Cheeky Pint - The history and future of AI at Google, with Sundar Pichai

Episode Date: April 7, 2026

Sundar Pichai is the CEO of Google and Alphabet. He sits down with John and Elad Gil to discuss Google’s resurgence in the AI race, managing a massive $180 billion CapEx budget, and why 2026 is the year of the supply crunch. They cover the constraints of memory and power, why he believes the US economy will grow significantly due to AI, and the internal cultural shift back to "Googley" optimism. Sundar also shares details on long-term bets like data centers in space, why he wishes he had funded Waymo even faster, and the small thing inside Google that still ignites his passion for building.Timestamps(00:00:18) The history of Google and AI(00:05:17) Speed and Search(00:12:12) Google’s AI comeback(00:27:03) Stripe network intelligence(00:27:53) Bottlenecks(00:41:25) Capital allocation(01:00:44) How Google works

Sundar Pitchai just passed a decade as CEO of Google. Alphabet is now not only one the world's biggest tech companies, but a leader in the AI race who plans to spend $175 billion in Kappex in 2006. Cheers. Cheers. Thanks for coming. Well, thanks for having me. A bit of history that people talk about a lot in the context of Google and AI

is the fact that Transformers were invented at Google, but then productized outside of Google, with mostly chat GPT and kind of that style of. product. How do you reflect on that now? I think it's actually worth talking about. It's a bit misunderstood. You know, Transformers was done in the context of a lot of like TPUs, transformers. We're all done to solve a specific product need to some extent, right? Like the team's thinking about how to make translation better.

In the case of TPUs, how do you, hey, speech rec works, where you suddenly have to serve it to two billion people. We don't have enough chips for it. It's like, how do you solve inference for it? I hadn't known that. Transformers were specifically... It was from our research teams, right? But they were guided by solving product problems.

And transformers were immediately used. So Bert and Mom, people underestimate how much, because we measure search quality so religiously, some of the biggest jumps in search quality in that period where search went ahead of everyone else was because of Bert and Mom. We built transformers and used it immediately in search to improve language, understanding, understanding web pages, understanding your queries, kept building better models.

We also started productizing it internally in the form of there were teams building something called Lambda. So obviously we weren't in the first to ship that. But I think it's less to do with like it was just research and we weren't applying it in a product direction. That I think is just... It's like you did this. this research, you then saw massive ROI from using it the way you intended, and then you didn't invent all of the products that were invented with it, but that's to be expected.

I would go a step further. We exactly even conceived the product, which is like Chad GPD, it was Lambda. If you would remember there was an engineer inside who thought it was sentient, right? So think of it as an early version of Chad GPD you were speaking to internally. So we even had the product version of it. In the multiverse somewhere else, Google probably shipped that nine months later or something like that. Maybe the, in fact, in the Google I.O. In 22, we launched something called AI Test Kitchen, and that was Lambda, but we had constrained it. Because internally, we didn't have an into-end version, which was RL-HF.

right? So the version I saw was a lot more toxic at a level. We couldn't have possibly put it out at that time. And also, I think as a company which had this search quality bias. And so, you know, we had a higher bar maybe, right, for what we thought was an acceptable product quality to go out. But it wasn't like we were figuring out how to get it out. I would also argue that even when Open AI Shep, they didn't. did their deal with Microsoft probably a couple months before. So you can look back and say it wasn't entirely fully obvious. I think they were lucky to also see it on the coding side with GitHub. I think maybe there was a signal we were missing. You know, coding side probably you were seeing more of a sequential jump than probably, you know, just on the language side. So maybe the jumps between GPD2 and 3 and later,

were more pronounced, you know, if you were using it for coding to. So, you know, you can point to things. But, yeah, so, but I think to answer the original question, I think it was less that research to product than a bunch of other factors. I also, I remember talking to some of the people who worked on chat GPT, and I think they launched at the week of Thanksgiving. You know, it was a little bit of a buried launch. It wasn't like this is a big, prominent thing,

and this is going to be an important part of our future. I think it was a cool sort of test case. It was really interesting. But, you know, the way I internalized these moments, if you're in consumer internet, you're going to have surprises. We were at Google then, Elad and I, there was something called Google Video Search. YouTube came out, right?

Just that we acquired YouTube. Or think about if you were in Facebook, Instagram came out. Nobody sits and says, like, you know, you don't look at those moments with that drama because Facebook just bought Instagram. Yeah, yeah. Right. But the way I've internalized is consumer internet,

people are able to think people are going to be sitting and prototyping and throwing out millions of things. I'm not trying to diminish anything, but I'm just saying you're always going to have these moments. You know, I don't think people like wake up in a garage and ship a better iPhone. Like that's not going to happen, right? But that's not how consumer internet is. So you just have to be conscious of that and internalize that. As I think about the AI race in 2026, one thing that strikes me is Google has, for so long had speed as the place that tries to differentiate.

And so original Google search was really fast and, you know, famously displayed the, you know, search query time within the results sort of showing off. And then, you know, Gmail fast search compared to the competitors at the time or Chrome compared to the competitors of the time. And now, I mean, I use all of the AI services for different things, but Gemini on TPUs is just so fast. And I'm curious how much this is part of the explicit, product strategy and how you think of it, or it's much more nuanced than that?

I've always internalized speed, let's call it as latency for this purpose, right, and as like one of the distinguishing features of a great product. And also almost always reflects the technical underpinnings of the product having been done well. There's a different speed which matters too, which is the speed of shipping and iteration and release cycles. So both are important. But you know, you talk about latency. There are, you know, it's easy to say, you know, you want latency, but you're constantly adding capabilities.

So the capability frontier is progressing. So there's some sense of how do you balance that? So that's where it gets more complicated. But to give an example, like search, you know, speaking with the teams, right, like they now have for subteams, like latency budgets. like in the milliseconds. You get 50% credit.

So if you ship something which saves off three milliseconds, you earn 1.5 milliseconds for your latency budget and 1.5 milliseconds gets passed onto the user. Right? And depending on what we think you're doing, some people may get a latency budget of 30 milliseconds or 10 milliseconds. You can use it.

But you have rigorous reviews against that. but that's how much we think it matters. And for context, I guess humans pick it up in the low hundreds of milliseconds, is that correct, in terms of where it actually impacts? That's right. Yeah, that's right. I think we've actually, you know, last it checked the dashboards in the metrics, we've actually improved search latency by 30% in the last five years,

but think about the functionality progression that's happened. And this is why in Gemini, you know, we deeply think about that Pareto Frontier of making sure, you know, the capability to speed and, you know, the flash models are like 90% the capability of the pro models. But much faster, much more effective to serve. And the vertical integration helps and so on. How do you think about the future of search actually? Because a lot of people now are talking about chat is a new interface. Obviously, Gemini is incorporated or Search has incorporated Gemini or AI results in the context of Google, but a lot of people are now talking about agentic flows and everybody's going

to have a personal agent who, instead of typing in a query, it'll go and do something for you. Instead of asking about trips, it'll go and plan a trip for you? What do you view as a future of search? Is it a distribution mechanism? Is it a future product? Is it one of N-ways people are going to interact? with the world? I feel like in search with every shift, you're able to do more with it.

And, you know, we have to absorb those new capabilities and keep evolving the product frontier. You know, if it's mobile, a product evolved pretty quickly, you're getting out of a New York subway. You're looking for web pages. You want to go somewhere. How do you find it? So you're constantly shifting the, you know, people's expectations shift and you're moving along. Yeah, if I fast forward, you know, a lot of what are just information-seeking queries will be agent-taking search, you'll be completing tasks, you know, many threads running. Well, search exists in 10 years? Well, you know, you may...

Or just evolves in this case evolves. It keeps evolves. You know, search would be an agent manager, right, in which you're doing a lot of things. I think in some ways I use anti-gravity today and, you know, you have a bunch of agents doing stuff. And, you know, I can see search doing versions of those things, and you're getting a bunch of stuff done. But I think the root of your question is, if you think of search as a prompt that is not longer than one line, returning a bunch of different ranked results, as opposed to just telling you the right answer or something. I think your question is, does that product modality exist?

But today in AI mode and search, people do deep research queries. Right. Right? So that doesn't quite fit the definition of what you're saying. Right? So, but kind of people adapted to that. Right? So I think people will do long-running tasks. Sure. It can be asynchronous. We all started, or the, you know, life started as unicellular organisms, and now we have this complex life. And so the question is almost like, does that former version or period I'm eventually go away?

And really, what was search becomes an agent and your future interface is an agent in the search box in 10 years. or N years is no longer. I mean, the form factor of devices are going to change. IOS is going to radically change. And so it's tough to, I think you can paralyse yourself thinking 10 years ahead. But we are fortunate to be in a moment where you can think a year ahead and the curve is so steep. It's exciting to just do that year ahead, right? Whereas in the past, you may need to sit in like Envision five years out.

I'm like, you know, the models are going to be dramatically different in a year. years time. And so I think riding the curve itself is exciting and so I think it'll evolve, but it's an expansionary moment. I think what a lot of people underestimate in these moments is it feels so far from a zero-sum game to me. Right. Like the value of what people are going to be able to do is also on some crazy curve, right? So once you view it that way, you know, like people would ask all these questions, right? Like, I mean, YouTube was done well since TikTok and Instagram is, you know, so you can, I can give many examples. I think, you know, the more you viewed as a zero-sum game, it looks difficult.

It can't become a zero-sum game if you're an innovating or the product is not evolving or, you know, but as long as you are at the cutting edge of doing those things, and we are doing both search and Gemini and, right, and like, you know, and so they will overlap in certain ways. They will profoundly diverge in certain ways, right? And so I think it's good to have both and embrace it. When we talk about kind of search and where it's going and things like this, I'm reminded of the fact that basically a year ago, kind of spring, summer 25, sentiment was very negative on Google.

The prevailing view was that, you know, search is cooked and, you know, going to have a really hard time. The core business model is under attack, blah, blah, blah. You know, Google was trading for $150-ish a share. and now people have realized that's silly. You know, Google has up and down the stack, whether it be applications or models or TPUs or whatever, as well as Waymo and YouTube and all the full bets.

What do you think investors as a proxy for kind of informed sentiment misunderstood this time last year? Because clearly there was some big misunderstanding. You know, it was obviously kind of very inward focused in that moment. So, you know, to me, it was very clear in that moment, hey, the Overton window shifted. We have, like I felt like the company was built for that moment. You know, the vertical thing, it's not an accident or something. It was a very intentful.

We were on the seventh version of TPUs. I remember it might have been 2016 Google I-O where we announced the TPUs. and spoke about we are building AI data centers. This was 2016. We were thinking about, you know, you know, the company was operating an AI first way. So we had deeply internalized the shift. So to me, we were behind in terms of frontier LLM models,

but we had all the capabilities internally, and we had to execute to meet the moment. But we had the exciting part was, when I look at it from full stack, we had the research teams, we had the infrastructure teams, we had all the platforms, and we had been investing intentfully in many businesses. And to me, I suddenly felt like, wow, we have this one common technology which can accelerate all those businesses. Search to YouTube, to cloud, to Waymo.

relies on progress in this so it was a very leveraged way to make progress so I understood it and to the earlier point of the discussion I didn't view it as a zero-some moment at all right and I felt like everything is going to scale up 10x right and there's going to be room for other people right and you go back you know Amazon has done well since Google came into the picture and Facebook So we underestimate the growth scenario of how all these things work, right? So, but we had to execute better as a company. So that's what I meant by.

I was more focused on that. Was there something that demonstrated to the outside world that, oh, they got this. Was it Gemini 3 that changed people in the lines? I don't follow all the timelines. I think the real model probably where people saw it was maybe Gemini 2.5. And, you know, I'm getting to the frontier on multi, particularly around multimodality. We made a bunch of, I mean, credit to the Google Deep Mine teams, right? They, I think we paid a bit more of a fixed cost up front, but we designed the Gemini

models to be very multimodal from day one. And so there were, there were areas, I think, I think we started, the strength started showing nano-banana was an example of it, right? so you were able to see it all together. But look, it's an amazing, amazingly dynamic frontier. I think there are two to three labs who are pushing each other pretty vigorously. You know, at any given month, we feel like,

oh, great, we've done this well. Oh, shit, there's a couple things we're behind, right? But I think the picture will again be dynamic in a few months. So I think the frontier is intense, as you would expect it to be. So that's how I think about it. interesting because when I talk to researchers, not at Google or at the other labs, one of the things that they commonly bring up is that they feel like the difference between the, you know, two or three other labs and the Google team is that Google is not as, they call it AGI-pilled.

In other words, there's less of a belief in AGI being right around the corner and the acceleration through it. And obviously, the folks at Google were thinking deeply about that. A, do you think that's true? And B, do you think that it all impacts some notion of what the future actually looks like and therefore what people are building against. Look, I think, you know, we probably have scaled our CAPEX from 30 billion to 100 and approximately 180 billion. It's like real money now. You don't do it if you don't think, you know, about the curve a certain way.

I view it as largely semantics maybe because we are a larger company with a lot of products that touches so many people at so many levels. Maybe the language of how we talk about it might be different. I think the founders were AGI built probably, you know, my earliest conversation. So I think this notion that at Google, we haven't understood what AGI is, or Demis and team or Jeff Dean and team, like, you know, at one point, I, I don't know, Demas, Jeff, Ilya, Daria, you are all there.

So he can... I like that retort. It's like, hello. Have you been paying attention for the past 20 years? Yeah, so that doesn't make sense to me. I think some of it is,

you know, if you're a younger company, you know, or you were more a pure research lab, you know, maybe headquartered in San Francisco. Yeah, yeah. There are a lot of small attributes

which can probably make a difference. But I don't think at a foundational level, there is a difference in outlook on what the curve is, right? Or how we internalize the technology. Look, I think even within the company, there's a set of us living on the bleeding edge, firing agents, seeing what these things can do, see the agents pick up skills, do stuff,

and also look back three months ago what they could do now. and they are living that exponential internally. Right. I think you're both right, where I agree, you can kind of point as the history of Google. I think what a lot's getting is like a feeling, where I saw a tweet go by that, someone was saying,

what you have to realize to explain what's currently going on in the valley is that every tech executive has severe AI psychosis right now and is spending a huge amount of time writing code and talk to AI and things like that. I thought there was a funny take and not without any truth to it. And I'm curious, what were your feeling the AGI moments along the way of the recent, or to what extent do you have AI psychosis these days? My first feeling the AGI moment was 2012 when Jeff Dean demoed the earliest version of Google Brain. This is when the neural

networks recognized a cat, right? So that was 2012. I went with Larry to the DARPA Challenge It might have been 2014, I think I need to be exact about when we went there seeing the cars drive there. Demas demoing the earliest versions of the models

having what we would call as imagination. So there were many moments like that so it was obvious that technology is progressing. In terms of living now and kind of having a visceral feel for it, I think the closest I would say is if you're co-

and you give it a complex task and you never open the IDE and you're in some agent manager world and you see it kind of do it, you know, and how powerful it is. So, you know, if you can call it field AI, so there are moments like that. Yes, yes. I've been a little hobby project recently and after a while I was like, oh, I wonder what language it's using. But that was like a detail that I needed to ask it about after everything was up and running. Yeah. He feels like magic. Yeah.

Yeah. So, you know, moments like that for sure. Yeah. And but the, but the slope of the curve is what surprises you. Yeah. Right. And you're improving it on so many paradigms. It feels clear that there's going to be progress ahead.

Right. So when you talk about the visceral feel, I feel like one thing that's important at tech companies and every CEO thinks about this differently is how you stay connected. the product experience in everyday users because tech products are so abstract that it's easy to, you cannot just manage through reports from teams and slide decks and spreadsheets. And so Tony Hsu is talking about how he still works as a door dasher

to stay very connected to that experience. We do at our little weekly all-handsbeaver occurring segment of just walk the store where we like click around in the dashboard together and we're tripping over like, why is that modal there? And that's a bit confusing or whatever. just so we're like collectively using the product. I'm curious how it works for you and how at Google you ensure that you're staying connected

to the experience of using the products. Other than you use like Gmail and everything every day. Oh yeah, like you know, dog footing like literally internal versions. I do block time like to kind of use it intensely. So like kind of focus time to do it. And so that helps. like even just two weeks ago I was stretching in the gym

and I had the phone with Gemini Live and so I'm like I'm going to talk to it for like the entire 30 minutes on like one topic so you do those things and it's some of it works some of it is frustrating but you kind of learn a lot right

like so I force myself to use it in those power user mode ways and and stay in touch that way X helps because sometimes you get the raw feedback. Thank you for fixing the Google calendar thing. That was so good. Well, the few more we have to fix, but thanks for flagging it.

So, yeah, X helps because you kind of get the raw comments and I tried to follow it directly. But I'll tell you what has helped. Internally, like, I would go fire to our earlier part. Like,

I would query in anti-gravity, just our internal version of anti-gravity, hey, we launched this thing, like what did people think about this, tell me that the worst five things people are talking about, the best five things people are talking about, and I type that. Now that brings it back.

So has my life gotten easier? Yes. So in the past, I would have to spend a lot more time trying to get a sense for it. Now an AI agent is helping me in that journey. So you can get, you know, well, how much should I be spending first-hand to get that field? This is actually leveraging these tools.

Yes. So even I'm going through a journey day, right? So I'm trying to adapt to this future. I guess there's, you mentioned, A, that it's not zero sum, B, there's all these productivity gains people are seeing. And if you look at a lot of prior technology cycles, it took a while for the internet or for mobile or for SaaS to show up in actual GDP numbers, right?

In the context of AI, we're seeing it from a data center buildup perspective, right? That's driving part of GDP growth. How do you think ahead in terms of three, four, or five years? Do you think the U.S. economy is bigger because of AI? And if so, how much bigger? Look, for these returns to make sense, somewhere it has to, you know, how long was it before? I think it was maybe from Sequoia, someone wrote and saying people are investing this much. Yeah, they're compared the Capax to the...

Yeah, and just might have two and a half years ago. It was a talk and like saying it doesn't make sense because you would need to return at that level. You're probably 10x days. Yeah, yeah, yeah. Since that moment, I need to go look at the numbers again, right? So at some point, you know, it has to reconcile. To be very clear, you know, we are supply constrained. We are seeing the demand across all the surface areas.

I actually don't have any doubt that this is a massive market and outcome. So my question, and I think there's a lot of things that people misunderstand. So, for example, people often talk about software engineering budgets, and then what proportion of that is token versus salary. And to some extent, I think that market, has been so demand constrained for great software engineers, that suddenly adding supply can 10x that market. In other words, I think the market for software engineering and coding

is dramatically bigger than anybody thinks, and it's the wrong metric to say, you know, token budget versus engineers. So I actually think it should grow a lot of things. I was just sort of curious of your view of like, how much growth do we think is likely actually to come of this? I actually wasn't doubting at all, sort of cap-backs versus outcomes or, you know. I see.

Yeah. Look, I mean, going back at the internet and looking at GDP growth, It doesn't quite capture what we all feel with the internet, right? And so maybe we would have had negative GDP growth without the internet. Consumer surplus. Yeah. So, you know, it's tough to look ahead. I do think there are natural dampening mechanisms in society at various levels.

And the obvious ones being, you know, the compute build out is a different curve than the rate at which we can improve the models. Right. So you're already dealing with a more constrained curve there. Then how do you diffuse it into society, right? We are doing this with Waymo. Right. And you can make Waymo safer than human drivers, but you know, but you have to be careful at like the pace at which we are rolling out, etc. So sometimes, you know, how do you diffuse it through society responsibly? There are constraints in all these layers, right? So, but I think the U.S. economy is so.

much larger than it was 10 years ago. So to grow that, even at a half a percentage point higher, then that's a massive contribution. So I expected to play out that way. Listening to Sundar is a powerful reminder of what it means to operate at true internet scale. When it comes to commerce, though, most businesses are forced to make critical decisions in a vacuum. They view the internet economy through only their own lens,

using only the data that exists within their four walls. This is where Stripe comes in. Because we process $1.9 trillion a year in global payments, we have a panoramic view. When you build on Stripe, you're plugging in to that network intelligence, from verifying a user's identity to stopping fraud before it starts, to streamlining how businesses connect and knowing what payment methods work best locally. We've built a system that puts 1.6% of global GDP to work to protect and grow your revenue. So, if you want to use the power of Stripes Network Intelligence, come see what you can build here. You reference the supply constraints, and I think that's a really interesting defining aspect of 2026, basically, where you said 150 billion in CAPEX, 180?

We have said it'll be between 175 and 185. Okay, so 180-ish billion of CAPEX. And what's interesting to me is that Google could not spend... $400 billion in CAP-X if it wanted to, because the memory isn't there and the power isn't there and all these components. So you just tick through... We can find a number of electricians we would need. Exactly. So I'd love to hear just your overview of the various bottlenecks.

Look at some level, you have to work back to actual wafer capacity or something like that, right? So there are deeper ground troops, right? I think so wafer starts. It's kind of a fundamental constraint. I think power and energy are more solvable. Permitting and actually working through a regulatory environment might be a constraint, right? So the pace at which you can do things. Even though there's lots of land in pro-growth, you know, Texas or Nevada or Montana, just maybe not enough?

I think we're making tremendous progress. I think for the U.S., I think it's a particularly important thing. you know, you're in awe of like how the pace in China, how fast they can build things. So I really think we need to learn to build things much faster. Like you almost have to shift your mentality to think about what would it take to do things 10x faster, right, in the physical world. Construct 10x faster. But I would worry about that as a constraint. I think, you know, there could be growing resistance.

And so, you know, it's not as simple as, like, a few people deciding you want to build faster. The data center moratorium. Yeah. So I would say if it starts, the ability to permit and do things. And I do think there's a lot of good work being done from the government on. I think people realize you need to do these things better. Then comes critical competence in the supply chain. Memory is a good one.

We are constraining those things in the short term. Everyone will respond to it. Yes. But I think all of us running companies, regardless of how AGI pill you are, then comes this error bands of like, you know, how bullish can you be? What's the margins you can afford? Because there are extraneous factors which can go wrong in the world, right? Which are outside of control.

So everyone is making those adjustments. Those are all constraints, right? And constraints. So I think, but that's where I see the constraints. Is memory the biggest components that you think about? Memory is definitely one of the most critical components now. And you said in the short term, do you think just people who ramp up supply and so high prices will take care of us? There is no way the leading memory companies are going to dramatically improve their capacity.

So you have those constraints in the short term, but they get more relaxed as you go out. But I do expect all of this to constrain. By the, I think it will push a lot of innovations on, we will make these things 30x more efficient. So all that is happening simultaneously as well. Does that enforce a oligopoly market? So if you actually look on the model side, because if you look at a lot of the views of models

and how they're going to improve, a lot of it is going to be both self-improvement. So the models will start writing more and more pieces of themselves, do more data labeling for themselves, etc. So there's a musical chairs game of who has right now basically? Exactly. Who has compute right now and how much can you actually scale relative to overall industry capacity?

And if everybody is roughly prorata up to some number, you've effectively put a ceiling on how much far ahead somebody can pull versus everybody else. Do you think that's a correct statement or an incorrect statement? I think it's a reasonable framework to think about it that way. But there are things which are, you know, I'm coming here as we just shipped Jemma 4. And it's a really good open source model.

I mean, the Chinese models are very good. But I think outside of China, you know, it's a very good open source model. You know, the frontier to Gemma 4 is both huge and not so huge in terms of time. Like, Gemma 4 is based on Gemini 3 architecture. Right. You know, it's a very weird thing, right? You're talking about a set of weights, which can fit on a USB stick. Yeah, yeah, that's right.

So it's like a really... you know, crazy, it's not like a SpaceX rocket. I'm always shocked that you run a data center for months and months and months, and then your output is a flat file. Literally, it's like having a Word doc or something, and that's remarkable. It's amazing. So there are these unique attributes about this, which makes me challenge those frameworks and say,

you know, how should we think about this? But I think it's reasonable, at least on the inference side, what you're saying is a very reasonable way to think about it. Think about it. But I do think, I do think everyone is trying to figure out how to blow through the capitalist incentive to break through these constraints. Yeah. You know, it's immense. But as you say, there's only so much memory in the world.

So like no capitalist incentive will really solve 26 or 27 memories of play. That may be the era where you see more diversions and moments. You know, and remember, that has to balance with wafer capacity. increasing, you're being able to permit those data centers. So this constraint may be less severe than it appears, right? So you have to kind of envision the total square set of like all the things that you need. And think it through, right? Are you-

Are you- Are you- But again, what's interesting to me is that plausibly people would invest beyond the current CAP-X, But we're now just running against 26 and 27 real world constraints. It's a little about the straight of Hormuz. You can have whatever price of oil you want. Ultimately, if you take 20 million barrels a day out of the system, you need to destroy 20 million barrels a day of demand.

And it's kind of similar with memory where, like, ultimately some people have to not get the memory if they want. But there are other constraints, right, which, you know, take security as a constraint. And these models are definitely, like, really, going to break pretty much all software out there. Maybe already we don't know, we sit here and speak.

Do you really think all software there? Because like SSH people have been trying to break for a long time. Do you think like... I'm talking about just things, just regular software, large platforms, right? How many zero days? You know, so there are constraints here in the system, right?

You just can't wish away. Somebody was telling me the black market price of zero days is dropping because the supply is growing due to AI, which I thought was a really interesting. are really interesting. Which I... Not at all surprise, right? And not at all surprise.

So, but how does it practically diffuse through society? What are the implications of it? Right? Like, you know, and so, and so there are parallels, I think. So I think there could be hidden constraints. Yes.

And there could be shocks to the system, if you will. But having said that, like, you know, I genuinely think there's a lot of upside ahead. Some of the constraints maybe are helpful. Yes. Right. I think constraint inspires creativity. Forces a compaction cycle where we get more efficient.

Forces maybe important conversations to be had which otherwise wouldn't happen. Right. I think, you know, just on my security point alone, like I thought about we are going to need more coordination. Yes. Which is not happening today. There will be a moment of, you know, it could be a sharp moment, right? And so all those things, I don't think you can wish them away. Yes, yes.

Right, yeah. Actually, related to that, Google does have an amazing portfolio of things that's both built and bought in, too. From a ownership perspective, you know, you own a reasonable amount of SpaceX. I don't know the exact amount, but I think it was 10% way back when, Anthropic, 10ish percent. The majority of Waymo, which is like an amazing thing. And then internally, obviously, there's this enormous swath of amazing technology that's

been developed. We talked about AI and Transformers. There's TPUs. Obviously, Waymo is another one of these things. There's quantum. You know, you just released a very interesting result there. Are there other hidden gems that people should know about or that are especially interesting or that may have very big impact in the future?

Or people maybe underestimates. Look, we are constantly trying to take these long-term projects, which when you first announced them slightly marginally looks ridiculous. You know, like we're in the earliest stages of thinking about data centers in space, right? but to your earlier discussion around constraint inspires creativity. But if you take a 20-year outlook, right, where are you going to put most of these data centers? Really hard problems to solve.

But those are examples of projects we think about today, which are way more in 2010. Quantum itself is one of these projects. we are in a deeply committed way making progress there, and I'm excited about it. Where do you think quantum will have the biggest impact? Because mainly people talk about molecular modeling, they talk about cryptography. There's quantum-proof sort of cryptography that people have been developing over time. On the molecular modeling side, it actually looks like the deep learning models tend to be very good at that in certain circumstances.

I mean, you all pioneered that with alpha-fold. Do you think quantum will actually matter? And if so, where do you think it will have the biggest impact? Look at an abstract level to me, it feels like to simulate nature more and more. Like, you know, like given it's inherently quantum, you would need quantum systems to better simulate it. We may get there with classical computing techniques in a surprising way or get at it with enough compression and if abstraction, it may work. But I fundamentally felt like quantum would have an edge there.

And I don't know, we still don't understand the behavior process for fertilization. I mean, you know, it's probably your background, going back to what you did in college, more. So my, you know, my instinct tells me there'll be, you know, simulating weather, simulating, you know, reality, all that. I think quantum 11 advantage. I think the way the history of technology is you get something to a scale where it works

and then you use it and people's creativity on the top find the applications. So, you know, I mean, I always give this example of mobile phones plus GPS enabled Uber. Like there's nobody who was working on phones who would predict that as an outcome of this platform shift. So, you know, confident quantum will have many, many, many applications if you can actually make it work. Yeah. So that's how I think about it. Mr. Sir, we interrupts you? You're talking about kind of your favorite of the Google further afield initiatives.

I think we're making, you know, the GM team is deeply thinking through robotics. Right. Right. And, you know, robotics is an area where we were too early as a company before. It turned out AI was the missing ingredient for a lot of ideas, maybe 15 years ago or 10 years ago. But, you know, the Gemini robotics models are sort of on spatial reasoning, etc. So we definitely have state-of-the-art models there. And we are partnering back in an ironic way with Boston Dynamics and agile and a few of the companies.

And in a determined way making progress. And there are extraordinary startups out there as well. But so we are investing in, you know, I spoke about quantum, data centers in space, drone delivery with Ving. You know, I think we are scaling up Ving where in some reasonable time period, like 40 million Americans will have access to a Ving delivery service, right?

And I'm not talking years out or something like that. But again, these are all like methodical compounding when you take these long-term projects. So, you know, we are committed, isomorphic. Yeah, isomorphic is very exciting. You know, think about being focused on these models in a targeted way to improving all the possible steps in drug discovery. And even though you have long poles like phase three trials, et cetera,

getting there with a much higher probability of success. Yeah, I think it's definitely the smartest approach I've seen in terms of the different biomodels and really thinking about the broader swath beyond just the molecular design, which is I think where most of them are stock. Yeah. It seems very smart.

Can I ask, I'm curious, how capital allocation actually works at Google? And what I mean by that is, you know, the idea good capital allocation is about internalizing that the opportunity cost for capital and putting the cash that a business generates towards its highest and best use.

And in the toy example in a business school book, you know, maybe you're Boeing. And we can either, you know, we have this cash that our business generates and we can either go bid on the next defense contract and we'll invest this much in R&D dollars and we model this much revenue from the contract. Or we can go develop a clean sheet commercial airliner and we'll put in this money and we'll model this kind of thing. It's like a 16% IRA versus a 19% IRA. Okay, I prefer the 19%.

In Google's case, the projects are extremely heterogeneous where it's like, okay, we can give the YouTube team more funding so they can go, you know, improve the recommender algorithm and therefore 10 months size increases and so does monetization. Or we can give the Waymo team more funding so that they can actually get to market faster or scale up faster. Or we can invest in this new AI approach that might, you know, pay off in five years' time. And so I'm curious, if you are trying to put capital towards the highest and best use and you're ultimately comparing, how do you compare initiatives that are so different in nature and so different in payoff curve shape?

This is the most John question ever. I need to know the answer. You need to throw an R.C. It's a good question. Look, I feel it today more than ever, ironically, because of TPU allocation. So in some ways, I feel even way more needs TPUs right. So, you know, computers made the question, ironically, much more front of mind. By the way, of all the things I do, I'm really looking forward to how AI as a companion at least gives inputs to this task.

You know, and I think once we can actually get all the data connected and flowing through, the models are already capable. It's more getting all the data unlocked. I think will be helpful. So I feel it there. Historically, I think at Google, one of the advantages we have had is sometimes we make these decisions very early in the site. So it's almost like going back to that route, it's a deep technology orientation.

And, you know, we actually think about the question you were asking a lot of bit ago about, like, what are those longer term things? And so I think thinking at that stage, it's easier because your initial funding amounts can be smaller. But then, like, you know, you stay committed for the long term,

but you're making sure you're, like, making progress in a deep way. So as long as you're seeing that underlying technology, Like take quantum, for example. How do we judge it? Like, we're judging the underlying, like, you know, so you have goals around, you know, what logical qubit, error corrected,

large stable, logical qubit threshold by when you're going to get to and is the team able to do that? Right. So I think you assess it that way. So one of the, I won't say advantage, I think one of the ways we have thought about it and we've been disciplined about, or at least to me, matters a lot,

is to make those early technology bets in kind of a deep way. And so that's helped. But on a constant basis, look, I always view it as you have to assess the long-term value of these things. So it's almost like in some intuitive way you're thinking about the option value and the time of something five to ten years down the line. And you assume like a crazy growth. And think through whether those decisions make sense. So the TPU investments have been great that way, right? And, you know, we've steadily invested in that.

Waymo was a great example where I think we increased our investment two to three years ago when the rest of the world got pessimistic on it. But now there's some of the people are backing off. It's very magical. It's such a magical experience. I take Waymo now every day to work when I can. But I think Waymo is a good example of this, like this question I have, which is Google does cut projects, and there's various things.

things you've tried where you said, you know, we're actually not going to fund, you know, this part of X all the way, or, you know, we're not going to, you know, we're going to retire this product that's not working. But Waymo, despite the fact that it was a long road from a compelling demo to commercial service in market, you guys didn't lose the faith. And so what was it that you were seeing? Is that a qualitative decision or a quantitative decision? How do you decide that we're going to cut Loon but keep Waymo? I think it's to do with that some kind of quantified you look at the way more driver that's underlying technology which you know how does the software drive the car and the progress in terms of safety and

reliability so it's a long-running task how safe and how will you do it and you follow that curve and you predict or you set goals where you want to be and how you perform against those curves I think the team has been phenomenal there have been maybe phases where they it didn't progress but those are the Sometimes you need to kind of like, you know, you have confidence in the quality of the team to break through those faces. But I think the more you're able to evaluate things at that deeper technology level, I think you tend to make those decisions better. Or at least that's how I have tried to do it. One argument I've heard or one discussion I've heard made about Waymo is that a lot of the huge gains that have been seen recently because it used to be this hand-map heuristics of like how do you deal with education?

cases of driving or something happens, how do you respond? And a subset of those were almost hand-drawn out for the cars to follow. And so that kind of a narrow set of things that it could do. And then really the breakthrough was moving to end-to-end deep learning a couple years ago as this big transformer wave was happening in general. Do you think if Waymo had been started five years ago, it'd be at the same place as it is relative to having been started 15 plus years ago? Just given that that's the breakthrough that's kind of propelled it forward. Look, I think, you know, we spoke earlier about robotics. You can think about Waymo's a robot, right?

I think people who are starting robotics in the last three years, by definition, would be making faster progress, maybe. But I think Waymo is such an integrated system. There are aspects of it. Not quite like, you know, like, you take something complex like TSM or SpaceX launching things. You are talking about system integration in these things in a very complex way. I think Waymo has hidden aspects of that, which the time of how you do it,

the craft of it matters. But having said that, I do think the end-to-in approaches are going to be an accident in these series. Because just having a team arguably was a huge benefit to Alphabet and Google, right? I mean, just the fact that you kept investing in it and then it hit a moment in time

where this technology liftoff was more than worth it and was very smart and forward-thinking. I just think it's interesting to ask how does that apply to other domains? Because to your point on robotics, it seems like with robotics will potentially have a different history where you can move very quickly now.

Do you folks think about re-internalizing hardware again, or is it largely going to be a partner-driven model to bringing this stuff to the world? I think we'd keep a very open mind. My lesson from Waymo and on the AI side with TPUs, et cetera, I think to really push the curve well, particularly in areas where you have safety, regulatory, everything, you want the first-hand experience of the product feedback cycle.

So I think having first-party hardware will end up being very important. That's how I would say right at this stage. That sounds. So I have two more capital allocation questions. Can you make the case that Google has historically been under levered where Google has historically carried a strong-neck cash position? And given that both Google has more ideas than it knows what to do with, like it's just brimming with good ideas,

and just the core business grows very durably. And I think Google clearly has a very good understanding of that core business, and it has grown at a higher rate than Google's cost of capital. As you look back on it, should Google have been more leaned in and said, okay, we will be willing to have a leverage position that's slightly more aggressive than strongly net cash, and we will put that. that towards new initiatives or just buy more of this core Google business for Google shareholders or do more minority investing, which again, Google seems to have been

best in CloudSat. It's a great question. For example, if Waymo had reached this point earlier, I think I would have invested the capital earlier. So to some extent, I think you were judging it by, like, you want to be goods towards of capital. So to the extent you're bullish on ROIC. you want to invest every last dollar you can there.

But to the extent, you know, you have excess where you don't think. I mean, this is why we've invested in other companies too, right? Even if not, but we've always thought about it with the lens of being good stewards of it. Yes. We felt our investment in Stripe was being a good steward of our capital. SpaceX, right? You know, SpaceX and Anthropic and so on. So I think now with the AI shift, there are more opportunities on which we can deploy capital

in a good way. And so we are doing that. Yes, yes. But I think we always had that mindset. Yes. But I would have been glad to invest more capital in Waymo earlier, but we weren't at the level of maturity needed to do that. There was a point in Waymo from a safety standpoint, you know, we did approach Waymo safety first.

Yeah. And it just, it wasn't the right thing. to do. Do you feel like you cannot point a project where they would have gone faster had they gone more capital sooner? They just needed a, they had a natural ram. I wouldn't say that, but I think in generally at least, we might have gotten the decision wrong, but our approach at least was like to say, if we got excited about something and had the conviction, we were willing to come in the capital to see through. My other capital allocation question was historically at tech companies, the large majority of the R&D expense was the people walking around the building.

And, you know, headcount was managed through a very tightly controlled process. And indeed, as you thought about kind of allocating R&D effort, it was really allocating kind of highly paid people to go work on the challenge. And the tech costs were, unless you were doing something very computationally expensive, which obviously Google did in place, you know, Google Books or something. But broadly speaking, the tech was, an afterthought compared to the cost to the people. We're now going to a world for, as you say, that's not the case, with, you know, TPUs and how you allocate that.

Just at a very concrete budgeting level, how does that work inside of Google? Like, are you, do you have an overall TPU budget for the company? And then when you are giving a project resourcing, previously you gave us, you know, a certain headcount budget, and now you give it a headcount and a TPU budget? Are they the same budget? Just how does that work when you're doing it? doing a quarterly review or an annual review. Look, we've always had a compute budget. Ask me for a friend.

Ask me for a friend. Now, we've always had a compute budget, right, you know, even in classic compute. I would say with ML, and we use both TPUs and GPS, by the way, extensively. But ML compute planning is, we're super thoughtful about headcount planning too. But we've always had to plan that. And ML compute, we've gone through phases where they've been been easy and then there have been faces where we've been constrained as a company.

But now it is really acutely constrained, right? So you spend a lot more time. I at least spend a dedicated hour a week thinking about that question at a pretty granular level. So I will know by projects and by teams, the compute units they are using. right and you know or at least I have that information and I'm looking at it and assessing it and in some ways it's a really important thing to be doing right now I feel so the scarce resource is compute in a lot of cases and so you're ensuring that Google's precious compute resources are being spent on the most worthwhile that's right initiatives yeah how do you think about that in the

context of GCP and Google Cloud because there you're actually allocating the compute to others instead of for your own purposes and given the constraints in the system, how do you think through that differential allocation? Look, Amy, plan ahead, right? So when we do the forward planning, you know, the cloud team is forward planning, and they're putting a plan in place, and, you know, and so you're funding that, and you're doing that for our internal needs. You forward plan, and as part of that, you're also signing long-term commitment,

to customers. Anything we commit to a customer is sacrosaned, right? So these are contractual commitments. So, so you know, solve a lot of it with planning. And, and so there are, when you plan, we are all in a constrained world. So I think the cloud team would say they don't know the compute they want, et cetera, et cetera. But you will solve it with planning ahead. Speaking of Google Cloud, I have my product request that I've been saving up for this section that I know you're looking forward to. You could have posted it on that. Exactly, yeah, yeah. But no, I'll say one thing that works really well is the GCP MCP is awesome,

where your AI can just interact programmatically with Google Cloud, and I guess you guys have exposed almost everything except like the core, you know, permissioning stuff. And I feel like in a way part of the curse of Google Cloud has been there is so much functionality there that I'm sure you occasionally hear from people that it was like a little hard to navigate that you log in, you have to create an organization, a project and whatever, and find the right services or whatever. And now all that doesn't matter.

And so you just say, you know, hey, go add this Google Cloud functionality. And so that is something that actually it feels like Google Cloud is really benefiting from like the, it is so broad and there's so much functionality there. I mean, we have a little bit of this problem with Stripe where as we add more functionality to it, just the right way to navigate this big product surface area is an AI that's read all the API docs for you. So that's working really well. I mean the promise of AI being this orchestration layer, like for anything you think,

about. To my earlier question, even internally within an enterprise as a CEO, it's not like you don't have all the data, but how do you get it in one place and you see it in the past that would have meant one more big ERP-ish project to go connect all the data sources, et cetera. Again, like AI being this orchestration layer in a way that makes sense for the end user, I think it's been delightful to see. And the bigger the product surface area, the more that benefits hits you. And again, we've seen that to some extent with Stripe, but I feel like, with GCP, it must be just a massive effect. I think we could do a lot better.

But you're right, it's an immense opportunity, I think. Yeah. I've been really happy with it. Okay, and then that gets to my products. Did you bring product suggestions for something? You go first, yeah. I wanted to hear about it. What's interesting me of a kind of open claw and the product market fit of things like

that, is they're allowing stateful AI for consumers? And if you want to say, you know, the classic, you know, round up the daily news that I'm interested in and send it to me each morning, or just something that involves persistence that none of the popular, you know, or like mainstream AI apps allow persistence. Is that common? I think that actually, look, I think you want to give users capability where you have persistent long-running tasks.

Yes. In a reliable, secure way, you know, you have to think through things like identity, access, et cetera. But I think that's the future, that's the agent future. And bringing that for consumers is like a bit of exciting frontier we are looking at. Yeah, this is one of mine too. This is Dreamer, which was the former CTO of Stripes company. They just got bought back Meda, I think, did a very good version of this. It's a very early kind

of view of. Yeah, they were making custom software, including persistence, but also, you know, you could just kind of spec out. They kind of make your own little app. Exactly, yeah, yeah. And they made that very easy to use. But I feel like when people have this experience, there's a surprise and delight moment, and it's just interesting to me that... Look, I think effectively the consumer interfaces are going to have full coding models underneath, right?

And the right harnesses and like the right skills and the ability to persist and run somewhere securely in the cloud, locally and in the cloud. So all those primitives are coming together. And so what developers are like today, I feel like there's 1% of the world, maybe 1% 0.1% of the world who's kind of living this future, right?

They are building stuff for themselves, but bringing that to mass adoption is a very exciting frontier, I think. Okay, my other product suggestion is, sorry, you have to endure this part of the interview. My other product's idea is, for some reason, I don't know if this is your lived experience, but certainly my lived experience, that search in Google Docs is so much harder than, say, search in Gmail.

And obviously, like, they're both equally good search engines. But I think what's going on is keyword search works reasonably well for email because you can probably remember a unique set of keywords for that email. Whereas what always happens, at least to me, is like, I want to go back and look at the 2026 budget. It turns out if I search, you know, Google slides for 2026 budget, neither of those words is, like, particularly unique in the context. of words that exist in, you know, PowerPoint's as Stripe, and so I can never find the exact right one. And I'm curious, does Sunder Pichai also have this problem? Somehow, I haven't felt it as acutely as you're describing it,

but when you describe it, it resonates well with my experience. I'm literally playing through the person to whom I'm going to play this segment of the conversation. I know exactly who I'm going to go talk to, the people are working on it. I think we can make it a lot better. I think, look, the AI integration into these services, including Google Docs, I think you will see sharp improvements in the coming months ahead. I think we all did the first versions of it where you just put it in somewhere. But I think, you know, over time, what all can you keep in context? What can you cash and what can you really bring to bear?

I think we can make a lot of progress on. So I think we can do a lot better. Okay. Great. We have a good extra putting up with. A lot of companies that I'm involved with. Even ones that were started reasonably recently have had dramatically shift their workflows relative to product development, engineering practices, who they even think of should be on the design team

and the capabilities of that. Are you revisiting all that at Google? Are you rethinking it? Has there been big shifts in workflow or other aspects? The way I would say it is, you can think of it as concentric circles. There are some groups that in Google who are shifting more profoundly. And so for me, a big task is how do you diffuse that to? more and more groups, particularly in 2026. Some of it, we couldn't do it early because it breaks so

often that like, you know, almost like you see this promising new world, but it's kind of semi-broken. But this year, I feel like the curve is shifting pretty dramatically. So I can see groups, particularly I would say GDM and some of the Svi groups really change their workflows, right? And, you know, they are using, we call this for some strange reason, we have a different name internally than externally of the same product, but it's jet ski internally, which is anti-gravity. And you're living on it, you're living in an agent-manager world, your workflows, and you're kind of working in this new way, right?

But just last week, we kind of rolled it out to the search team. Right? So we're constantly pushing that. You know, in a large organization, I think change management is a hard aspect of this technology diffusing, which may be easy for a small company, right? You know, you can quickly switch over. Can I lay a few problems I see when it comes to actual diffusion of AI in industry? And I'm curious how and when you think will solve them.

Because as I see it, we have a big intelligence overhang. Like, the AIs are now amazing in terms of what they can do in the abstract. And if you look at how AI-native company is, or just kind of how much it uses that intelligence, there'll probably be a shortfall. And the problems that I see are something like, one, it actually takes a while to get good as an engineer as prompting your AI well. And you can prompt an AI better or worse to write code. Then there's a lot of, say, stripe-specific prompting in our case to know which tools to use. And so there's kind of the general being good at prompting.

and then there's a stripe of being good at prompting. And then, of course, you have the fact that it's hard to share an AI-generated code base because you have a blast radius, and you're just changing so much in the turnover of the code is high enough, where maybe you're rewriting it several times before you ship, that it's kind of hard for many people to collaborate on the code base versus before when the code velocity was slower. And then as you go outside of engineering,

the big one I see is access to data, where you'd like to have your agent go, how many times a day do people at companies around the world say, hey, what's the status of this deal? And that is like information that the company knows and should be agentically answerable. And we actually have some cool stuff at Striper I was seeing where you can actually answer that pretty well. But with both habits and access to data and as you get into a bigger company, you know, the permissions engine of who can actually get access to this data, that all needs to be written. And then you get into role definition where kind of like you were saying, EngPM design kind of stems a little bit from a prior year, and you may want to, at least in some

cases, merge those roles a little bit as AI gets better as all those, and you've got a product doer. Anyway, that's kind of my characterization of in 26, the models are capable of, you know, this, but we're only doing, we're only using them so much. What do you think that adoption of the intelligence looks like? Look, a lot of us are working on. like, you know, literally what the Gemini teams, the Gemini enterprise teams and the anti-gravity teams, they're precisely working on these problems. This is the roadmap you're talking about, right?

Like, you know, and that's literally we are using it internally, running into these barriers, kind of working pastures. So that's the products that are shipping. We are still diffusing it because what you do is people, as part of using it, like if you're the SRE team at Google, you suddenly find portions which you can create an automated workflow. And so that's happening in like these spots, right? But doing it more systematically when you develop skills, how does it get centralized, how is it available to the models and for everyone to use. Identity access controls are like real hard

problems. And so we are working through those things. But those are the key things. But those are the key things which are limiting diffusion to us too. Right? And we take security a lot more seriously, and so we have to. So that is another layer on top of all these things, the cost of mistakes when you're running these services. And so we have to work through it. But I think because of it, when we solve it, I think we will bring it in a more

robust way, which will help. So I feel like we're going through that fixed cost right now. But you will see this jumps of what people are able to do when we bring it outside. certain others are doing it too. And in a more robust way, the models are improving. Google reforecast its business a few times a year, formally, I presume. At least we do it, Stripe, or we set a budget for the year, and then three times year we produce a formal re-forecast.

And when you think about it, a re-forecast is a moment-in-time function where you take the state of the business, some of which is in people's heads, but most of which is written down everywhere, where it's like, how is this product doing, how is that product doing? Will this deal close? will that happen or whatever. So there's like the moment in time stage the business, we put it into a function

and out comes the updated numbers for the year. You can imagine an AI doing a fully no-human-in-the-loop forecast. What quarter do you think Google's first fully agentic forecast is? I definitely expect in some of these areas 27 to be an important inflection point for certain things, even the people doing it, that is the workflow through which they would produce it. And maybe for a while you would check it in the conventional way,

but you kind of switch over, a crossover. But I expect 27 to be a big year in which some of those shifts happen pretty profoundly. I think that was, Alad's question was Eng as an early adopter, but kind of outside of Eng. And okay, it sounds like you think 27, a lot of these non-Engh processes really start. I do think your question earlier on like, you know, I think you were asking in the context of way more robotics, like companies.

I do think companies which are, that's one advantage startups are going to have, more AI native teams. And, you know, you can probably get at it through your interview processes, et cetera. Whereas for us, we would have like retrainings, transformation, et cetera. And I think that that's maybe an advantage like the younger companies are going to have. And we have to, you know, kind of like drive that transformation. Last question. We're talking a lot about initiatives that started small at Google, like the Transformer, which are not Google's main priority, you know, when that initiative started.

What's a small thing inside Google that you're excited about these days? It probably would surprise people, like, you know, when we decided to do data centers in space, like, you know, we started as a very small team, right? So it's literally a few people with a small budget to go to the first milestone. So I think it's important to start small, even if it's a big idea. So that is an example of a small thing. Look, I literally spent time yesterday who was explaining some improvement in post-training, which is like one person talking through the improvement they are doing,

listening to it, I'm like, oh, that's going to like really show us like a nice jump. Right? So that's the constant power of this moment. And so all of that, I don't want to be specific about the second one, but we'll publish it one day, I'm sure. You know, so, but those are, those are some of the small gems I'm excited about. As a data center space and new ML techniques. Yeah. Yeah. Great answer. So, no, thank you. All right, real pleasure. Thanks. Take care.