7 Apr 2026 · 1h 9m · Sundar Pichai, John Collison, Elad Gil · 21 quoted facts
Sundar Pichai argues that Google's position in AI is the result of a deliberate, full-stack strategy rather than a lucky comeback. Years of work on research, custom chips (TPUs) and data centers fed products like Search, YouTube, Cloud and Waymo. He sees AI as expanding the market rather than splitting it, and he says Google's $180 billion capital spending is evidence that the company takes the AI curve seriously.
The central tension is between runaway demand and physical limits. Pichai says Google is supply constrained: memory chips, wafer capacity, permitting, power and even security all limit how fast it can build. He treats these limits as temporary and partly useful, since they force efficiency. A second tension runs through capital allocation: how to fund long-shot bets like Waymo, quantum and space data centers while compute is scarce.
Pichai says BERT and MUM, built on transformers, drove some of Search's biggest quality jumps. He argues the research was guided by product problems, and that Google's high quality bar and an immature chatbot (LaMDA) delayed a ChatGPT-style launch.
Search sub-teams get millisecond latency budgets, and shipping a speedup earns them half the saved time while half goes to users. Pichai says Search latency improved 30% in five years even as features grew.
Pichai spends a dedicated hour each week reviewing compute use by project and theme. Memory is among the most critical shortages, and he says leading memory makers cannot dramatically raise capacity in the short term.
Google funds long-term projects modestly at first and judges them on underlying technical goals, such as error-corrected qubit thresholds for quantum or safety curves for Waymo. This is how it stayed with Waymo while others backed off, and why a tiny space data center team exists.
He says engineering groups at Google, especially DeepMind, have already changed workflows, and the Search team got the same tooling only recently. He expects 2027 to be a major inflection point for non-engineering processes such as forecasting, though identity and access controls remain hard.
Pichai says transformers were used right away in Search, and Google had an internal chatbot called LaMDA. In his telling, Google held it back because it was too toxic and Google's search-quality culture set a higher bar for release.
Pichai means Google could use more computing power than it can get. Memory chips, wafer capacity, permitting and power all limit how fast it can build data centers, even with roughly $180 billion of capital spending.
Pichai says Search will keep evolving, with simple information queries turning into agents that complete tasks, and eventually perhaps acting as an 'agent manager'. He says Search and the Gemini app will overlap and also diverge, and that he does not see AI as a zero-sum fight.
Pichai gives no forecast, but says the economy is so large that raising growth by even half a percentage point would be a massive contribution. He notes that building compute and spreading the technology through society act as natural brakes.
Pichai names data centers in space, quantum computing, robotics (including partnerships with Boston Dynamics and Agile), Wing drone delivery, Waymo and Isomorphic Labs for drug discovery. He says each starts small and is judged on technical progress.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Pichai says Google used transformer models such as BERT and MUM in Search soon after inventing them, to better understand web pages and queries.
“We built transformers and used it immediately in search to improve language understanding, understanding web pages, understanding your queries, kept building better models.”
Google launched AI Test Kitchen, based on its LaMDA model, in 2022, but constrained it because the internal version was not fully trained with reinforcement learning.
“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 end-to-end version which was RL achieved.”
Pichai says Google Search sub-teams have millisecond latency budgets and receive 50% credit for the latency they shave off.
“they now have for sub teams, like latency budgets, like in the milliseconds. You get 50 % credit.”
Pichai says Google Search latency improved by 30% over the previous five years while capabilities increased.
“We've actually improved search latency by 30 % in the last five years, but think about the functionality progression that's happened.”
Pichai says Gemini's Flash models deliver about 90% of the capability of the Pro models while being faster and cheaper to serve.
“making sure the capability to speed and the flash models are at 90 % the capability of the pro models.”
Pichai recalls that Google announced its TPU chips and AI data centers at Google I/O around 2016.
“We were in 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.”
Pichai says Gemini 2.5 was probably the model at which outsiders recognized Google had reached the AI frontier.
“I think the real model probably where people saw it was maybe Gemini 2 .5.”
Pichai says Google's capital expenditure has grown from about $30 billion to approximately $180 billion.
“we probably have scaled our CapEx from $30 billion to approximately $180 billion. It's like real money now.”
Pichai names Jeff Dean's 2012 demo of an early Google Brain system, in which a neural network recognized cats, as his first 'AGI moment'.
“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?”
Pichai says that raising US economic growth by half a percentage point through AI would be a massive contribution.
“So to grow that, even at a half a percentage point higher, then that's a massive contribution.”
Pichai says leading memory makers cannot dramatically expand capacity in the short term, constraining AI hardware supply.
“There is no way that the leading memory companies are going to dramatically improve their capacity.”
Pichai says Google's open model Gemma 4 is based on the Gemini 3 architecture.
“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.”
Pichai notes that the weights of an open model such as Gemma can fit on a USB stick.
“You're talking about a set of weights which can fit on a USB stick.”
Pichai says Google is partnering with Boston Dynamics, Agile and other companies on robotics, using its Gemini robotics models.
“we are partnering uh back in an ironic way with Boston Dynamics and and Agile and a few other companies”
Pichai says Google is scaling up Wing drone delivery so that about 40 million Americans will have access to it in a reasonable time period.
“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”
Pichai says Google increased its investment in Waymo two to three years earlier, when others were pessimistic about self-driving.
“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.”
Pichai says Google's investment in Stripe was an example of acting as a good steward of capital.
“We felt our investment in Stripe was being a good steward of our capital.”
Pichai says he spends a dedicated hour each week reviewing how Google's compute is allocated across projects.
“I at least spend a dedicated hour a week thinking about that question at a pretty granular level.”
Pichai says compute that Google Cloud has committed to customers is treated as contractually protected when Google plans its capacity.
“Anything we commit to a customer is sacrosanct, right? So these are contractual commitments.”
Pichai says Google's internal name for its Antigravity agent coding product is JetSki.
“we have a different name internally than externally of the same product, but it's JetSki internally, which is anti-gravity.”
Pichai says Google's project to build data centers in space began as a very small team with a small budget.
“When we decided to do data centers in space, we started as a very small team. It's literally a few people with a small budget to go to the first milestone.”
Pichai pushes back on the idea that Google squandered its transformer invention. He says it powered Search and that the LaMDA chatbot was held back on quality.
“I think it's actually worth talking about. It's a bit misunderstood.”
Explains how Google makes speed a managed resource inside Search teams. It shows how a product value from the early days is still enforced.
“you earn 1 .5 milliseconds for your latency budget”
Pichai describes Search shifting from one-line queries to long-running tasks. He rejects the idea that AI makes this a zero-sum contest.
“search would be an agent manager, right, in which you're doing a lot of things.”
Pichai explains why he felt Google was built for the AI shift, citing TPUs, research and many businesses sharing one technology. He admits Google lagged at the frontier on models.
“we were behind in terms of frontier LLM models.”
Pichai walks through what limits AI buildout, from wafer capacity to regulation. He argues these constraints may force big efficiency gains.
“Memory is definitely one of the most critical components now, yes.”
Pichai lists Google's long-horizon bets and the logic of starting them small. He also gives a view of where quantum computing might matter.
“we're in the earliest stages of thinking about data centers in space, right?”
Explains how Google judges very different projects, and why it kept backing Waymo when others pulled back. Pichai says he would have invested earlier if Waymo had matured sooner.
“Waymo was a great example where I think we increased our investment two to three years ago”
Pichai says compute is now acutely scarce and that he personally reviews allocations. Cloud customer commitments are treated as contractual priorities.
“I at least spend a dedicated hour a week thinking about that question at a pretty granular level.”
Pichai describes AI adoption at Google as concentric circles, from DeepMind and engineers outward, and names change management as the hard part.
“In a large organization, I think change management is a hard aspect of this technology diffusing”
Pichai predicts 2027 as a turning point for AI doing non-engineering work, and ends on the small teams behind space data centers and new training techniques.
“I expect 27 to be a big year in which some of those shifts happen pretty profoundly.”
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.
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 ChatGPT 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. Transformers was done in the context of a lot of TPUs, Transformers, were 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 2 billion people. We don't have enough chips for it. It's like, how do you solve inference for it?
So transformers were specifically... It was from our research teams, 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 Mark. We built transformers and used it immediately in search to improve language understanding, understanding web pages, 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. 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 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 ChatGP, 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 ChatGP. he was 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 end-to-end version which was RL achieved. 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 we had a higher bar maybe 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 OpenAI did their deal with Microsoft probably a couple of 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.
Yes. So maybe the jumps between GPT-2 and 3 and later 4 were more pronounced if you were using it for coding. So you can point to things. But I think to answer your original question, I think it was less that research to product than a bunch of other factors.
I also remember talking to some of the people who worked on ChatGPT, and I think they launched it the week of Thanksgiving. It was a little bit of a varied 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 the way I internalize these moments is if you're in consumer internet, you're going to have surprises. We were at Google when 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, you don't look at those moments with that drama. up 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. 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 it tries to differentiate. And so original Google search was really fast and famously displayed the search query time within the results sort of showing off. And then Gmail fast search compared to the competitors at the time or Chrome compared to the competitors at 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. It's easy to say 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, I was speaking with the teams, right? Like they now have for sub teams, like latency budgets, like in the milliseconds. You get 50 % credit. So if you ship something which, you know, shaves off three milliseconds, you earn 1 .5 milliseconds for your latency budget and 1 .5 milliseconds gets passed on to the user.
Right? 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. So but you have rigorous reviews against that. But that's how much we think it matters.
So 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 I 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. This is why in Gemini, we deeply think about that Pareto frontier of making sure the capability to speed and the flash models are at 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 as a new interface. Obviously, 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 any 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 we have to absorb those new capabilities and keep evolving the product frontier. If it's mobile, the 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 that, 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 will be completing tasks. You have many threads running.
Will search exist in 10 years?
Well, you know, you may. Or it just evolves into 10 years. It keeps evolving. like, 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.
I think that really 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 in search, people do deep research queries. So that doesn't quite fit the definition of what you're saying. But kind of people adapt it to that. Yeah. Right. So I think people will do long-running tasks. Sure. It's kind of like that.
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 search box in 10 years or n years is no longer. 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 and like envision five years out. I'm like, 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. 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 has done well since TikTok and Instagram has, you know, so I can give many examples. I think, you know, the more you view it as a zero-sum game, it looks difficult. It can become a zero-sum game if you're not 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 searching 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, we're 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, you know, Waymo and YouTube and all the cool 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's 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 in 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, the company was operating in 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, 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 Waymo all relies on progress. 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. And I felt like everything is going to scale up 10x. 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? Oh, they got this. Was it Gemini 3 that changed people's minds? I don't follow the timelines.
I think the real model probably where people saw it was maybe Gemini 2 .5. And, you know, in getting to the frontier on particularly around multimodality, we made a bunch of this. I mean, credit to the Google DeepMind teams, right? 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 areas, 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 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 like a couple of 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.
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 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 what people are building against?
Look, I think, you know, we probably have scaled our CapEx from $30 billion to approximately $180 billion. It's like real money now. You don't do it. if you don't think 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 Pilt, probably 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, I mean, at one point, I don't know, Demis, Jeff, Ilya, Dario were all there. 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 are more a pure research lab, you know, you're maybe headquartered in San Francisco. 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. Yeah. 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 we are living that exponential internally.
I think you're both right. Where I agree, you can kind of point us at the history of Google. I think what a lad's getting at 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 a huge amount of time writing code and talk to AI and things like that.
I thought that 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, you know, 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. Demest demoing the earliest versions of the models, having what we would call as imagination. So there have been many moments like that, so it was obvious the 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 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 field AGI. So there are moments like that.
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, you work those like magic.
Yeah. So, yeah, moments like that for sure. Yeah. 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. When you talk about the visceral feel,
I feel like one thing that's important to 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 door dasher, you know, to stay very connected to that experience.
we do at our little weekly All Hands Weaver recurring segment of just walk the store where we 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 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 Gmail and everything every day.
Oh, yeah. Yeah, like dogfooding, like literally internal versions. I do block time to kind of use it intensely, so 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 some of it works, some of it is frustrating.
We kind of learn a lot, right? So I force myself to use it in those power user mode ways 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, there's a few more we have to fix. No, that's awesome. 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 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 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 firsthand to get that feel versus actually leveraging these tools? So even I'm going through a journey there, 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, 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 comparing the CapEx to the... 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. 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 we offer.
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 anybody thinks and it's the wrong metric to say but you know token budget versus engineers so I actually think it should grow a lot of things yeah 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 I see. Yeah.
Look, I mean, going back to 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 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. 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, then that's a massive contribution. So I expect it 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 $1 1 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 Stripe's network intelligence, come see what you can build here. You referenced the supply constraints, and I think that's a really interesting defining aspect of twin 26 basically where you said 150 billion in capex 180 we have said it's it'll be between 175 and 185 okay so 180 ish uh 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 you know the power isn't there and all these components so can you just tick through you can't 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? It's 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, 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. Yes. Construct 10x faster.
But I would worry about that as a constraint. I think there could be growing resistance, so it's not as simple as a few people deciding you want to build faster. The data center moratoriums and stuff. So I would say way for 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. But I think all of us running companies, regardless of how AGI pill you are, then comes this error bans. 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 that's where I see the constraints. Is memory the biggest component that you think about? Memory is definitely one of the most critical components now, yes.
And you said in the short term, Do you think just people ramp up supply and so high prices will take care of us?
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. 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. Yes. Like, so all that is happening simultaneously as well works.
Does that enforce an 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, et cetera.
So it's a musical chairs game of who has compute right now, basically you're saying?
Exactly. Who has compute right now and how much can you actually scale relative to overall industry capacity? and if everybody is roughly parada 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, 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.
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. It's amazing. 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 your model. It's amazing.
So there are these unique attributes about this, which makes me challenge those frameworks and say, 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 everyone is trying to figure out how to blow through the capitalist incentive to break through these constraints. It's immense.
But as you say, there's only so much memory in the world. So no capitalist incentive will really solve 26 or 27 memory supply.
That may be the era where you see more divergence.
Yeah, so, you know, and remember, 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 envision the total square set of, like, all the things that you need and think it through, right? Including capital.
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 destroy 20 million barrels a day of demand. And it's kind of similar with memory where ultimately some people have to not get the memory they want.
But there are other constraints, right? Like, 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 if 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 thinking... Just regular software. ...open source software, large platforms, right? How many zero days? Yeah. 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 market metric.
Not at all surprised, right? And not at all surprised. So, but how does it practically diffuse through society? What are the implications of it? 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, I genuinely think there's a lot of upside ahead. Some of the constraints maybe are helpful. Yes. Right, I think constraint inspires creativity. Or it's a compaction cycle where we get more efficient.
Forces maybe important conversations to be had which otherwise won't happen. Right, I think, you know, just on my security point alone, Like I thought about we are going to need more coordination, 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 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 into. From an 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 10 % way back when. Anthropic, 10-ish 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? People may be underestimated.
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 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 like 10.
Quantum itself is one of the one of these projects we are like 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 AlphaFold. Do you think quantum will actually matter? And if so,
where do you think it'll have the biggest impact? 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, 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 Haber process for fertilizer. There are many components. 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 finds the applications.
So, you know, I mean, I always give this example of mobile phones plus GPS enabled Uber. Yeah. 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.
But sorry, we interrupted you. You were talking about kind of your favorite of the Google further afield.
I think we're making, you know, the GM team is deeply thinking through robotics, 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 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 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.
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 pulls 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 stuck. 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 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 &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 model this kind
of thing. It's like a 16 % IRR versus a 19 % IRR. 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 improve the recommender algorithm and therefore time and 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 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 RIC and then it's like... It's a good question. Look, I feel it today more than ever, ironically, because of TPU allocation. So in some ways, I feel it even way more than TPU. That's interesting. 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. And I think once we can actually get all the data connected and flowing through, models are already capable.
It's more getting all the data unlocked. I think it 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 truth as 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 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 assist 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 you assume like a crazy growth, right? 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. When others, some of the people were backing off.
It's very magical. It's such a magical experience. I take Waymo now every day to work when I can
I think Waymo is a good example of this question I have, which is Google does cut projects. And there's various things you've tried where you said, we're actually not going to fund this part of X all the way. Or 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 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, 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 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 I had 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.
Look, I think, 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 take something complex like TSMC or SpaceX launching things. You are talking about system integration and 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.
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 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, etc., 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.
Makes sense. 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, OK, we will be willing to have a leveraged position that's slightly more aggressive than strongly net cash. And we will put 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 class at.
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. So to the extent you're bullish on ROIC, you want to invest every last dollar you can there. but to the extent you have access where you don't think. This is why we've invested in other companies too even if not but we've always thought about it with the lens of being good stewards of it.
We felt our investment in Stripe was being a good steward of our capital. SpaceX. You know SpaceX and Anthropik and so on. I think And 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. But I think we always had that mindset. 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. And it wasn't the right thing to do.
So you feel like you cannot point to projects where they would have gone faster had they gone more capital sooner? They just needed a, they had a natural ramp.
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 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. Just at a very concrete budgeting level, how does that work inside of Google? 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 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 classic compute. I would say with ML, 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. So I will know by projects and by themes 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 initiatives.
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 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. 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're going to post it on that. Exactly, yeah, yeah. We're taking care of it. 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, whatever. And now, all that doesn't matter. And so you just say, you know, hey, go, you know, 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 with an enterprise as a CEO, it's not like you don't have all the data. Yes. 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. so, 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 product suggestions. Did you bring product suggestions for a second? No, you go first, yeah. I wanted to, but... What's interesting to me about 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 or like mainstream AI apps allow persistence.
Is that common?
I think directionally, look, I think you want to give users capability where you have persistent long-running tasks in a reliable, secure way. You have to think through things like identity access, 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.
Yeah, this is what I'm saying. 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... Yeah, they were making custom software, including persistence, but also, you know, you could kind of spec out. 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 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 not 1%, 0 .1 % of the world, who's kind of living this future. 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. It's a right of passage. Exactly. 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, 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 Google Slides, for 2026 budget. Neither of those words is like particularly unique in the context of words that exist in, you know, PowerPoints at Stripe. And so I can never find the exact right one. And I'm curious, does Sundar 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 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 cache 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 action.
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 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's 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, 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 SWE groups, really changed 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 JetSki 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. In a large organization, I think change management is a hard aspect of this technology diffusing, which may be easy for a small company. You can quickly switch over.
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 as I see it, we have a big intelligence overhang. The AI is now amazing in terms of what they can do in the abstract. And if you look at how AI-native a 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, and then there's the stripe 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, 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 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 Stripe where 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, 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, NGPM 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 at all those since you've got a product doer.
Anyway, that's kind of my characterization of in 2026, the models are capable of this, but 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 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? 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 you can create an automated workflow.
And so that's happening in like these parts. 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 which are limiting diffusion to us too. 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. 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 outside and others are doing it too. And in a more robust way, the models are improving.
Google re-forecasts its business a few times a year, formally, I presume, at least we do at Stripe, where 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 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? Whatever. So there's like the moment in time stage of 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, cross over. 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 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 really start wrapping up.
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 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.
Last question. We're talking a lot about initiatives that started small at Google, like the Transformer, which is 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. When we decided to do data centers in space, we started as a very small team. 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 going to like really show up as like a like a nice jump right so that's the constant power of this moment 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. But those are some of the small gems I'm excited about.
So data centers in space and new ML techniques. Yeah. Great answer. Sundar, thank you. All right, real pleasure. Thanks. Take care.