people, you know, people who, 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, you know, being, engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power and we cover the hundred

• 40:36Speaker 01

  percent the cost of our power needs for these projects. It's understanding the impact on the, on neighborhoods and making sure you're working early to address them, et cetera. So I think done rightly, you know, I think, I think you can continue making progress. But, you know, we, this is obviously, we, we are constantly taking feedback into account and, and so on. I do worry not everyone operates the same way.

• 41:04Speaker 00

  Yeah, people have been surprised by data centers showing up in their backyard.

• 41:08Speaker 01

  So I think, I think those are all important to, as an industry, we should continue to do better. But part of the reason for, in a 20 year timeframe, we are thinking about things like data centers in space is making sure we, you know, we are able to put these data centers where, you know, where maybe the trade offs will be different.

• 41:27Speaker 00

  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?

• 41:41Speaker 01

  Look, there are multiple parallel bets. I mean, you saw with Oregon, we have a million output tokens, right? You know, you know, 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. You know, how do you think about synthetic data? How do you think about continual learning? There are, how do you think about more efficient architectures? So part of, we do dedicate a, 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

• 42:24Speaker 01

  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, you know, fully, fully talk about it, but you know, I, I think we have multiple bets in these areas.

• 42:45Speaker 00

  Okay, I want to end with this. To be involved in the tech industry the way that you are, there's a, there's a certain inherent optimism to it, right? A belief that if you build, you will improve the world. And this is, you know, something deep within the tech industry. Now, there are sides to that, right? If you, 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, I went in a five year bracket and I saw in this one town, just a, a amazing amount of enthusiasm for Bitcoin

• 43:28Speaker 00

  and the blockchain. And there was a construction happening and it was booming. And then it didn't happen the way that, that folks expected. And this visit was basically a ghost town with a lot of unfinished construction projects. I think that, that to, to do what you do, it requires, you know, a type of optimism that says it's worth going for, you know, even if the bets don't pan out. And, and there could be consequences there. So I'd, I'd just love to hear your perspective on, you spoke about this at your Stanford commencement, addressed 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, and is

• 44:10Speaker 00

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

• 44:16Speaker 01

  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, whenever, you know, no, you, you work hard to make sure you're building technology in the service of humanity, to, to make the

• 45:01Speaker 01

  human condition better. And, and that's got to be the defining goal as we are approaching it. And with that framework, I'm very, very optimistic. And I think we are in very early stages of the kind of progress that we are going to see on many areas. You know, we're all talking about areas like healthcare and stuff, but you know, I think a lot of the progress is ahead. And the ability to make things in a fundamentally cheaper way on, on many, many things where things are, things have been getting expensive for a long, long time, discovering new materials, et cetera, which really bend the curve. I think, I think it's tough to step today and understand the impact of all that, but they're going to

• 45:45Speaker 01

  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, you, you solve those concerns. So I think, I think they both go hand in hand.

• 46:03Speaker 00

  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.

• 46:10Speaker 01

  Thanks, Alex. I appreciate it. Thanks.

• 46:12Speaker 00

  All right, everybody. Thanks much for listening and watching and we'll see you next time on Big Technology Podcast.

• 46:34Speaker 02

  Everyone's got a hard question about AI. Your job, your kids, where it's going? Anthropic was built to surface those questions and share what it finds along the way because there's hope in hard questions. Ask yours at claud.ai/spotify and keep thinking. That's claud.ai/spotify.