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

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

• 0:01Speaker 00

  Sundar Pichai just passed a decade as CEO of Google. Alphabet is now not only one of the world's biggest tech companies, but a leader in the AI race with plans to spend $175 billion in CapEx in 2026.

• 0:13Speaker 01

  Cheers. Cheers. Thanks for coming. Well, thanks for having me.

• 0:18Speaker 00

  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 ChatGPT and that style of product. How do you reflect on that now?

• 0:33Speaker 01

  I think it's actually worth talking about. It's a bit misunderstood. Transformers was done in the context of a lot of like TPUs, transformers were all done to solve a specific product need to some extent, right? Like the teams thinking about how to make translation better. In the case of TPUs, how do you, hey, SpeechRec works. But you suddenly have to serve it to 2 billion people. We don't have enough chips for it. It's like, how do you solve inference for it?

• 1:00Speaker 00

  So transformers were specifically.

• 1:04Speaker 01

  It was from our research teams, but they were guided by solving product problems.

• 1:10Speaker 00

  Yeah.

• 1:10Speaker 01

  And transformers were immediately used. So BERT and MAM, 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 MAM. We built transformers and used it immediately in search to improve language understanding, understanding webpages, understanding your queries, kept building better models. We had also started productizing it internally in the form of, there were teams building something called Lambda. So obviously, we were in the first to ship that.

• 1:53Speaker 01

  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.

• 1:59Speaker 00

  It's like, you did 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.

• 2:09Speaker 01

  I would go a step further. We exactly even conceived the product, which is like ChatGPT. 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 ChatGPT who was speaking to internally. So we 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 in twenty two, we launched something called AI Test Kitchen, and that was Lambda, but we had constrained it because internally we didn't have an end to end version, which was RL-Chat.

• 2:53Speaker 01

  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, Chad, 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 it wasn't good. We were figuring out how to get it out. I would also argue that even when OpenAI shipped, they did their deal with Microsoft probably a couple of months before. So, you know, 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.

• 3:38Speaker 01

  You know, coding side, probably you were seeing more of a sequential jump than probably, you know, just on the language side. Yes. So maybe the jumps between G P T two and three and later four were more pronounced, you know, if you were using it for coding too. So that, you know, you can point to things. But, yeah, so, but I I think to to answer your original question. Yes. I think it was less that research to product. Yeah. Than a bunch of other factors.

• 4:06Speaker 02

  I also, I remember talking to some of the people who worked on ChatGPT, and I think they launched it the week of Thanksgiving. You know, it was a little 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. It was clearly a surprise.

• 4:17Speaker 01

  Yeah.

• 4:18Speaker 02

  Sort of test case. So it was really interesting.

• 4:20Speaker 01

  But, you know, the way I internalize these moments is if you're in consumer internet, you're going to have surprises. We were at Google when E E E L A and I, I mean, you there was something called Google Video Search.

• 4:32Speaker 02

  Yeah.

• 4:32Speaker 01

  YouTube came out, right? Just that we acquired YouTube. Or think about if you were in Facebook, Instagram came out.

• 4:41Speaker 01

  Nobody sits and says, like, you know, you don't look at those moments with that

• 4:44Speaker 00

  Yeah.

• 4:45Speaker 01

  drama, because Facebook just bought Instagram.

• 4:47Speaker 00

  Yeah, yeah.

• 4:48Speaker 01

  Right, and but the way I've internalized is consumer internet. People are able to, people are going to be sitting and prototyping and throwing out millions of things. I 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 gonna happen, right? But that's not how consumer internet is. So you you just have to be conscious of that and internalize that.

• 5:16Speaker 00

  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 it 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 of 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.

• 5:55Speaker 01

  I've always internalized speed. Let's call it as latency for this purpose, right? And 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.

• 6:41Speaker 01

  You know, I was speaking with the teams, right? Like, they now have for sub-teams, like latency budgets, like in the milliseconds.

• 6:51Speaker 01

  You'll get 50% credit if, so if you ship something which, you know, shaves off 3 milliseconds, you earn 1.5 milliseconds for your latency budget and 1.5 milliseconds gets passed on to the user. Right? And, 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, so, but you have rigorous reviews against that. But that's how much we think it matters, so.

• 7:22Speaker 02

  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.

• 7:27Speaker 01

  Uh, that's right, yeah, that's right. I think we've actually, you know, last I checked the dashboards and the metrics, we've actually improved search latency by 30% in the last five years. But think about the functionality progression that's happened. This is why in Gemini, you know, we deeply think about that parade of frontier of making sure, you know, the, the, uh, the capability to, uh, speed and, and, you know, the flash models are at like 90% the capability of the pro models, uh, but much faster, uh, much more effective to serve and, and the vertical integration helps and, and so on.

• 8:12Speaker 02

  How do you think about the future of search actually? Because a lot of people now are talking about chat as a new interface. Obviously Gemini has incorporated, um, or search has incorporated Gemini or AI results in the, in the context of Google. But a lot of people are now talking about a Gentic flows and everybody's going to have a personal agent who, uh, instead of typing in a query, it'll go and do something for you, you know? Instead of asking about trips, it'll go and plan a trip for you. Uh, 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?

• 8:43Speaker 01

  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 webpages. 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 taken search. You will be completing tasks. You have many threads running.

• 9:20Speaker 02

  Well, search exists in 10 years?

• 9:22Speaker 01

  Well, you know, you may.

• 9:23Speaker 02

  Or it just evolves and just.

• 9:24Speaker 01

  It keeps evolving. So, 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.

• 9:45Speaker 00

  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?

• 10:02Speaker 01

  But today in AI mode in search, people do deep research queries, 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.

• 10:17Speaker 02

  Sure. 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 paradigm eventually go away? And really, what was search becomes an agent and your future interface is an agent. And the, the search box in 10 years or N years is, is no longer.

• 10:37Speaker 01

  I mean, the form factor of devices are going to change. I O is going to radically change. And so, you know, so it's tough to, I think you can paralyze 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 N vision five years out. Unlike, you know, the models are going to be dramatically different in a year's time. And so, you know, 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.

• 11:22Speaker 01

  Right, like the value of what people are going to be able to do is also on some crazy curve, right? So, so once you view it that way, you know, like people would ask all these questions, right, like when YouTube has done well since TikTok and Instagram is, you know, so you can, I can give many examples. I think, you know, the more you view it as a zero sum game, it looks difficult. It can't become a zero sum game if you're un-innovating or the product is not evolving or, you know, but, 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.

• 12:05Speaker 01

  Right, so, and, and so I think, I think, I think it's good to have both and embrace it.

• 12:12Speaker 00

  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 twenty five, 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 a hundred and fifty-ish dollars 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, you know, Waymo and YouTube and all the cool bets. What do you think investors as a proxy for kind of informed sentiment

• 12:57Speaker 00

  misunderstood this time last year? Because clearly there was some big misunderstanding.

• 13:06Speaker 01

  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, it's, it's not an accident or something. It was a very intentful, we were in the seventh version of TPUs. Yes. I remember, it might have been 2016 Google I O where we announced the TPUs and, you know, spoke about we are building AI data centers. This is 2016. We were thinking about, you know, you know, company was operating in AI first way. So we had deeply internalized the shift.

• 13:52Speaker 01

  So to me, we were behind in terms of frontier L L M 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, right? 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 way more, all relies on progress in this.

• 14:36Speaker 01

  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 sum 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.

• 14:59Speaker 01

  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.

• 15:17Speaker 00

  Was there something that demonstrated to the outside world that, oh, they got this? Was it Gemini three that changed people's minds? Or like, I don't follow the timelines.

• 15:24Speaker 01

  I think, I think the real model probably where people saw it was maybe Gemini two point five.

• 15:33Speaker 01

  And, you know, and getting to the frontier on multi, particularly around multimodality. We made a bunch of, I mean, credit to the Google DeepMind teams, right? They, I think we paid a bit more of a fixed cost upfront, 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, Banana 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.

• 16:18Speaker 01

  Oh, shit, there's like a couple of things we're behind, right? But I think the picture will again be dynamic in a few months. So that I think the frontier is intense, as you would expect it to be.

• 16:30Speaker 01

  So that's how I think about it.

• 16:31Speaker 02

  It's been 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 are 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, how, what people are building against?

• 17:05Speaker 01

  Look, I think, you know, we probably have scaled our capex from thirty billion to hundred and

• 17:13Speaker 01

  Approximately 180 billion.

• 17:15Speaker 00

  It's like real money now.

• 17:17Speaker 01

  You don't do it if you don't think, you know, about the curve a certain way. I viewed 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

• 17:39Speaker 01

  talk about it might be different. I think the founders where AGI built.

• 17:45Speaker 00

  Yeah, yeah.

• 17:46Speaker 01

  Probably.

• 17:47Speaker 00

  That's kind of the first.

• 17:47Speaker 01

  My earliest conversations. 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, I mean, at one point, I don't know, Demis, Jeff, Ilya, Dario were all there. So he can.

• 18:07Speaker 00

  I like that retort. It's like, hello, have you been paying attention for the past 20 years?

• 18:12Speaker 01

  Yeah, so that doesn't make sense to me. I think some of it is, you know, if you're a younger company,

• 18:21Speaker 01

  you know, or you were more a pure research lab, you know, you may be headquartered in San Francisco.

• 18:27Speaker 00

  Yeah, yeah, yeah.

• 18:28Speaker 01

  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.

• 18:39Speaker 00

  Yeah.

• 18:40Speaker 01

  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.

• 18:54Speaker 01

  See the agents pick up skills, do stuff, and also look back three months ago what they could do now. And we are living that exponential internally, right?

• 19:04Speaker 00

  I think you're both right, where I agree. You can kind of point us at the history of Google. I think what a lot is getting is 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 they're spending, you know, a huge amount of time writing code and talk to AI and things like that. Those are 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 you know, to what extent do you have AI psychosis these days?

• 19:40Speaker 01

  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. Demis demoing the earliest versions of the models, having what you would call as imagination. So there have been many moments like that. So it was obvious the technology is progressing.

• 20:21Speaker 01

  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 coding 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 feeling the AGI. So there are moments like that.

• 20:48Speaker 00

  Yes, yes. I did 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, it feels like magic, yeah.

• 21:01Speaker 01

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

• 21:17Speaker 00

  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 to the product experience and everyday users because tech products are so abstract that it's easy to, you know, you cannot just manage through reports from teams and slide decks and spreadsheets. And so, you know, Tony Hsu is talking about how he still works as a DoorDash, you know, to stay very connected to that experience. We do at our like little weekly all hands, we have a recurring segment of just walk the store where we like click around in the dashboard together and we're tripping over like, why is that model there? And that's a bit confusing or whatever, just so we're like collectively using the product.

• 21:57Speaker 00

  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.

• 22:07Speaker 00

  Often you use Gmail and everything every day.

• 22:09Speaker 01

  Oh, yeah, like, you know, I'm dogfooting 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 want to talk to it for like the entire thirty minutes on like one topic. So you you do those things and it's it's some of it works, some of it is frustrating. We 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.

• 22:53Speaker 00

  Thank you for fixing the Google Calendar thing. That was so good.

• 22:56Speaker 01

  Well, a few more we have to fix, but.

• 22:59Speaker 00

  No, it's awesome.

• 23:00Speaker 01

  Thanks for flagging it. So, yeah, X helps because you you kind of get the raw raw comments and I try to follow it directly.

• 23:09Speaker 01

  But I'll tell you what what has helped. Internally, like, I would go fire to our earlier part. Like, I would query in AntiGravity, just our internal version of AntiGravity. 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 typed that. Now that brings it back. So has my life gotten easier? Yes. So in the past, I would have spent 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 firsthand to get that feel? Was this actually leveraging these tools? So even I'm going through a journey there, right?

• 23:54Speaker 01

  So I'm trying to adapt to this future.

• 23:56Speaker 02

  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 US economy is bigger because of AI? And if so, how much bigger?

• 24:22Speaker 01

  Look, for these returns to make sense somewhere, it has to, you know, how long was it before?

• 24:31Speaker 01

  I think it was maybe from Sequoia someone wrote and saying people are investing this much.

• 24:37Speaker 00

  Yeah, they're comparing the capex to the.

• 24:39Speaker 01

  Yeah, and this might have been two and a half years ago and 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 the investment 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.

• 25:05Speaker 02

  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, right? In other words, I think the market for software engineering and coding is dramatically bigger than everybody 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.

• 25:35Speaker 01

  Yeah.

• 25:35Speaker 02

  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 capex versus outcomes or, you know.

• 25:43Speaker 01

  I see.

• 25:44Speaker 02

  Yeah.

• 25:45Speaker 01

  Look, I mean, going back to the internet and looking at GDP growth, you know, 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.

• 25:56Speaker 00

  Consumer surplus.

• 25:57Speaker 01

  But yeah, so you know, it's tough to look ahead. I do think there are natural dampening mechanisms in society at various levels.

• 26:10Speaker 01

  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 the more constrained curve there. Then how do you diffuse it into society, right? We are doing this with Waymo, right? And.

• 26:32Speaker 01

  You can make way more safer than human drivers, but you have to be careful at the pace at which we are rolling out, etc. So sometimes, how do you diffuse it through society responsibly? There are constraints in all these layers, right? So, but I think the US economy is so much larger than it was 10 years ago. So to grow that even at a half a percentage point higher than, you know, that's a massive contribution. So I expect it to play out that way.

• 27:03Speaker 00

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• 27:54Speaker 00

  You referenced the supply constraints, and I think that's a really interesting defining aspect of 2026, basically, where you said 150 billion in CapEx, 180?

• 28:05Speaker 01

  We have said it will be between 175 and 185.

• 28:08Speaker 00

  Okay, so 180-ish billion of CapEx. And what's interesting to me is that Google could not spend $400 billion in CapEx if it wanted to because the memory isn't there and the power isn't there and all these components. So can you just tick through?

• 28:23Speaker 01

  We can find a number of electricians we would need.

• 28:25Speaker 00

  Exactly. So I'd love to hear just your overview of the various bottlenecks.

• 28:30Speaker 01

  Look, at some level, you have to work back to actual wafer capacity or something like that, right? So there are deeper ground truths, right? I think so wafer starts. It's kind of a fundamental constraint.

• 28:46Speaker 01

  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.

• 28:59Speaker 00

  Even though there's lots of land in pro-growth, you know, Texas or Nevada or Montana, just maybe not enough?

• 29:04Speaker 01

  I think we're making tremendous progress. I think for the US, I think it's a particularly important thing. You know, it's, 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. Right, I think, you know, there could be growing resistance, so you know, it's not as simple as like a few people deciding we want to build faster.

• 29:43Speaker 00

  The data center moratorium.

• 29:44Speaker 01

  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.

• 30:03Speaker 01

  Memory is a good one. We are constrained in those things in the short term. Everyone will respond to it.

• 30:08Speaker 00

  Yes.

• 30:09Speaker 01

  But I think all of us running companies, regardless of how AGI-pilled 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 your control. So everyone is making those adjustments. Those are all constraints, right, and constraints.

• 30:37Speaker 01

  So I think, but that's where I see the constraints.

• 30:40Speaker 00

  Is memory the biggest component that you think about?

• 30:44Speaker 01

  Memory is definitely one of the most critical components now, yes.

• 30:48Speaker 00

  And you said in the short term, do you think just people ramp up supply and so high prices will take care of us?

• 30:54Speaker 01

  There is no way that 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.

• 31:06Speaker 00

  Yes.

• 31:07Speaker 01

  But I do expect all of this to constrain. By the way, I think it'll push a lot of innovations on we will make these things 30x more efficient.

• 31:16Speaker 00

  Yes.

• 31:17Speaker 01

  Like, so all that is happening simultaneously as well.

• 31:20Speaker 02

  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.

• 31:34Speaker 00

  So it's a musical chairs game of who has compute right now, basically, you're saying?

• 31:37Speaker 02

  Exactly. Who has compute right now and how much can you actually scale relative to overall industry capacity? And if everybody is roughly per rata 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?

• 31:53Speaker 01

  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 Gemma 4. Right? 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.

• 32:25Speaker 01

  Right?

• 32:27Speaker 01

  You know, it's a very weird thing, right? You're talking about a set of weights which can fit on a USB stick.

• 32:32Speaker 00

  Yeah, yeah, it's amazing.

• 32:33Speaker 01

  So it's like a really...

• 32:36Speaker 01

  You know, crazy, it's not like a SpaceX rocket.

• 32:41Speaker 02

  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 your model. It's amazing.

• 32:51Speaker 01

  So there are these unique attributes about this, so which makes me challenge those frameworks and say, you know, how should we think about this? But I think it's a 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. You know, it's immense.

• 33:21Speaker 00

  But as you say, there's only so much memory in the world, so like, no capitalist incentive will really solve 26 or 27 memory supply.

• 33:28Speaker 02

  That may be the era where you see more divergence in models.

• 33:30Speaker 01

  You know, and that has to balance with wafer capacity increasing, you being able to permit those data centers. So this constraint may be less severe than it appears, right? So you have to kind of, you have to envision the total square set of like all the things that you need and think it through, right? Including capital.

• 33:54Speaker 00

  Yes, yes. But again, what's interesting to me is that plausibly, people would invest beyond the current capex, but we're now just running against 26 and 27 real world constraints. It's a little about the Strait 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 like destroy 20 million barrels a day of demand. And it's kind of similar with memory where like ultimately some people have to knock out the memory they want.

• 34:22Speaker 01

  But there are other constraints, right, which, you know, take security as a constraint. And these models are definitely.

• 34:32Speaker 01

  Like really gonna break pretty much all software out there. Maybe already we don't know, we sit here and speak.

• 34:41Speaker 00

  Do you think all software there, because like SSH people have been trying to break for a long time.

• 34:45Speaker 01

  No, I'm talking with just thing.

• 34:47Speaker 00

  Just break my software.

• 34:47Speaker 01

  Open source software, large platforms, right? How many zero days?

• 34:51Speaker 00

  Yeah.

• 34:52Speaker 01

  You know, so there are constraints here in the system, right? You just can wish away, right?

• 34:57Speaker 02

  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 market metric, yeah.

• 35:04Speaker 01

  Not at all surprised, right? And not at all surprised. So, so but when, 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.

• 35:20Speaker 00

  Yes.

• 35:20Speaker 01

  And there could be shocks to the system, if you will. Uh, but having said that, like, you know, uh, I, I genuinely think there's a lot of upside ahead. Some of the constraints maybe are helpful.

• 35:34Speaker 00

  Yes.

• 35:35Speaker 01

  Right, I think constraint inspires creativity.

• 35:37Speaker 00

  Forces a compaction cycle, hurry, get more efficient.

• 35:39Speaker 01

  Forces maybe important conversations to be had, which otherwise won't, won't happen. Right, I think, um, you know, just on my security point alone, like, I thought about, we are going to need more coordination.

• 35:53Speaker 00

  Yes.

• 35:54Speaker 01

  Which is not happening today. There will be a moment of, you know, it could be a sharp moment, right? And like, you know, and so all those things, I, I don't think you can wish them away.

• 36:04Speaker 00

  Yes, yes.

• 36:05Speaker 01

  Right, yeah.

• 36:06Speaker 02

  Actually, related to that, Google does have an amazing portfolio of things that is both built and bought into. From a ownership perspective, you know, you own a reasonable amount of SpaceX, I think. I don't know the exact amount, but I think it was tenish percent way back when. Anthropic, tenish 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 was 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?

• 36:43Speaker 00

  People may be underestimated, yeah.

• 36:45Speaker 01

  Look, we're constantly trying to take these long-term projects, which when you first announce them, slightly marginally looks ridiculous. You know, like, we're in the earliest stages of thinking about.

• 36:57Speaker 01

  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 like in a deeply committed way making progress there. And I'm excited about it.

• 37:34Speaker 02

  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'll have the biggest impact?

• 37:58Speaker 01

  Look at 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 enough, you know, 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 Hubble process for real life. Like the many, I mean, you know, it's probably your background, going back to what you did in college more.

• 38:43Speaker 01

  So my, you know, my instinct tells me there'll be, you know, simulating weather, simulating, you know, reality, all that, I think quantum will have an advantage. I think the way 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 points to applications. So, you know, I mean, I always give this example of. Mobile phones plus GPS enabled Uber.

• 39:13Speaker 02

  Yeah.

• 39:14Speaker 01

  Like, like.

• 39:14Speaker 02

  Yeah, yeah.

• 39:15Speaker 01

  There's nobody who is working on phones who would predict that as an outcome of this platform shift. So, you know, I'm confident Quantum will have many, many, many applications.

• 39:26Speaker 02

  Mm hmm.

• 39:27Speaker 01

  If it can actually make it work.

• 39:28Speaker 02

  Yeah.

• 39:29Speaker 01

  So that's how I, I, I think about it.

• 39:31Speaker 00

  So we interrupt you. You're talking about kind of your favorite of the Google Further Field.

• 39:36Speaker 01

  I think we're making, um, uh, you know, the GM team is deeply thinking through robotics.

• 39:43Speaker 02

  Right.

• 39:44Speaker 01

  Right. And, uh, 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 fifteen years ago or ten years ago.

• 39:58Speaker 01

  But, you know, the Gemini robotics models are sort of on spatial reasoning, et cetera. So we definitely have state of the art models there. And we are partnering, uh, back in an ironic way with Boston Dynamics and, and Agile and a few other companies and, and in a determined way, making progress. And there are extraordinary startups out there as well.

• 40:22Speaker 02

  Mm hmm.

• 40:23Speaker 01

  But, so we are investing in, you know, I spoke about Quantum, data centers in space, drone delivery with Wing. You know, I think we are scaling up Wing where in some reasonable time period, like forty million Americans will have access to a Wing 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, we are committed isomorphic.

• 40:54Speaker 02

  Mm hmm. Yeah, isomorphic is very exciting.

• 40:55Speaker 01

  Uh, you know, think about, uh, uh, you know, think about being focused on these models in a targeted way to improving all the possible steps in drug discovery.

• 41:06Speaker 02

  Mm hmm.

• 41:07Speaker 01

  And even though you have long polls like phase three trials, et cetera, getting there with a much higher probability of success.

• 41:14Speaker 02

  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 stuck.

• 41:21Speaker 01

  Yeah.

• 41:22Speaker 02

  It seems very smart. Can, can I ask? I'm curious.

• 41:26Speaker 00

  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 invest 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 and D dollars and we model this much revenue from the contract, or we 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% IRR versus a 19% IRR. Okay, I prefer the 19%.

• 42:09Speaker 00

  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 time on site 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 invest 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 ROIC and then it's like.

• 42:54Speaker 01

  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 Waymo needs TPUs, right? And 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.

• 43:23Speaker 01

  Once we can actually get all the data connected and flowing through, and the models are already capable, 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 cycle. So it's almost like going back to that roots, it's a deep technology orientation. And, you know, we actually think about the question you were asking a lot a 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.

• 44:08Speaker 01

  So as long as you're seeing that underlying, 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, 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.

• 44:39Speaker 01

  And, and so that's helped. But on a, on a constant basis, look, I, I always view it as you have to assess the long-term value of these things. Right? So it's almost like in some intuitive way, you're thinking about the option value and the time of something five to 10 years down the line. And, and you assume like a crazy growth, right? And, and, and think through whether those decisions make sense. So the TPU investments have been great that way, right? And, uh, you know, we have 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.

• 45:23Speaker 02

  It's very magical. It's such a magical experience. I take Waymo now every day to work when I can. And it's.

• 45:28Speaker 00

  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 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. It'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?

• 46:02Speaker 01

  I think it's to do with that some kind of quantified.

• 46:08Speaker 01

  You look at the Waymo 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 it didn't progress. But those are the times you need to kind of like, you know, you have confidence in the quality of the team to break through those phases. 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.

• 46:51Speaker 01

  Or at least that's how I have tried to do it.

• 46:54Speaker 02

  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 mapped heuristics of like, how do you deal with edge cases of driving or something happens? How do you respond? And a subset of those were almost like 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 of 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?

• 47:31Speaker 01

  Okay, thank. You know, we spoke earlier about robotics. You can think about Waymo as 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 know, you take something complex like TSMC 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-end approaches are going to be an accident.

• 48:14Speaker 02

  Yeah, 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, we'll potentially have a different history where you can move very quickly now. Do you folks think about reinternalizing hardware again? Or is it largely going to be a partner-driven model to bringing this stuff to the world?

• 48:46Speaker 01

  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.

• 49:17Speaker 02

  That makes sense.

• 49:18Speaker 00

  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 this, should Google have been more leaned in and said, "Okay, we will be willing to..." Have a leveraged position that's slightly more aggressive than, you know, strongly net cash. And we will put that towards new initiatives or just buy more of this core Google business for Google shareholders.

• 50:01Speaker 00

  Or do more minority investing, which again, Google seems to have been best in class at.

• 50:05Speaker 01

  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 good stewards of capital.

• 50:23Speaker 01

  So to the extent you're bullish on ROIC, you want to invest every last dollar you can there.

• 50:32Speaker 01

  But to the extent, you know, you have excess where you don't think, I mean, that's why we invested in other companies too, right? Even if not, then, but we've always thought about it with the lens of being good stewards of it.

• 50:44Speaker 00

  Yes.

• 50:45Speaker 01

  We felt our investment in Stripe was being a good steward of our capital.

• 50:51Speaker 01

  SpaceX, right? You know, SpaceX and Anthropic and so on. So I think, I think now with the AI shift, there are more opportunities on which we can deploy capital.

• 51:03Speaker 00

  Yes.

• 51:03Speaker 01

  In a good way. And so we are doing that.

• 51:05Speaker 00

  Yes, yes.

• 51:06Speaker 01

  But I think we always had that mindset.

• 51:07Speaker 00

  Yes.

• 51:09Speaker 01

  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.

• 51:22Speaker 00

  Yes.

• 51:23Speaker 01

  And it just, it wasn't the right thing to do.

• 51:27Speaker 00

  Thing is, you feel like you cannot point to projects where they would have gone faster had they gone more capital sooner. They just needed, they had a natural ramp.

• 51:35Speaker 01

  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 you got excited about something and had the conviction.

• 51:47Speaker 00

  Yes.

• 51:47Speaker 01

  We were willing to commit the capital to see it through.

• 51:50Speaker 00

  My other capital allocation question was historically at tech companies, the large majority of the R and D expense was the people walking around the building. And headcount was managed through a very tightly controlled process. And indeed, as you thought about allocating R and D effort, it was really allocating 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 of the people. We're now going to a world where, as you say, that's not the case with TPUs and how you allocate that.

• 52:31Speaker 00

  Just at a very concrete budgeting level, how does that work inside of Google? Like, do you have an overall TPU budget for the company? And then when you are giving a project resourcing, previously you gave us 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 a quarterly review or an annual review?

• 52:56Speaker 01

  Look, we've always had a compute budget.

• 52:58Speaker 00

  Asking for a friend.

• 52:59Speaker 01

  Asking 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 GPUs, by the way, extensively. But ML compute planning is, we are 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 easy. And then there have been phases 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.

• 53:44Speaker 01

  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 accessing it and in some ways it's a really important thing to be doing right now, I feel.

• 54:04Speaker 00

  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.

• 54:14Speaker 01

  That's right.

• 54:15Speaker 00

  Initiatives.

• 54:15Speaker 01

  Yeah.

• 54:16Speaker 02

  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?

• 54:29Speaker 01

  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 commitments to customers. Anything we commit to a customer is sacrosanct, right? So these are contractual commitments. So you solve a lot of it with planning. And so there are, when you plan, we're all in a constrained world. So I think the cloud team would say they don't have the compute they want, et cetera, et cetera. But you solve it with planning ahead.

• 55:14Speaker 00

  Speaking of Google Cloud, I have my product requests that I've been saving up for this section that I know you're looking forward to.

• 55:20Speaker 01

  You could have posted it on X.

• 55:22Speaker 00

  Exactly, yeah, yeah.

• 55:23Speaker 01

  You've taken care of it.

• 55:23Speaker 00

  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 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. 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, 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 is so much functionality there.

• 56:06Speaker 00

  And 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.

• 56:16Speaker 01

  I think the promise of AI being this orchestration layer, like for anything you think about, to my earlier question, even internally within the enterprise as a CEO. It's not like you don't have all the data.

• 56:27Speaker 00

  Yes.

• 56:28Speaker 01

  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, you know, AI being this orchestration layer in a way that makes sense for the end user, I think it's been delightful to see.

• 56:44Speaker 00

  And the bigger the product surface area, the more that benefits, you know, 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.

• 56:53Speaker 01

  I think, I think, I think we could do a lot better. So, but you're right, it's an immense opportunity, I think, yeah.

• 56:58Speaker 00

  I've been really happy with it. Okay, and then that gets to my product suggestions. Did you bring product suggestions for us?

• 57:02Speaker 01

  You go first, yeah, I want to, but.

• 57:04Speaker 00

  What's interesting, we're kind of open claw in the product market field 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, AI, or like mainstream AI apps allow persistence. Is that common?

• 57:30Speaker 01

  I think they're actually, look, I think you want to give users capability where you have persistent, long-running tasks.

• 57:37Speaker 00

  Yes.

• 57:38Speaker 01

  In a, in a reliable, secure way. You know, you have to think through things like identity, access.

• 57:45Speaker 00

  Yes.

• 57:46Speaker 01

  Et cetera. But I think that's the future, that's the agentic future. And bringing that for consumers is like a bit of an exciting frontier we are looking at.

• 57:57Speaker 02

  Yeah, this is one of mine, too. This is Dreamer, which was the former CTO of Stripe's company that just got bought by Meta. I think they did a very good version of this. It's a very early kind of view of.

• 58:08Speaker 00

  Yeah, they were making custom software, including persistence, but also, you know, you could kind of spec out.

• 58:14Speaker 02

  Kind of make your own little app.

• 58:15Speaker 00

  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.

• 58:25Speaker 01

  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 one percent of the world. Maybe not one percent, point one percent of the world who's kind of living this future. Right? They are building stuff for themselves. You know, but bringing that to mass adoption.

• 59:02Speaker 00

  Yes.

• 59:03Speaker 01

  Is a very exciting frontier, I think.

• 59:06Speaker 00

  Okay, my other product suggestion is, sorry, you have to endure this part of the interview.

• 59:11Speaker 01

  Sorry, I have passed.

• 59:12Speaker 00

  Exactly. My other product idea is, for some reason, I don't know if this is your lived experience, but certainly my lived experience, that searching Google Docs is so much harder than, say, searching 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, PowerPoints or Stripe. And so I can never find the exact right one. And I'm curious, does Sundar Pichai also have this problem?

• 59:57Speaker 01

  Somehow I haven't felt it adequately 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 who are working on it. I think we can make it a lot better. I think with 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.

• 1:00:26Speaker 00

  Yes.

• 1:00:27Speaker 01

  Put it in somewhere.

• 1:00:30Speaker 01

  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.

• 1:00:41Speaker 00

  Okay, great. We have a good.

• 1:00:43Speaker 02

  A lot of companies that I'm involved with, even ones that were started reasonably recently, have had to dramatically shift their workflows relative to product development, engineering practices, who they even think of it 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?

• 1:01:03Speaker 01

  The way I would say it is, you can think of it as concentric circles. There are some groups within 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, it's 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 sweet groups, really change their workflows, right? And, you know, they are using, we call this, for some strange reason, we have a different

• 1:01:47Speaker 01

  name internally than externally of the same product, but it's Jet Ski internally, which is anti-gravity. And, and you're living on it, you're living in a agent manager world, you have 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, is a hard aspect of this technology diffusing, which may be easy for a small company, right? Like, you know, you can quickly switch over.

• 1:02:20Speaker 00

  Can I lay out a few problems I see when it comes to actual diffusion of AI in industry? And I'm curious. How and when you think we'll solve them? Because 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 at 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,

• 1:03:04Speaker 00

  and then there's a Stripe being good at prompting. And then, of course, you have the fact that it's hard to share an AI-generated codebase because you are, you have a blast radius and you're just changing so much and the turnover of the code is high enough, or maybe you're rewriting it several times before you ship, that it's kind of hard for many people to collaborate on the codebase 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 Stripe where I was seeing where you can actually answer that pretty well. But with both habits and access to data,

• 1:03:49Speaker 00

  and you know, 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 rewritten. And then you get into role definition, where kind of like you were saying, NPM 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, since you've got a product door. Anyway, that's kind of my characterization of in 2026, 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?

• 1:04:29Speaker 01

  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, and that's literally we are using it internally, running into these barriers, kind of working past it. 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 can create an automated workflow. And so that's happening in like these parts, right?

• 1:05:12Speaker 01

  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 which are limiting diffusion to us too. Uh huh. Right? And we take security a lot more seriously, and so we have to, right? 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.

• 1:05:47Speaker 01

  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 these jumps of what people are able to do when we bring it out. And others are doing it too. Uh huh. And, and in a more robust way, the models are improving.

• 1:06:06Speaker 00

  Google re-forecast its business a few times a year, formally, I presume. At least we do at Stripe, where we, you know, we set a budget for the year and then three times a 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 which is written down everywhere, where it's like, how is this product doing? And how is that product doing? Will this deal close? Will that happen? Whatever. So there's like the moment in time state of the business, we put it into a, you know, function, and out comes the updated numbers for the year. You can imagine AI doing a fully no human in the loop forecast. What quarter do you think Google's first fully agentic forecast is?

• 1:06:51Speaker 01

  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, crossover. But I expect 27 to be a big year in which some of those shifts happen pretty profoundly.

• 1:07:19Speaker 00

  I think that was a lot of question was Eng is an early adopter, but kind of outside of Eng. And okay, it sounds like you think 27, a lot of these non Eng processes.

• 1:07:28Speaker 01

  I don't think your question earlier on, like, you know, I think you were asking in the context of Waymo or robotics, like companies. I don't 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 retraining, 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 the transformation.

• 1:08:02Speaker 00

  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?

• 1:08:16Speaker 01

  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, like which is like one person talking through the improvement they are doing, listening to it. I'm like, oh, that's gonna like really show us like a like a nice jump. Right? So that's the constant power of this moment.

• 1:08:58Speaker 01

  Um, and so all of that, I can't, I don't want to be specific about the second one, but we'll publish it one day, I'm sure, uh, you know, so but those are, uh, those are some of the small jumps I'm excited about.

• 1:09:09Speaker 00

  There's a data center in space and new ML techniques.

• 1:09:12Speaker 01

  Yeah.

• 1:09:13Speaker 00

  Yeah. Brilliant answer. Thank you.

• 1:09:15Speaker 01

  All right. Real pleasure. Thanks. Take care.