Full Transcript for Sundar Pichai on what's next for Google Gemini, Waymo, and data centers in space

Sundar, we're here talking about a week or so after the wider release of Gemini 4 Argonne. And I'd love to hear from you what the early reception has been to the new model. Pretty excited. You know, we had been using it internally for a while. It's definitely a world -class frontier model. Nowadays, when you look at the frontier, the breadth and depth of that frontier intelligence is pretty comprehensive.

So if I were to mentally visualize a heat map across agentic tasks, knowledge, work, coding, etc., I think it is very, very strong on many dimensions, and I think it competes very well. The reception has been, you know, in the first 72 hours since Argonne launched, you know, we've had about almost 25 trillion tokens, which is about 3x what 3 .0 had in the first 72 hours.

So, you know, it's definitely NVR capacity constrained as we work through this launch. So really good feedback, particularly from our customers. But it's clearly areas to do better. You know, but it's, I think the frontier is constantly, you ship, you get feedback, and you're on to the next thing. And it's as dynamic a moment as I've ever seen.

What are the areas that you feel like you want to do better on still? You know, we want to continue on coding. It's a very good model. Our engineers have loved using it internally. But there are areas where we can see we can do better. At any given moment in time when we have shipped, we internally are continuing to make progress on. And so you have behavioral refinements based on the feedback you're getting from people using it.

So that I guess we're going to do better there. You were mentioning the compute constraints. It's interesting that even a company like Google has those for a new model like this. I mean, Argon's a large model. It's obviously a very expensive model to serve. Can you walk me through the decision of how you have to now gate this? It's, I believe right now it's accessible through the API, AI Pro and Ultra subscribers, obviously not to the widest possible user base yet.

And I imagine that's challenging for a model of this size. So how do you think through that, gating that and releasing it? One of the things we've been very good at, if you go back to the 3 .0 models, we can distill off these models and get versions which are nearly as capable but much more efficient. So we are working on those models as well, which will help us give wide access to as many people as possible.

So those efforts are underway. We also, with this model, you know, we are definitely at a moment as we are working through Frontier, you want to really think about safety of these models. So we've invested a lot more effort, both in terms of how we tested it externally, through our Fairwind program, the U .S. government, giving early access to cyber researchers, internally, widely testing it.

And we've obviously brought it out externally. You're right, it's a large model. We are serving it pretty fast, given the size of the model. So I'm excited what we are doing, but, you know, we'll continuously expand access from now on. And so you saw at the launch time, we had made it available to all Ultra tiers, Pro tier in the Gemini app.

We followed it with Pro tier in the anti -gravity app. And we are working on, you know, distilled versions, which we can serve to more people with the 4 .0 series. Is this the carbon model that there's rumors about? At any given moment in time, we have internal versions of our Frontier models. And so some of these are code names of those versions.

That's different from us getting a more efficient version of Argon available more broadly. So more efficient version coming. The process of testing it for safety, I'm curious to hear you talk about that. It's interesting to think about how the private Frontier Labs, OpenAnanthropic, talk about the security and privacy of their new models versus a Google or a Meta or, you know, a larger hyperscaler.

It sounds like you guys are being very methodical with the safety testing for this one. And it's maybe taken longer than previous models. Is that fair to say? Definitely. I think with the agentic capabilities and its ability to code so well, particularly when you are deploying it, you know, we are serving many enterprise customers at scale.

You want to make sure. So, for example, the work we've done, obviously the work you do to align these models well. But more importantly, both the compute and the systems you build to monitor them while they are working. Right. And that can be compute intensive too. But that real -time monitoring as these models are deployed, I think ends up being critical so that you can, you know, you get as early signals as possible if something is not working as intended and you can intervene.

So, we have taken time to build those systems, the sandboxing you would need to do, the real -time monitoring you would need to do. And with that, also for customers, giving them the protections they need so that you exactly know where it's running, what access it has, all the telemetry you need, all of that. This interview is presented by HyperAgent.

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Mercury's a fintech, not a bank. Check the show notes for details. How closely do you follow the rat race of new models? I mean, I'm sure you do, but on X and everything of like, who's up, who's down? And, you know, I think you all really had a moment with Gemini 3 last year where I was like, wow, Google's back at the frontier. I think everyone obviously sees the benchmarks and Argonne is there for sure.

But it feels like the energy is constantly moving to who is ahead in the pole position. And how much do you focus on that? How much do you encourage, you know, your team to focus on that versus not? I'd be curious to hear right now how you're thinking about it. It's a great question. I think there's so much, it's such a dynamic moment. There's so much noise coming through.

I'd obviously pay attention. You know, I'm on X. I pay attention. It's important to get the pulse externally because you get a sense of where you're doing well, where you're not. So I observe all that. Internally, but more importantly, internally, we now have, you know, we have our own evals, which are pretty robust to evaluate SSR models.

We can see the capability curve thereon. So we get very high quality signals internally as well. I think what I encourage the teams is we are very committed to being at the AI frontier for the very long term. We didn't get on this train recently. We've been, you know, focused on this for a long time. And we want to be at that intelligence frontier a decade from now and so on.

So it is doing everything you need to, including safety, by the way. I think that's going to be critical to develop intelligence at the frontier. And but I think one of the things for us through this year is we needed to pick up the cadence at which we are shipping things. Right. I mean, the industry has been dynamic and, you know, the frontier models are pretty fast too.

So some of what we had to adapt this year was to make deeper progress on agentic coding particularly and then use that to improve our release cycles. We were able to start doing that with 3 .5 to 3 .6 to 3 .7 to 3 .8 flash. We kind of in about three months went through that sequence. And so obviously with 4 .0 we expect to have improved release cadence as well.

There's aspects of Argon I'm interested in. One thing I saw was that it beat Minecraft on its own in a little over a day with I think only less than 2 ,000 in compute which is pretty impressive. I'm curious what the implications of this level of agentic capability. I mean a video game is one thing but you could extrapolate that to potentially a lot of things.

That's advanced decision making, long horizon task, right? Minecraft. When you see that, what do you think and how does that impact how you plan ahead? We've had internal versions where, for example, with Argon we have seen this step change in internal productivity ever since Argon has been rolled out internally. A version of internally there, for example, there are large code bases on which we have pointed it to and it's gone.

Let's say we've had problems that we know we need to fix. It's millions of lines of code to be defected. In Chromium, et cetera, if you want to create an experience where you can drag out a tab, move it around, put it in an AI experience, how do you keep it live without reloading? That's the kind of like you can get excited about this feature in your head but historically for teams it cuts through all layers of Chromium.

But you can point Argon at it and it can persistently go and work through it and get it done. So we are seeing examples like that everywhere. So what that excites me is I think if in some ways I view 2027 as a golden age of that productivity frontier for us because of the new class of models and how well we are integrating it with our internal environments.

You can also apply these models to deeply improve model development itself, right? And what we in the industry talk about as recursive self -improvement. So those are all practically I think areas where you will see continued progress, which is exciting to me. Look, I think what you saw in Minecraft, from now on I think the capability frontier is there where you can in a dedicated way apply, assuming you have the compute and resources, deeper progress on many areas than we have before.

Any verifiable domain really, right? Yeah. And, you know, for sure. Yeah. Even in other areas, you can always have humans in the loop to give feedback and keep making progress, right? And so it feels like infinite possibilities. It's one of those moments where you think about, you know, if you can think about a thing, you know, you can apply it and make progress.

And you've seen that with mathematics recently, you know, and so things which you would think would take a long time, you can point and put effort into it and show dramatic progress like OpenAI did with mathematics with the recent models.

So I think we're going to see a lot more of that in 2027. Yeah, I've heard Sergey also talking about RSI recently and pushing the teams on that. And I'm curious, like, where is Google at on RSI internally? How much of current research and development is, I assume most of it's being assisted by the models, but where are we at on that continuum of the models really pushing? There's a lot of humans in the loop on these things and given the safety aspects of it.

But we are definitely at a stage where AI researchers are co -working with the models in a deep way, right? And, you know, the models are involved and maybe suggesting recipes, automatically evaluating next checkpoints. So there's a healthy amount of models and researchers co -working. There are aspects of the pipeline which are, like, probably closer to being, like, you know, what I would say in this continuum, more RSI -like.

Some of them are lesser, but the whole continuum is shifting. So, you know, these are the reasons I think, you know, it's important to think about how we develop the frontier as we go through it. But, you know, I would expect the next 12 months to see continued acceleration because of RSI. Quickly, while we're on just the model front here, I was at the last I .O.

and you guys talked about Gemini 3 .5 Pro. And there's still some questions out there of, like, whatever happened to that model? Did that just become Argon or what happened there? Now, when we were training 3 .5 series, we had Flash and Pro, which we were working on. An area where we internalized through the middle of this year is that there were gaps in both agentic capabilities, particularly around coding, and they are related.

And how fundamental it is to overall progress. So we wanted to definitely hill climb there. And the team felt the fastest way to hill climb was using Flash. It's a smaller model. It's more efficient. So you can really get a lot more iterative loops there. And so that's what led to 3 .5 to 3 .6 to 3 .7 to 3 .8 Flash. By the time we got that done, we had a chance to apply it to Pro, but Argon pre -training was done.

Got it. So it was just realizing that 3 .5 Pro would have had a very short shelf life. And so we decided to take all the learnings and apply it in Argon post -training.

And so it was, you know, in a constrained environment, you know, if there were things like compute, time, etc., you would do all of it. Yeah. But I think it gave us a chance to kind of skate to where the future needs to be. Yeah. And so obviously with 4 .0, you will have, again, different model versions in 4 .0. Argon is our largest model and what we have outside, but we will ship more models there.

But we are also making Argon available to the pro tier in the meantime as well. Where is the difficulty still in coding? Where is the area you're still trying to hill climb? It's not that difficulty. It's just the frontier is constantly evolving. Right. Right. And, you know, on every aspect of coding, Argon will be the frontier if you were to look two months ago.

Right. So it's not, I wouldn't say it's a difficulty as in you're constantly giving it more complex end -to -end agentic tasks. And you want the model to do incredibly well in a way that, you know, world -class team programming would accomplish it with good taste, great code, good reviewability. It's very safe, etc., etc. So it's just a dynamic frontier.

There are times, you know, all our models, you know, for example, take hallucination. It's a never -ending frontier to keep making progress on. It's an inherent part of the current state -of -the -art technology. But you're constantly working to reduce that. So it's both an opportunity and a difficulty to give one example of that. We haven't talked since there were some leadership changes that I wanted to ask you about at Google DeepMind.

Demis Asabas, the CEO, stepping back, becoming chief scientist and also focusing more on isomorphic, which I'm very curious about. And then Corey, you know, being elevated, running DeepMind, Jeff Dean, Google legend leaving to start Discovery Loop. And, you know, when that happened, there were there was a lot of press about that and intrigue about what was going on behind the scenes.

I haven't heard you or anyone named there speak on it since then. So I'd be curious to hear from you. Maybe you can put some of the rumors to rest. What was the thinking there? What motivated that? Why did that happen? You know, you're seeing some natural evolutions as people spend their time doing things for a while. Jeff was 27 years, I believe.

27 years. I feel proud I've worked together with Jeff for a long time, a long time. You know, obviously, he was seminal in his contributions here. And we are excited to see what he does with Discovery Loop. You know, I would start with saying he definitely wanted, I think, a moment in time where he saw an opportunity to go do something, which he was excited by with a set of people.

The change internally, I would just say you're going through a moment in the industry where the frontier model development is an all -consuming thing. Right. You know, we've had broad AI research, AGI research, and so on. But frontier model development is all -consuming. It's moving. It's picking up pace. Right. And it's a very scaled operation, all the way from data to compute to the algorithmic work you do to managing the teams and your model agents to do that.

So I think it's become an important job on its whole.

Demis, he's obviously been thinking about this moment for a long time. He's particularly interested in both how do you go through this frontier moment safely and how do you apply it to solve other problems. It's part of the work that he did with AlphaFold, and hence what we are doing now with isomorphic are examples of how you can apply, you know, AGI to solve other problems.

Medicine, drug discovery. Yeah. Yeah. Robotics and so on. Like, you know, it'll apply to everything. So I think he wants to spend more of his time. So he's very leaned in into those things. Which if you know Demis, you know he's always been passionate about that. He's passionate about it and he has been at it early on. And I think in some ways, both seeing that the model development is kind of a well -established process.

And Cori has been a deep mine for, you know, well over a decade too. And so we saw this opportunity for Cori to step up, do that. Allows Demis. Demis is still involved as chair of deep mine, but he's also spending a lot more time. You've seen him more involved with AI security conversations. But as well as thinking about how to apply AI in a more practical way, be it science, be it healthcare, and there'll be other areas as well.

Does this signal any kind of shift with how DeepMind relates to the rest of Google? Is it merging in closer? Yeah. What are the natural next order effects of this? There's been a natural process over many years, including when Demis was running it, where DeepMind, the Gemini app gets built within DeepMind, right? So as an example,

Cori had moved here to the Bay Area almost, you know, maybe 18 months ago. There's been close integration. It makes sense for compute, for DeepMind to impact the rest of the Google. So there's been deep collaboration, which has already been underway a lot. So I don't think any of this in the continuum wasn't maybe externally people noticed that as a moment.

Yeah. But for us, it's been a deep coupling, which has been underway for a long time. How do you manage and are managing allocating compute towards long -term research bets? I think what DeepMind was historically known for kind of pre -CHATPT and the current AI boomer in with the near term, the frontier models that you talked about, the Gemini app, the products, the commercialization.

How do you allocate that? Has that shifted at all in the last year or two? You know, the problem is always, you know, stakes are high. And, you know, and I think we are fortunate to see extraordinary opportunities at being at the frontier of intelligence, having a very successful consumer business across many, many products and services, and being, you know, one of the top cloud hyperscaler companies in the world and the opportunities to provide the technology.

So I think the opportunity space is large. And so obviously we have to plan ahead. But I think we have a pretty rigorous approach to plan ahead. You know, these teams plan independently. Underlying all of that, we've been an AI -first company for a long time. And so with that comes a commitment to invest in the compute that you need to be at the frontier, both for research and developing the models.

So that is ground zero with which we plan for the future. Beyond that, there are these businesses. For example, Thomas and the cloud team, they have their own long -range plans. You know, with their revenue projections, the opportunities. And we use that to plan, compute for them. You can always have moments when you look back in hindsight.

I mean, there's no company in the world now sitting here. To give you a sense of how fast the technology frontier is moving, same time last year, this agent encoding liftoff hadn't happened. Yeah. And remember, we build our own compute, right? And that has long lead times. Yeah. So at any given moment in time, you're living with all the compute decisions you made two years ago.

Yeah. But people are assessing you based on the compute decision. They have the perfect information today. And so… You had to make the call two years ago. Two years ago. And you make the call based on the signals. You're confident. You take a long -term optimistic view. And you're aggressive in your investments. Yeah. I don't think any company in the world today would sit and tell you, like, you know, today they got their decision right two years ago.

Right? And so you're working through those moments. And I think that comes with the territory. Comes with the territory. We plan to be state of the art in terms of our compute investments on an ongoing basis to develop, to both drive research and develop frontier models. Right? And beyond that, you know, we'll have opportunities to apply it in many other parts.

And I think, you know, we plan to do it to the best of our ability. So that means you're planning right now for 2028, 2029, right? Yeah. Depending on, you know… Yeah. Yeah. If you're planning for land, power, et cetera, you're probably in the outer years. Right. And there was a big moment over the summer where you all went cash flow negative for the first time since the company IPO'd.

And I think a lot of people in Wall Street… Free cash flow. Free cash flow. Not already. Yes, of course. And people saw that and go, wow, Google's continuing to invest more aggressively. I think you guided up CapEx for 2027. You're thinking about 2028, 2029. What are you seeing that's giving you the resolve? Especially the moment in the summer where Google goes negative on free cash flow.

I assume that was a big decision. And how do you think about that and what is giving you that resolve? The strength and durability of the demand signals we are seeing. So it's probably, you know, to do something like this, you need to be confident at multiple levels. One is I think we are fortunate to have early signals from such a diversified opportunity set, right? You know, all the way from consumer to enterprise.

And so we are seeing that robust demand signals. You probably have more signal than any company on earth, actually. I haven't thought about it that way, but you could arguably make a case for it. And so you have that robust demand signals. We have the demand signal for Google itself, which is a cutting edge enterprise of how we are using AI and adopting it.

And you're seeing the capability frontier. Then you're able to independently, outside of the demand signal, in some objective way, actually assess the capability frontier and see how it's progressing. Right. And seeing how that curve looks like and be the scaling loss or like how our new models are doing compared to our old models, the complexity of tasks we are able to tackle, our internal evals and benchmarks and so on.

So you're getting a first -hand sense of it. You're also getting a sense of the breakthroughs from research, which give you a sense of, OK, will Gemini 5 .0 be much better than where for Argon is? And the answer is yes. Right. So you're getting all those confidence signals across that layer of the stack. And we also have durable long -term contracts with committed revenue, which we are signing.

So it's not just hope, right? You're planning to book of business as well. So you take all that into account and you lean in in a responsible, disciplined way, which is what we are doing. It's interesting to hear you talk with confidence about those signals, which obviously I believe. And then I think people are also trying to grapple with, for example, like Core Reave was the latest one.

But there's fears about the neoclouds. There's fears about the financing of these things, the brittleness of it. When you look out there, even beyond Google, but Google's involved in a lot of these things. Do you sense anything that gives you worry about a near -term, not even Google -specific, but just that the market's maybe over its skis? Look, I was obviously answering the earlier question from a Google standpoint, right? Like looking at it, et cetera.

You know, the broader market, to me, you have anytime you work through these moments where the opportunity is massive and the rewards are high and you have a lot of players coming in, you are going to have dynamism and volatility associated with that. And I think that is inherent part of a moment like that. This is why I try to focus very hard on that signals, which I have control over and I can see, right? And the demand signals, the business clear line of sight to the business opportunities we have in terms of contracts, backlogs, and so on.

And you plan for that, right? And I think you design the company in a way in which you can work through moments where even if there's some variation, the long -term arc of it is good for the company, right? And so that's why your ability to take a long -term view helps. And even if you look through the last five years, the pandemic drove a big shift towards digital.

Then some of it corrected. But it made sense to invest to drive that shift forward for most companies, which did so, have done better, right? So you have to understand long -term circular trends and invest to capture the transition. In fact, we invested a lot of our headcount growth as a company through those years was for Google Cloud. And we are benefiting on the other side of having scaled up.

We were relatively later to the cloud market, but we took a very aggressive investment posture into that for a period of six to eight years. And that helps us through a moment like that. I think that's how I think about the AI moment as well. Can you look out anywhere in the future and see where you're not compute constrained right now? Is there any moment in time you can look out and see? I hesitate to answer that way because...

Yeah, you got to be careful because everyone will freak out if it's like... No, what I mean is it's more that we live in a moment in time in which the global economy is so intermingled in such complex ways. And you can have the butterfly effect of one incident which can have ripple effects everywhere, right? And so I'm trying to decouple two things.

I'm not saying from now on everything will be a smooth... No, life doesn't work that way, right? You know, economies grow. There are hiccups. We work through. There are business cycles. Yeah. All that are inevitable parts of how the world works. Right. And I think no one thinks this is like the past bubble. Like this is not fiber. Yeah.

Regardless of... We're using all this. We are super constrained. And more importantly, we're using it for real today in practical value -deriving ways. And we see opportunities. We see clear line of sight in which all of this will work better in the future. But that doesn't mean as a collective industry, you know, or an economy, you overshoot or, you know, or you have shocks to the system which are out of your control and all that.

So those are two separate things, right? And which is why part of it is I focus on the things that are in our control, our ability to take a long -term view, take our years of experience to better forecast, better model, better invest, and apply to this wide opportunity set we have in a pretty disciplined way. And so I put my energy, time and attention into that and less thinking about, you know, is there too much exuberance or not?

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I'm curious, even in the last few months, is it involved at all? Are you finding that you're able to make make better decisions or that you would even maybe hand off certain decisions to a model on that? Something as big and important as that? Srinivasan Parikh Two ways I would answer the question. One is given the stakes, it's quietly, you know, the decisions are being made by humans in the loop, including me and, you know, directly involved.

But I would say, even for me, one of the use cases, you know, I have agents running internally for me, you know, agents I've deployed, both to help me understand, you know, simple thing. It's not super fancy, but, you know, at our scale, even to understand the model development arc, you know, there are all these chat groups that are pre -training chat groups, mid -training chat groups, you know, post -training data, et cetera.

It could easily take me an hour a day to kind of catch up what's happened in the last 24 hours, right? But now I have agents running with summarize and bring that information to me. I can still go deeper and I do so often. Similarly, there are agents with telemetry into our compute usage, by -product area, efficiency, utilization, and is giving me inputs and insights.

You know, so those are the ways we are weaving AI into all this. We have extraordinary people working with all of this as well. I think it's made all of us, you know, be better at what we do. And so that's how I'm using it. So no future near term where AI is making the compute decisions? I mean, even five years ago, you know, we used AI to make power consumption efficient in our data centers.

So we do have, when we are making compute allocation decisions, AI is a core part of the input it's providing, right? So I'm trying to get you off the hook here. Oh, the model said it, not me. You know, it's a, it's a continuum there. By the time it's doing that, you would be applying your sort of the next problem and so on. So that's how I think about it.

Yeah. I'm curious what you make of this personal agent race that the industry is now in with Muse, which is a great product instinct. I remember at the last IO, you guys showed Gemini Spark. And I'm curious, you know, do you think Google needs to do more here on consumer? I know you just had the Gemini workspace where you're giving an agent an email, a workspace identity, all that.

But in the consumer race, when you look at it, it's early, but I imagine you want Google to have even more of a stake in that. Oh, you know, it's a huge area of focus. In fact, you know, the goal we set out for Gemini, how we call it, we want it to be a personal, proactive, and powerful assistant, right? So it's a core part of what we're trying to do with Gemini.

And you are right in IO, that's what, you know, with Spark, I think, you know, that's the direction we are doing. We obviously spoke more about it in our enterprise event in the context of the Gemini agent. To be very clear between enterprise and consumer, it's the same model, same harness, same runtime. It is just the context of what you have in a business or your personal context, you know, is different.

And everything we announced in the event, because we're building it for enterprise, has world -class security governance. Every task there, you know, we are provisioning essentially a micro VM to go complete the task, running securely 24 -7 with real -time monitoring, et cetera, and providing enterprises with the governance and control and auditability of those agents.

We're bringing all that to consumers as well. So already you saw, you know, you have chat and task now in Gemini. Our vision, you know, in the near future is it's one box and, you know, it's a content of what is a chat, what is a task, you know, which of those need to be persistent, which of those need to be scheduled, how does it come back to you.

I think we view it as a natural opportunity. Empirically, when we look at what people are doing in Gemini, if you were to go back and classify it, we see the trend already, right? So the percentage of, if I were to call queries, which are more agentic, has been steadily increasing over time, right? And we're going to make all of that more widely available as we go through the next few months.

And we mainly launched all of this to Ultra and Pro users. And so we're going to make it more widely available. The newer Argon models deliver a huge step change. But you will be able to, again, as a consumer, you know, talk to Gemini, not just in the app, but in the context of like, you know, maybe from Gmail, or you can call, you know, so we are focused on it, you know, from search, and we're focused on it in a pretty deep way.

Yeah. A thing I've been dying to ask you, especially with your experience, you know, running Chrome before, like, the moment we're in, all these agents are using browsers like humans to complete tasks. I mean, with Muse, you can literally see, right? And, you know, it feels when I look at it, I get why it's happening, you want to get the task done.

And that's the way to do it. But it feels like trying to fit a triangle into a square, it doesn't feel quite right. And to me, I'm, it's making me go, does the web need to fundamentally change for these agents? And how I know there's protocols, you guys have worked on them, there's others. But I feel like Google is uniquely positioned to do something here bigger.

And I'm curious if you've been thinking about that. No, you know, deeply, you are right. Look, it's a, it's a complex moment, because, you know, when we say agents working at scale, that means many, many different parts of the economy interoperating with each other in some way, in a way that has to make sense for all the players involved, right? And I think the technology frontier will be ahead of the ecosystem, how it comes to work together in this case.

I definitely think a big part of this is why there's been a lot of momentum behind MCP or agent protocols or payment protocols, like what we did with UCP, along with a set of companies. And there's going to be a chaotic evolution there, hopefully working towards more interoperable standards. Right. And I think that's a very elegant way to do it.

Browser use and computer use is a very elegant way to work too, because that's what you as a user would do. And allows you to efficiently build controls for the users to be in the loop as well. So I think you have both ways and they're going to develop.

But there are moments where anytime I think if you solve it with better protocols, you know, I think it's a far more scalable approach, and it'll work anywhere and instantly. So I think but that will take longer time. Yeah. How do you think about protecting the interface in the way that you need to in this agent world, right? There's an argument you made, there's a new kind of agentic Google search that could be invented.

Now, agents don't need you. Amazon has blocked Muse, for example, you have a very large ads business that requires engagement. So what do agents do to those kinds of interfaces? How do you protect or not from that? The right answer longer term is you put users first and say, what would users want? And you want to try your best to be aligned in that direction, work towards that direction, right? And I think that's what will drive these experiences.

But ultimately, it has to work for all the businesses who are providing value to. So at any given point, you're going to reach equilibrium, and that equilibrium will shift. There'll be new players who view each of these moments as disruptive ways to do things differently. Like when the internet came, you know, you would sit and think, shouldn't the way real estate works, how people buy and sell houses overnight transform? Right.

But, you know, it's kind of played out. Not quite what people thought it would happen that way. Right. And I would argue even to today, most of it happens the way it used to happen, like, you know, 20 years ago. So there are real constraints in the real world. I think AI agents and AI are even more, they will be even more disruptive, but, you know, there'll be micro equilibriums which will emerge.

For me, from our standpoint, I think you embrace the innovation. You kind of put yourself in the shoes of users and you try innovate in that direction. That's how I would approach our product development. So I think that's what users would demand. I've talked to Liz and some of your other colleagues over the last year or so about this idea of when do Google search AI overviews and Gemini fully merge, if ever? Because right now you've got these two large scaled, you know, AI products.

Gemini, billion users, search billions of users. But relative, you know, different, but overlapping in some cases, AI experiences. Do you ever see them fully combining? Okay. Here's an example, right? Like when we ship personal intelligence, they work. I mean, you can turn on personal intelligence both in Gemini and in search and it has your context and so kind of works in a unified way.

I expect our agents to provide that unifying experience across these products. In fact, that agent declare rather than try to converge or, you know, think about different products and how they come together. I think the agent declare naturally ends up doing that, right? So because your agents, they're running in the cloud, they're running 24 -7, they have your context.

And so they can pick up things where you left off, right? So you can start a task in some place, you go to the other place, your agent has that context for you. So I think the agent declare offers us a chance to bring a unified product experience better than ever before. So that's how I think about it. Interesting. You guys are also starting to send data centers to space.

You've got Project Suncatcher, your first pilot with Planet Labs and SpaceX.

How's it been going? I mean, it went up. Is it still there? Is it burned up? What's been the early feedback?

It's been doing great. It's been doing great. It's still there. It's doing well. And we are, I think we'll get a chance to say hello to the TPUs, but, you know, which I'm really excited by. I had a chance to watch the launch here while it was happening. Look, I think when we take these projects, to me, it's no different from starting Waymo.

I still recall flying to the desert to watch the Stanley car race as part of the DARPA challenge. And at that time, if you try to envision where Waymo would be today, you know, it would seem like, you know, you're talking about something futuristic, sci -fi thing. Right. But, you know, 10 to 20 years is a long time, particularly with the inflection points we are in.

So I think we are taking the early steps, but we are deeply committed to the journey. And the team is a world -class team. And, you know, in a 10 to 20 year time frame, they're going to have amazing milestones. So that's what I'm excited about. What about, like, the desert, the ocean, all these places on planet Earth where we could put them now? I mean, why invest in space right now? If you think through from a first principle standpoint of energy availability,

you know, being in sun -synchronous orbits, getting access to 24 -7 energy kind of, you know, it's a pretty good reason. There'll be more reasons, you know, I think. We'll have so many opportunities to use the physical space, the constrained physical space on planet Earth. The data centers today look big to us, but they are not. It's just arbitrarily big to how they were, like, 10 years ago.

So it is tough for us to exponentially extrapolate our compute needs 20 years out. And so I don't think Earth would look nice if that's how you plan to do it. Yeah. It's a good point. Yeah. And so we would have other constraints here and so on. But also, by thinking about these technologies, it's like in the 60s, going to the moon, going to space.

You're pushing so many aspects of the technology curve. That's how you drive R &D. That's how you drive the future. So there will be, you know, it's the fundamental thing about a moonshot you realize is when you focus on something very ambitious, you invent so many things along the way, all of which end up being helpful. Which you found with Waymo and...

Waymo, our TPUs. Yeah. You know, because we were trying to build AI so early. Right. And the compute wasn't there. It's what led us to build TPUs. Right. Right. And so, and I think so, thinking about a technology curve early and pushing, I think definitely gives you those opportunities. And you said the word moonshot a couple times. It's literally a moonshot.

But is this, I saw that in the release too for it. Is this the next Waymo in that sense? Is this the next Alphabet spinoff kind of new company, Suncatcher? Yeah, we are definitely in a, compute is so integral to what we do as AI. You know, we haven't thought through this truck. May I view it as our, the future way we will scale up compute in a pretty fundamental way.

And so, but it's definitely a moonshot and, you know, and, but we are committed to doing that well. I would love to hear the strategy for Waymo because I can see it going a lot of directions. Um, and I'm curious, do you want to keep having these partnerships with the Ubers, et cetera, or will it be the fully, you know, vertically integrated Waymo fleet? That's one phase of it.

And then I have a question about just, you know, selling Waymo vehicles directly to consumers. I'm curious if you've, what those two buckets, can Waymo and Uber coexist? Uh, and then could I just buy a Waymo car one day and maybe rent it out myself? It's an area where we are so, so far from a zero sum construct. I think like, you know, it's just like, uh, it's so early.

The opportunity space seems so vast to me. So many applications for autonomous, uh, uh, driver technology. I expect Waymo to work in a myriad of ways, including with other partners. I expect the other partners to do very well as, as companies on their own too. So all of that, I'm very, very bullish on. The Waymo team, you know, they're very, very focused on scaling up what they are doing.

Uh, we just crossed, um, half a million, uh, paid rights per week. And, you know, we are, so the team is razor focused on scaling that up. Toronto launch is coming. Yeah. 10 new cities this year. Right. And so it's operationally, logistically scaling up the vehicles, scaling up in more environments, um, scaling up the, uh, customer service, the operations, all with safety being the, uh, number one priority.

And I think that is what the next 12 months is going to be more of it. International, other weather conditions, highways, and everywhere scaling up with this high safety bar. We have already, uh, driving with Alex Heath and Alex Heath

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possibilities of quantum are both vast in some ways uncertain etc but I genuinely see practical commercial applications in a in a five year time frame which we would get excited by you know we would be today like we provide TPU pods you know if I were sitting in a chair five years out, our Google Cloud team would be negotiating deals on quantum deployments at some scale.

It is incredibly exciting to me. It's another technology we've been committed to for a long time. You'll continue to see us be committed and be at the frontier. Sondar, thank you. Alex, real pleasure. Thank you.

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