Sundar Pichai, CEO of Alphabet | The All-In Interview

[Music] We're sitting here at the Google Plex with the CEO of Alphabet, Sundar. Thanks for being here.

Great to have you here, David. Look forward to it. Is Google at risk of being truly disrupted from AI? Recently, we're testing it in labs. This whole new dedicated AI experience called AI mode coming to search. Open AI has SAM, XAI has Elon, Meta has Zuck, Microsoft has Satcha. Are you willing to kind of share your perspectives on those four competitors? I think maybe only one of them has invited me to a dance, not the others. Biggest regret.

Look, there are acquisitions. We debated hard, came close. Just give me one name or get in trouble. Maybe Netflix. We just leaned into the user experience and over time we figured out monetization to follow. It's like one of the original principles of Google: Follow the user. All will follow. There you go. I'm going all in.

All right, besties. I think that was another epic discussion. People love the interviews. I could hear him talk for hours.

Absolutely. Oh, he crushed your questions in a minute. We are giving people ground truth data to underwrite your own opinion. What do you guys think? That was fun. That was great. What's going on? I'm really excited for this conversation. You and I started working at Google on the same day in 2004. I didn't quite realize that. Same nougler class. We had the the hats on that same week on the Friday all hands. I'm now a podcaster. You've done a little bit differently. You're more than you're more than a podcaster, but you're very good at podcasting.

Well, I appreciate it. I think I respect the other stuff you've done as well. So, no, I appreciate it. But in your tenure at Google, you ran Chrome, Chrome OS, Drive, Google Maps, and it's been 10 years now since you've been the CEO here at Google, now Alphabet. Amazing. And congratulations. Under your tenure as CEO, the stock has gone up by 4 and a.5x to a $2 trillion market cap today. You've grown revenue from 20 billion a quarter to nearly 100 billion a quarter. It's been a really like incredible run to see someone that kind of started as a a PM and you know grew your way into this incredible role. So congrats. How have you liked the job?

No, look, I mean, uh, I love building products and in some ways, you know, Google was really set up, I think the founders set up this kind of a deep computer science approach and like you take that and apply it to build things which can impact people on a day-to-day basis. And so, you know, it's that kind of a product and uh technical culture which, you know, is is the essence of the company. So, I love doing that. And you know there's not a single week which goes by where I I feel like I don't get to do that. So those are the parts I really enjoy. But obviously you know running a company of this scale where you impact uh so many people I think it's a privilege. So enjoyed every part of it.

You're at a pivotal moment in the company's history today. Have you read the innovators dilemma?

Uh you know my I I I'm obviously very very familiar with the concept. I don't think I've read the book actually. But but you know, it's one of those things which is so much in the ether. You think you know it, you know. I say it in justest because that's the talk of the town, the talk on Wall Street, the talk in Silicon Valley. Is Google getting disrupted in this moment? AI seems to create a fundamentally different paradigm for human computer interaction. Consumers are asking AI questions through chat interfaces. They're getting complete answers. They're engaging with AI systems in a way that they traditionally didn't do with the classical search interface. Is Google at risk of being truly disrupted from AI? Is the core search business, which the ad revenue and search is about a $200 billion run rate out of 360 billion of your total revenue, most of your profits? And it seems like Google's in a really challenging quandry where if you disrupt yourselves too quickly, all of that revenue can go away. It can be really impactful. So is Google being disrupted by AI at this moment or is Google leading?

It's a good framework, good question to talk about. Uh you know I've definitely uh you know for almost a decade uh you know one of the first things I did was to think of the company as AI first. It was very clear to us. Uh we had Google brain underway in 2012. We acquired deep mind in 2014. 2015 when I became the CEO I said look the technology is really evolving. The reason we were excited to be approach our work as AI first uh is because we really felt that AI is what will drive the biggest progress in search. And so you know I I think even the last couple of years I viewed this as an extraordinary opportunity for search. I think if you look at how much information means to people I think they're going to each person is going to have access to information in a way they've never had before. So it feels very far from a zero sum construct to me and we are seeing it empirically when people are using search. Obviously there are a couple of major things uh we've done with search. Uh you know transformers drove some of the biggest innovations in search with BERT and mom dramatically improved search quality. Um we we launched AI overviews about a year ago. It's now being used by over one and a half billion users uh in over 150 countries. It's expanding the types of queries people can type in and we see it empirically the nature of queries is expanded. So there are whole new use cases coming into search. We find for queries where we trigger AI overviews. Uh you know we see query growth and the growth continues over time. You know getting the feedback from AI overviews. We are you know we we've recently we're testing it in labs. There's a whole new dedicated AI experience called AI mode coming to search. We'll speak about it more at Google IO. And in AI mode, you can have a full-on AI experience in in search including follow on conversational queries and we're bringing our cutting edge models there. Uh where the models are actually working to answer your questions using search as a real native tool, right? And and there the queries people are typing in queries like literally long paragraphs, right? the average query length is somewhere two to three times is what we see in uh search as it existed uh two years ago. So we are seeing people respond. Uh you know search is always from the outside people look at it and say search all kind of looks easy to do. The craft of search is very hard. Over two decades I think we've had a real northstar of understanding what users want in search. And you know we you know you've been here we kind of a very metrics driven company. We kind of know what works. Uh users are are our northstar and and empirically we see that people are engaging more and using the product more. Right. So uh so all that uh to your question about innovators dilemma I think the dilemma only exists if you treat it as a dilemma right like you know so for me all along in technology you have these massive uh periods of innovation and you lean into it as hard as you can it's the only way to do it you know when mobile came everyone was like well you know it's like you're not going to have the real estate like how will ads work all that stuff uh you know mobile was a transition which ended ended up working great. I can give great examples, right? Like Tik Tok has come in. YouTube has thrived since the moment Tik Tok has come in, right? And uh it was a whole new format. We we uh did shorts when we launched shorts. Shorts absolutely didn't monetize anywhere near long form, but we just leaned into the user experience and over time and we figured out monetization to follow. So you know to me you know you don't think about it as a dilemma like you know because users you have to innovate to stay ahead and you kind of lean in that direction. It's like one of the original principles of Google: follow the user, all else will follow. There you go.

And I think the the Google is dead disruptor narrative has as you point out been kind of repeated a number of times. Today people are pointing specifically and I appreciate your points about there's new search experiences coming. The search experience, it sounds like, is going to evolve. As people look at standalone apps, they compare Gemini as a standalone app to chat GPT to the meta experience. The stats that came out in the recent court testimony that had some data revealed from March. I don't know where the data came from, but it said the uh Gemini AI app had 350 million monthly users compared to Chat GPT at 600 and Meta AI at 500. Is that the wrong way to think about it? that the Gemini standalone app isn't the future or the AI bet that Google's making, but it sounds like there's going to be much more of a kind of timed out integration into how the search experience evolves and what happens to Gemini, you know, in search, you know, maybe the most widely used GI product today might be search with AI overviews, right?

You know, uh people people are using it intensely. Obviously, uh we have a standalone Gemini app. Um I think I think uh we are making progress there. Particularly with the introduction of Gemini 2.5 Pro, we've seen a real uh uptick in engagement uh and usage growth uh in the product. We have a lot more to come. Just in the last few weeks, we've shipped deep research and updated canvas audio overviews. You can now go and generate v do video generation with V2 straight in the Gemini app uh on Android phones with Gemini live you know you can screen share it can talk to what's on your screen so the the you know there's a lot coming that way and you know users are responding um look chat GPD is obviously had phenomenal success um you know but but I think it's still early days and you know we are definitely seeing traction seeing growth to me what matters is if you innovate are users responding and using it more and that seems to be the case so it's in our hands to continue innovating right I think it's a fiercely competitive moment but I would say across our products people are coming and using and consuming information across search using the Gemini model increasingly in YouTube in the Gemini app and so on so I think I think it's a much broader view we have if I were to think about the unit economics of Google's business. There's a cost to serve a search query and there's revenue per search query, ad revenue per search query. How is that number changing or how will that change in this kind of evolution in search towards more of an AI interface? Because I've got to assume that to serve an AI driven query is much more expensive than to serve a search query.

Look, this is something I think you know people are really worried about uh two years ago, but you I've always felt to the extent that something is about the cost of serving it, Google with its infrastructure, I' I'd wager on that, right? And you know, on our chances to do that better than pretty much anyone else. And you know, we have actually seen like, you know, for a given query, the cost to serve that query is fallen dramatically in a 18-month time frame. What is probably more of a constraint is latency I would say. So it's less the cost per query. I think our ability to serve the experience at the right latency. You know search has been near instant. So how do you uh think about that frontier has been more of a question. Uh the cost per query is not what I think will end up uh you know I think I think we'll be able to we've done the transition well. That's that's not a primary driver of how it'll impact things.

Yeah. And do you have a point of view on ad revenue per AI query?

You know, we already with AI overviews um you know, we we are at the baseline of uh uh you know, it's the same as without AI overviews. And so we we've reached that stage in uh so but from there we can improve, right? And I think uh you I've always felt uh you know the reason ads have worked well in search is because commercial information is also information people in when they have that intent are looking for that most relevant information. So I don't see any reason why AI, you know, just from a first principal standpoint, why won't AI do a better job there as well, right? And and and and so I think I think, you know, I think we're comfortable that we can work the transition through. Some of it may take time, but all indicators are that we'll be able to do it well over time. Over time, but you know, it's already, you know, already AI overviews when we show ads. We've kind of reached the baseline.

Do you feel that pressure on Wall Street and the board and like what's the tension that you feel as a leader in trying to manage this transition on the product on the revenue model for an organization of this scale? I don't know how many leaders have done it successfully in the history of business. Where do you feel the tension? Where do you feel the pressure? And how much leeway are you being given by the founders and the board to do what's needed here?

Two things. I mean the main it's a moment of acceleration right so if anything um the good thing about these moments is you don't even have time a lot of times to think about you know some of those questions you are uh I think I think a lot about making sure we have the best models we are we pushing the frontier as a company uh and I think the last few months have shown the breadth and range of what we are doing uh you know uh we are there and we have to continue to stay there so for me you know you you think and you worry a lot more about execution from within that's all you know are we are we executing are we moving fast are we innovating and I think you know over the past 12 months I think we've really picked up pace as a company and uh you know to to meet the moment so that's where I do spend a lot of time look as a as a CEO one of the first things I did in 2015 in addition to being AI first was to really bet big on you know we had great products like YouTube we had workspace and cloud but really turning them into robust businesses, right? As well as great products. Last year, we exited a combination of YouTube and cloud at $110 billion. I think, you know, people don't internalize that Google is one of the largest enterprise software companies in the world now. Uh and and so look, I think and the largest media company, you know, in in some ways, right? And you know, definitely we're doing a podcast. I think we're the largest podcasting service in the world. And so you know so I I I feel like you know as a company we are set up well for the first time you have this crosscutting technology you know to to our earlier point thinking of us as a deep computer science company what better technology than AI which horizontally can impact all aspects of our business search YouTube cloud and the other new things we are doing so it feels like an exciting time so not a lot of what you know we've continued to do well in search we are doing well in these other businesses. Um, and so to me it feels like, you know, one of the biggest opportunities ahead as a company too. I think the next decade ahead looks to me as exciting as the past decade.

So as I think about my time at Google right below us in the garage and his team were building these super secret shipping container data centers. They had these like data center in a box that you could ship anywhere as long as you had access to water and power. could connect to the internet and you could scale data center capacity all over the world. That was 20 years ago. Um it's always seemed to me that one of Google's core and not well understood advantages was its infrastructure advantage. Something that Google's invested in to its core from the beginning. Can you tell me a little bit about where you view Google's infrastructure advantage playing out in the AI competitive landscape today? How does it translate into cost, speed, product quality and where do you guys think about investing the 70 billion of capex this year in the chip layer in the networking the data center?

We can unpack both right like where our capex is going but on your first part right like one of the ways you know we look at the parto frontier of performance and cost Google literally is on the parto frontier so we deliver the best models at the most cost effective price point right like you know and our flash series of models are a real workhorse uh in the industry right and and part of why we are able to do that uh is because you know we train and serve our models on our infrastructure including TPUs right and we are in our seventh generation of TPUs and uh we built our first version in 2017 um I remember talking about it at Google IO probably people didn't pay attention to it because like you know why are you building a specific machine learning accelerated chip uh look it plays out everywhere to your earlier question on cost per query in search the reason we feel comfortable we can serve it at that scale scale uh because we are constantly innovating through each generation including chips which are really really good at inference right and ironwood which is our latest in our TPU series a single part of ironwood is over 40 exoflops right and and so the scale of these things are incredible and and we have thought about our in all the way from subc cables to the scale at which we do infrastructure is unparalleled and I I've always viewed that full stack approach, you know, deep infrastructure foundational fundamental R&D on top of it and then you build and innovate on top of that and I think that approach will serve us well over time. But it really, you know, empirically plays out in, you know, the the cost at which we are able to provide our models. Part of the reason we've had a lot of traction with Gemini 2.5 series is not only are they great models, but we are offering it at a very attractive value. Uh, and we can do that because, you know, we we are driving our infrastructure cost down on the $75 billion in capex for uh 2025. You know, obviously majority of that goes into servers, data centers uh and so on. uh servers being the vast portion of it. Uh I would I would say on on looking at 2025 uh and looking at the compute part of the uh spend uh half of that is going towards our cloud business in 2025 and obviously that is a very different uh u uh it's a very different business to search and so on. So uh a lot of it is to power the innovations in uh from Google deep mind pushing the frontier and we're doing it across many dimensions right not just large language models but you know even there doing it across not just text images video etc building uh world models right so there's just a lot of uh innovation which we are pushing on the frontier obviously to support our core products like search YouTube gemini etc but 50% of the compute goes towards Google cloud

Let's just talk about chips for a second. This is a big part of the conversation is Nvidia's got the real market monopoly in AI is what everyone says. Um, do TPUs provide a wholesale replacement for your need for NVIDIA in the supply chain or is NVIDIA still a core part of the mix in the data center for training versus inference uh in LLM versus other models? Maybe just share your your understanding of where like the mix evolves to for you guys.

Look, first of all, at a high level, Nvidia is a phenomenal company. Uh, you know, Jensen is awesome. We we have been working with Nvidia now for a very very long time and we continue to do so, right? And we serve a lot of the Gemini traffic on GPUs as well, right? And so we give customers choice, etc. Internally, we train our Gemini models on TPUs, right? And and and we serve it that way across our products. But uh we use both and uh I do think look I I do think everyone in the industry is going to try and do something like that. But uh but you know it's it's you know Nvidia's R&D uh and and their ability to drive that innovation uh their software stack is world class. So you know they have a lot of uh advantages as a company and I have extraordinary respect for them. Uh you know but we've always had you know we are committed you know we are actually deploying GPUs internally as well. I think I like that flexibility and and uh but I I I we are also long-term committed to the TPU direction as well. So I think it's a good combination to have both and and I think we push each other um you know and drive the frontier forward.

Just going back so there there's an infrastructure advantage inherent in all of the investment that's been made for 20 plus years and the continued investment. A lot of folks have said that some of the performance in foundational LLMs is kind of starting to plateau and as a result we're seeing a less kind of differentiated landscape amongst the competitors and that's should be a consideration for Google. That's the outside kind of narrative. Can you share a little bit about and then I want to come back to nonLLM models where there's other advantages for Google in a minute but maybe just on this point how much more um of uh an opportunity to continue to evolve LLM is there where's Google's advantage lie in maintaining better performance in the models over time I think maybe it was Andre Karpati who used the term AJI which is like he called it artificial jag jag intelligence right so I think the progress is not going to be always smooth, right? Like you you go through these periods, it looks like something slow and then you see a paradigm breakthrough, etc. And it's been going like that for a while. uh I think obviously over the last couple of years uh you know all of us scaled up on pre-training and then there was a lot of momentum with post-training and then with inference compute uh and and and now you know there's progress with how do you take all that and stitch together in agentic workflows and you know and and and so on. So I do think there's a lot of progress and it feels pretty continuous to me, right? I think it's both true progress gets harder which I think will distinguish the elite teams at least on the foundational side. Uh you know I think I think I think that that might be a factor. Uh I felt the the harder the problem is I think you know we are well set up for that. Uh I think I think we are well set up for that. I do think we are uh pushing the research frontier in a much broader way than most other people beyond just LLMs transformer based models I mean diffusion you know do you do uh diffusion based models all those areas we are exploring in a deep deep way right so um and you know there's always the chance that we may reach a point where you know you quite don't get that returns to the additional compute you're going put in, but I quite haven't seen it yet. Right. It it the progress looks maybe harder because you're now dealing with a lots more compute. So, you're really running into the loss of like can I actually get as many electricians as I can to build the data centers at the speed like I you know all that stuff. But I haven't seen or at least talking to our researchers haven't seen anything fundamentally hey like we are not going to be able to move past this point or something like that.

Does Google have a data advantage with YouTube or other products or services? Are you able to train on that data in a way that others can't?

I think we have the opportunity to create much better experiences for people. I think people use products like Gmail, Calendar, Docs, YouTube, search etc. So with their permission taking that personal context into account I think we can deliver much better experiences. We

Are working on that, but it's it's something on which we have to deliver. But I view that as one of the uh uh differentiated innovation opportunities we have ahead as a company. But it's something we are thoughtfully working on. Uh, we'll make progress there. That makes a lot of sense if search evolves.

And I've been using a lot of voice AI tools. I find them incredible. I can have a conversation, access the news, dive deep on a topic. It's just it's so incredible. What do you view the future of human computer interaction being 5 to 10 years from now as AI evolves?

As computing evolves, am I looking at a screen? Am I typing in a chat? Am I using an AirPod and just getting audio? Am I doing audio plus a screen? Is it just a personalized interface? And there's no even concept of the web. What does the future look like for accessing information and pursuing my interests in life as a human using compute?

It's a great question. Uh, I do think, you know, the answer has got to be, you know, we've always—you humans have adapted to computing, and it's always been that way—but over time the answer will be that you need to do less of the hard work, less of the adaptation, and computing kind of works for you, right? And that's the holy grail, I think, and and we are making progress, right? Be touch, be voice, everything inches us towards this future.

Um, for example, when I wear AR glasses—I already wear glasses, so it's not that, you know—but the AR glasses aren't quite as comfortable as my normal glasses, but they're getting there. It's obvious to me that that'll push it to the next level of seamlessness where it kind of is ambiently there and doing stuff for you. So, I think that's the arrow there, you know, the air of uh uh how it'll, you know, it has to be more seamless and just be there for you, you know.

Will it be like neural link down the line, right? You know, like, you know, when I when I want to understand something, you know, is it is it that seamless, right? You know, I I think all of that is a possibility, but I think in the immediate world, given you're going to have really natively multimodal models which can take, you know, audio, vision, language, uh all of that and be there in your uh line of view, so I think when AR really works, I think that'll wow people.

Um, I'm not talking about immersive displays. I'm talking more about AR glasses, right? And I think I think that paradigm looks very interesting to me having used it. You can kind of feel that next leap, right? Where uh I think we'll all enjoy using it in a way, but you still have a little bit of system integration challenges to work through. So, we have maybe a couple cycles away to get to that sweet spot, uh what smartphones were in around 2006, 2007. So, but maybe that's the next leap, right? And and so probably that's what's exciting for me.

Are you spending a lot of time on hardware?

Yes. Right. I think um we are definitely excited about be AR glasses, the next form factors, um, you know, uh robotics is another area, all that. Uh, and we obviously build Pixel phones, uh, you know, we build vast data centers, so we are definitely in the physical world. You can think of Waymo as a big robot we are driving around everywhere, so we're making—with our partners—cars that way, so definitely yes.

I just want to zoom out and look at there's this competitive landscape that's emerged for Google that maybe maybe it's always been challenging, maybe there's always been competitors, but they're getting a lot of money and they're investing a lot of money, more than to compete with Google.

How have the founders of Google—I've seen both of them recently. Sounds like Sergey's spending time here. Uh, they both independently shared with me that this is the most exciting thing they've ever seen in computer science and it's transforming everything. How engaged are they? How much time do you spend with them? And what's your relationship like there?

They are, you know, obviously fortunate to have both of them uh involved in their own unique ways uh deeply. I talk to them all the time. Um, look, I think both Larry, Sergey, I, you know, you know, credit to them. They always envision like where AI would be. I think, uh, you know, I think I think their ability to understand trends and and you know, I I swear I've had conversations maybe as early as like 15, 20 years ago about moments like this with them. I think they are both would argue that this is the most exciting uh time in the field, uh, you know, and and I they both engage in their own ways. I think Sergey is definitely uh spending time with the with the Gemini team at a uh, you know, in a pretty hardcore way, like, you know, sitting and coding and uh spending time with the engineers, uh and that gives the energy to the team, which I think it's unparalleled, right? Like to have a founder sitting there looking at loss curves, giving feedback on model architectures, uh how can we improve post training, etc. I think I think you know it's a it's a rare rare place to be.

But you know my favorite conversations are sometimes when the three of us sit and talk. Uh, the combination of—I mean they are very nonlinear thinkers. So I feel like it expands the conversation into ways which you always don't expect and out of it which comes interesting ideas. So I think I always have access to that, but I think I've worked with them uh for such a long time, you know, you know, there is friendship, respect, mutual dialogue. Uh, we love doing that and uh and I think it's uh we'll I'll always have that.

Your competitors out there have active founders. OpenAI has Sam, XAI has Elon, Meta has Zuck, and um Microsoft has Satya. Are you willing to kind of share your perspectives on those four competitors, both the companies and the leaders?

Look, it's a obviously by by definition it's a very impressive group, right? And um I think I think you're talking about some of the best companies, some of the best entrepreneurs, uh uh all that. Look, I I you know it shows how uh both how much progress we are going to see because you're basically talking about many people uh who are working hard to drive that progress. Right? So to the earlier question when you were talking about are we going to see progress, the answer has got to be yes because of the uh you know the the unique types of people here pushing progress, right? So look, each of them—they're they're different people. I I fortunate to know all of them, and I think maybe only one of them has invited me to a dance, not the others, uh right, but I just look—I spent time with Elon maybe two weeks ago um when I talked to him, and his ability to will future technologies into existence, I think it's just unparalleled. So like look, these are phenomenal people. I respect all of them. U you know there's partnerships involved, there's competition involved. But if I were to step back and say, you know, at the end of the day, I love, you know, driving technology progress in a way that impacts people positively, um when you think about areas like healthcare and and other important areas, education and you know like we are now talking about—this is why AI is so profound—so the opportunity is what excites me. I think all of us are going to do well in this scenario, that's how I think about it, right? I think that's what a lot of people don't grok, and I think this is an important point. Everyone out there says there's competitors, there's a winner, and everyone else is a loser, but this is an entirely new world that's going to be a lot bigger than the world we had last year, and uh everyone's building down their own path, but there's going to be a lot of success. It's not just that who's going to beat whom in the in the marketplace. When the internet happened, right? Google wasn't even around. Right. Right. So, we obviously—So, the the other thing you can say is there are companies we don't even know, haven't been started yet, their names aren't known, might be extraordinarily big winners in the AI thing, right? So it's it's going to be—AI is a much bigger uh landscape opportunity landscape than all the previous technologies we have known combined combined, and so you know so which is why I think it's all about uh you know the companies which will end up doing well or you will do well because you're able to innovate and execute with the best talent, you know, that's that ends up being the driver.

Well, let's talk about that, and let's talk about the unknown competitor. DeepSeek popped up. Tell me about your impression of the model, the performance, the rumors about the next model, and what does that tell you about what's going on in China and what's going on that we're not seeing?

Look, I think the main moment from DeepSeek was uh look, always, you know, if you if you kind of follow the AI research and scan through papers and read them, no, nobody who does that would underestimate China, right? Like, you know, so when you look at the amount of research output from China, right? Um, they have extraordinary talent. Um, and and and so but I do think all of us had to adjust our priors a little bit after the DeepSeek moment, which was like, wow, they are even closer to the frontier than most people maybe assume, you know, and so I think I think that was a moment. I think internally for us, uh I think externally people are very impressed and rightfully so with how efficient their models were. Interestingly for us internally, we benchmarked it to PaLM 2, and you know PaLM 2 was uh as efficient or, you know, you could argue in some ways better. So you know, I think I think to our earlier conversations, I I do think this is more maybe internal baseball for us, you know, we were benchmarking and saying, look, it's good to see, you know, because they had to work in a hardware-constrained way, I think which is what drove a lot of their uh innovations and efficiency improvements and you know uh and so I was pleased with that, but you know it tells you that the frontier is is uh evolving rapidly; there are more players closer to it than people fully realize. And it's going to be a very dynamic moment uh in the industry. I think China will will will be uh very very competitive on the AI frontier is just what I always assumed and much of the narrative, and I think probably the fact around the ability to deploy AI at scale is one that is predicated on availability of electricity.

Mhm. Even Elon, and I've been talking about this for a while on my podcast, but Elon this week is saying, "Hey, I need a terawatt of compute." Uh, a terawatt is roughly the power production or the electricity production capacity of the entire United States. The US is going from 1 to 2 between now and 2040. China's going from 3 to 8, and there's probably upside given all the new electricity production technologies that they're rolling out now, which will be additive to that. How much is electricity generation going to play a role in who is going to economically benefit from AI over the next 10 to 15 years, and where is the US compared to China and maybe where is Google?

Well, look, you are uh definitely uh hitting on you know what is uh you know when you when you look at any system you want to find where the constraint is because that's what like gates the whole system, and you are rightfully identifying uh the most likely constraint for uh AI progress and and hence by definition GDP growth and all that stuff, right? So I do worry about it a lot, um but you know the answers or you know sometimes you run into challenges which are you know you have to solve, you know, you're running into physics barriers or something like that. This is not a problem like that, right? Like we already know the technologies that can work to supply the demand we need. So it's more to me an an execution challenge, right? I would I would phrase the energy problem as uh it's obviously multifaceted. Uh, but I think I think be it really embracing—we shouldn't have innovators' dilemma in the energy sector, right? So we should lean into all the possible innovations ahead, and there are many of them. Obviously, first of all, people perpetually I think will underestimate solar, right? You know, solar plus batteries will end up being huge. Uh, you know, obviously uh the amount of innovation that's going into nuclear, geothermal, all of that are uh opportunities to uh uh embrace and more—I'm not mentioning—but I think you know upgrading the grid, uh you know solving for transmission, uh you know permitting to make all of that progress faster, and then actually I think we maybe workforce constraint, like, you know, to my earlier point, right? You know, I think we are all—if you look at the number of electricians leaving the workforce versus suddenly all of us—and if you you project out this demand, there's a huge mismatch, right? So literally how, you know, how do you make sure there is incentives and workforce development to address shortages like that over the next decade will end up being important policies. Uh, I think we are fortunate, you know, people like Secretary Granholm and Secretary Buttigieg, I mean they are very I think deeply aware uh of the issue, and I think they are uh hitting hitting the problem hard, but I definitely think it's solvable, but I think we all have to put our mind towards it.

But for your business today, you don't see electricity constraining growth in the business in this moment or in the projectable future.

No, I won't say that, right? Like, just for example, we are supply-constrained this year in our cloud business, right? And when we are—all of us are simultaneously looking to scale up data centers, right? So we are running into real constraints. The way the constraints play out today is delays in projects because of permitting or you know not having access to electricians, all of that is realities all of us are dealing with, right? So if this trend line continues, the pace at which we are all ramping up—and obviously for it to continue we all have to generate the returns on it—and you know and and so it has to really impact the economy in a more substantive way. So they go hand in hand; if the trend continues these constraints will be much more visible. I think today we are all working through these constraints, uh so I think there are real constraints today, uh but I expect it to—for us to be competitive with China etc.—I think we have to solve these constraints in the near future.

What does that look like then? Fast forward 15 years. The US has 25% of the electricity of China.

Mhm. Is China just bigger GDP in that moment? Is the pie going to grow for everyone? You know, how do we kind of think about how, you know, the way I've assumed is the US is always—there's never been a time where the US just doesn't meet these moments, right? So to me I look at it and say it just means that you know the capitalist solutions will innovate through this moment, right? That's why people are working hard to build SMRs and uh nuclear fusion etc. So I've kind of assumed we will meet that moment, and if we don't or if if the if the lines don't match, I think the conversations will get louder and louder till we meet the moment. Uh, that's that's the way I internalize it.

There's a history of Google investing in innovative technologies and being ignored or being told that they don't make much sense. Good luck. The TPU is a great example. The acquisition of DeepMind is a great example. The investment in infrastructure is a great example. The insane continued investment forever in Waymo. Yeah. Is a great example. And suddenly it looks like Waymo's on track to be a hundred billion dollar business, and this is actually going to work. Mind-blowing persistence and patience. By the way, we have—we are doing the same patient approach in many other areas. That's my next question. Quantum is one. So tell me about quantum, but you know, but because everyone ignores quantum. You've had this investment for some time. Why is quantum so important? Because again, I want to use the historical data that it does—it seems like a small bet. Good luck. But what does quantum evolve to from a compute perspective for humanity, and when does that happen, do you think?

Obviously quantum has gotten a lot more attention in the last 12 months or so. Uh, but we've been—just like Waymo—we work through these things whether there's attention from the outside or not because we are working on these things out of conviction on the long-term trends. Right? So it's it comes from those first principles. Um, obviously the universe is fundamentally quantum. Uh, you know, to to do any kind of large-scale uh simulations in a way that truly represent nature, you know, you would need uh some versions of quantum computing. I think to me quantum feels like where AI was around you know 2015. So I would say in a 5-year time frame you would have that moment where some a really useful practical computation, you know, is done in a quantum way far superior to classical computers, and that'll be that aha moment, uh you know, I think which will really show the promise of the industry. Uh, I'm absolutely confident that we will get there when I see the progress and I can pattern match to progress in the other fundamental areas we have worked on. So it really doesn't feel like—obviously look, these are very challenging areas. You may hit a constraint. I do think a lot of people are making announcements in quantum. So in some ways it's tough to distinguish them. We had the same scenario in self-driving maybe 3 years ago. There were so many people doing self-driving. It looked like everyone was roughly the same, but they weren't. Uh, I could internally tell the difference that how far ahead Waymo was. I feel that way about our quantum effort too. I think there are a lot of announcements, a lot of noise in the industry. There are few good people, but like, you know, you know, but I I do think we are at the at the frontier there, and so you know I'm I'm pretty excited about it in a 3 to 5 year time frame, but we'll be patient and get there.

Yeah. Do you want to speculate on a business in quantum?

Look, I I we are committed to, you know, in almost all these cases, our goal would be to, you know, demonstrate more and more useful practical algorithms and show progress on that and and give access to it through cloud, right? And and I think, you know, I always say it's tough to project innovation on top of a platform, right? Nobody could say just because you had smartphones and GPS and payments, something like Uber would get invented. You couldn't linearly sit and project Uber from the underlying innovation. That's how the world works. And so uh for me quantum is that foundational—again, just like AI—there's going to be extraordinary innovations on top of it. We don't know the algorithms yet. It's almost like trying to predict how people would use personal computers in 1977 or something.

That's right. We're very early, and you know some of the the constraints in quantum are that there aren't quantum computers to test them out, new algorithms to test them out. There's a lot of theory in quantum algorithm development, but not a lot of testability, experimentation at this point. We are working on all of that too. I think we'll have more exciting moments to share this year. So look forward to making—I think that that's what's interesting. It will expand people's minds of the potential of what you can actually do. Right now, no one really knows how to think about quantum, where it's going to take us. But those announcements, I think, are going to be really prescient. And then I'm assuming all your friends will show up and say, "We've got a quantum effort," now, too.

Tell me about robotics. I think this was going to be the year of the robot. We see so many models being trained on simulation data or real-world kind of observational data that are then being used to control physical systems. Call it physical AI, call it robotics. Lots of startups, lots of big companies. Google bought Boston Dynamics and a bunch of other robotic companies. I think Andy Rubin was overseeing these for a while, and then you sold them off and decided it was too early. What's your point of view on the opportunity in robotics today? How does Google play here?

We are definitely for robotics, you know, we again have probably, you know, one of the most advanced frontier R&D teams in the world now, uh you know and and the Gemini robotics efforts around vision, language, action models etc are world class. I do think um you know robotics uh you know so we are now thinking through how we either partner or where we actually bring products out. You are right; we we tried the application layer too early, uh where I think robotics wasn't really being influenced by AI as much, but now it's it's really the combination of AI plus robotics that gives that next sweet spot, right? And and so we are uh making plans there, uh nothing to share today, but you will see us make more announcements in the space, but we are definitely uh foundationally driving uh the underlying uh models and—We are building state-of-the-art models there. We are working with partners and testing it. You know that when I look at the progress of humanoid robots etc., I mean they are uh you know in the past I would say, oh, these—this is obviously uh you know you can see how janky they are. Now I have to take 5 seconds to look at it and say closely and say is this fake or is this an actual robot doing it?

Right. Right. like already I'm in that moment and so and so you can see the progress uh in the field underway. So I think you know we are probably two to three years away from that magical moment in robotics too, and so so that's the next exciting phase is a good way to think about it. That Google could potentially develop the Android for robotics and ultimately have a broad play here.

Yeah, we have Intrinsic, so one of our bets is effectively doing that. So uh supporting uh robotics manufacturers, um you know we we are committed to having the Gemini as a model, you know, will will take all modalities into account, work very very well for robotics. It's definitely something we are committed to. Being on how we actually bring products out—first party versus third party etc.—is where we are thinking.

I want to talk a little bit about culture, which seems to be a key differentiator on the kind of competitive landscape. I go back to thinking about Google offering free food, massages at work, 20% time as a way to attract and win in the early days of the talent wars in Silicon Valley, early 2000s, and and and that persisted. But what happened is it grew and it became more amenities, and the narrative is that Google ended up creating a culture that kind of moved away from more accountability and performance and was much more about coddling employees. Can you just comment on kind of your observations on the evolution of Google over the 20 years that you've been here and what you've tried to do lately as a leader? How you think about the culture you want to foster and what you're doing about it?

Look, I think it's important to step back and say, you know, the underpinnings of a culture in which you really invest in employees and and you empower them and and even some of the perks was to create a a a culture where it's positive, optimistic, you're in an innovation mindset, people are talking to each other. Maybe by giving lunch here, people are all sitting and talking ideas through lunch, you're cross-pollinating. Imagine. So you know that is the thesis of it. Not that we are trying to give lunch to people, right? And and so I till today feel you know we still get a lot of innovation in the company at all levels of the company, and I think people wake up um you know and say, well, I can go do this. Notebook LLM etc are great examples, right? And so people do that all the time. So I think empowering employees has been and is and will be a source of strength for Google, right? I think we can attract higher caliber people who feel like they have agency to do that, right? And but that doesn't mean like you know I think I...

Think people shouldn't confuse that with, like, today, for example. You can take something like Google DeepMind. I think there's, all the way from Demis and others, you know, an extraordinary leadership team: Beko Rai, Jeff Dean, Noam Shazeer, etc. All these leaders have strong opinions on how to drive that frontier forward, and and that's happening too, right? So I think it's important to strike a balance, balance, uh, between the two.

I think when you empower employees a lot, in some ways, like, you know, we have allowed for more free speech than other companies. That's one way you can think about it. So you're going to hear voices; sometimes you can hear like what is effectively 500 people in the company, but that doesn't represent the company as a whole. So, in some ways, we are different from other companies and can confuse users on the outside, I think. But I think overall, look, we have a clear sense of where we are going. I think we want to empower people, all in the service of our mission.

So if anything, you know, over the past few years, and you are right, there are moments—not just us, but as an industry, I think—uh, I think some of the other things became more of the focus than the mission of the company and why we are all here, right? Like, we are we are not all here in the company to resolve all our personal differences or something. We are here because you're excited about, you know, innovating in the service of the mission of the company and and the impact you can have. And so bringing that focus back, that's something I've been very deliberate about for for the past few years. And I think it needs reinforcing.

I think one of the lessons for me was we all grew so much that you assumed everyone always understood those underpinnings, but then when you added so many people, you realize you have to go back and repeat that a lot, uh, to to help people internalize that. We've done that, and we do that all the time. Uh, I think moments like this help a lot too. The current moment is just genuinely both so exciting and so intense. It actually reminds me a lot of early Google, right? You know, when I walk into the GDM building, you know, some of our earliest engineers are all sitting there working together. People come in 5 days a week at a minimum, right? And so you have that intensity, and you have that excitement, and I feel that same sense of optimism.

So that's what I'm focused on, right? You know, to me that's the hardcordness which matters, right? Like, are people smart? Are people really working with a passion? And that's where that intensity comes from, and and you have to work hard to create that, and you know there are pockets of the company if that doesn't happen. You figure out what are the changes you need to make, uh, to to do that, right? And sometimes, for example, I recreated the notion of labs, right, and and and because I said, well, there are things that are possible with 10-person teams, and so we need to go and do that again, and and there are quite a few projects both we have shipping and are underway to come which will be an outcome of those efforts as well.

So you know, your culture, your values are enduring. Culture is something you're constantly tweaking to make sure you're true to your values, and so by definition there's going to be drift, and you know you work hard to uh uh snap it back.

Was there a moment in the last 10 years where you said, "I've got to spend more time on this?" Oh, you know, uh, for sure. I look, I think COVID was such a big distortion to to the to our way of working, right? So, you know, fundamentally Google was designed to be uh a culture in which people were seeing each other, engaging with each other. So, losing that continuity, right, I I think definitely impacted our culture. So when we have we've gotten people back uh in a 3-2 model, and some teams are, you know, work uh beyond that, I think it's been important. I've I've spent time to get those connections back, like, you know, for example, GDM. We were intentional in creating a physical space where we can get all of them back in the same building, both in London, both in Mountain View, and and and taking our newest building, um, you know, with that kind of a tent-like roof structure and putting all the people in and being intentional about it has made a massive difference.

Have you found a shift in your ability to recruit top talent? A lot of great talent has started other great companies; other great companies in Silicon Valley have recruited folks. I know there's always a talent war going on, but has there been a tenor shift for Google in the last period of time because of some of the underlying advantages in AI or some of the cultural changes that are underway? The talent market, you know, we go through these fierce moments for talent. AI is one of them, and whenever there are these, Google, you know, obviously we we are fortunate to have some of the most talented employees, so we are a source. I'm equally proud of the fact that I think Googlers have left to start over 2,000 companies, right? And and so you know there is a virtuous cycle. I think people come back; we acquire companies. I think all of that keeps the company uh fresh, but in the current AI moment, look, I think we are both holding on to critical talent; we are recruiting. You know, I always look at the tip of the tree: are we able to attract the best PhD researchers coming out of the top programs? And the answer is yes. Um, you know, and and there are people who have left who have come back, and so I feel good about the position we are, but you work at it hard every week, every month, and so on.

Do you think this is going to change in the future with how we do education and how AI plays a role in education? Are you going to be able to identify, recruit, and then teach and train talent out of high schools and at an earlier age? And the traditional kind of college education system is going to change because of AI on-the-job training. There's a lot of potential to change. I just, there's a part of me which feels maybe we've all misunderstood what colleges are about, and maybe colleges are about that community and people getting together and exchanging. So it, you know, there may be intangibles which which would still uh maybe make the uh uh uh you know, it more valuable than like we all perceive it to be, but um but the way I think about it is you're going to get extraordinary talent at more places around the world. So that's the way I think about it because people have access to with AI. So you don't need to be in a few certain places to be that uh that great uh great talent. So I think the nature of that changes.

By the way, I think it's an important thing to internalize. We often talk about talent. We've always been able to recruit the best talent uh in the country. But now there's extraordinary talent emerging in other parts of the world too. So I think it's something not to lose uh line of sight on, and you know, maybe that's the way I would think about it.

So just taking a step back, zooming back, I had a conversation 10 years ago with Larry Page where he talked about the transition from Google to Alphabet. Alphabet is going to be this holding company; it's going to discover or develop the next hundred billion dollar revenue business. At the time, I think Google wasn't quite at hundred billion. There have been a lot of these investments and other bets since that time. Do you still think about Alphabet as a holding company? Are there still multiple businesses that you want to kind of stand up and foster and have this kind of holding company model? Is that still hold, or is Google really the core engine that's going to continue to evolve and continue to have ancillary businesses that are, you know, somewhat adjacent to Google?

I'll answer it two ways, right? So I think the way we are not a holding company in the sense that we are we are not just like looking to invest capital in other attractive businesses; that's that's not who we are, right? Uh, we are, you know, from a foundational technology basis, if you can take that technology and that R&D we do and identify problems in which we can innovate and bring a differentiated value proposition, we'll do do that, right? So that's the way we approach, and so the the structure is an outcome of that, right? So and which means you will have businesses uh on on on on paper they may look like very disparate, but there's a common strand underneath them, right? So like, Mayo is going to keep getting better because of the same work we do in Gemini and AI over time, as Google Cloud to search to YouTube to Isomorphic to robotics, etc. So that is the unifying layer, right? And and then it's a continuum. Is Google Cloud a Google business or an Alphabet business, right? You know, we segment it out, right? And so uh so you know so the the the branding matters less, I think, right? You know, we'll have a range of companies; some of them will leave and IPO out because maybe that's the best way they can make progress, so all of that is a possibility. But what I think I, founders think about is like the underlying innovation by which, so we think at the units of quantum, right? You know, AlphaFold and hence Isomorphic, right? You know, self-driving and and building the way more driver and hence all the businesses on top of it, so it's more maybe maybe that's how we think about it, right?

Does X still play a big role in driving innovation, and you continue to invest there? Yeah, look, I think if anything, um, X over time, look, a lot of lot of these innovations did that come out of X, right? And so, um, including Waymo, uh, you know, the early incarnations of Google Brain, right? Yeah. So, so I think, uh, X as an incubator, um, allows us to, uh, you know, push the boundaries. They're thinking about uh tapestries, thinking about the grid problem, um, that are uh you know uh extraordinary, but but it's all rooted in computer science, physics, kind of a deep uh technology R&D, and I think that's the foundation across everything we do.

As we wrap up, um, I want to ask you one last question to hopefully frame your experience of the last 10 years as a CEO. Biggest regret, biggest mistake, and what you're most proud of.

Proud is obvious. Look, I I think I think we have, as a company, I think there aren't that many companies which can push the technology frontier like you don't hear of companies winning Nobel prizes often. That level of foundational R&D we do and then apply it to create businesses and value. I think I think we've done an extraordinary job at that, and we aspire to do that. You know, I'm I'm really proud of that. I think we're pretty unique as a company that way. You know, there are a lot of small regrets, you know, by nature. I tend to look forward, and I learn from mistakes we make. But look, there are acquisitions we debated hard, came close, and you know, some of them are just—give me one name or get in trouble. Maybe Netflix, right? Like we debated Netflix at some point super intensely inside. So you go through these moments, right? and and uh and uh you know and so uh I wouldn't call it regrets, but you always look back and like you know like you know in in a world of butterfly effects there were alternate paths, but maybe they are in a different part of the multiverse.

Yes. Yes. I always um tell people I I think they underappreciate the role that Bell Labs played in driving innovation and ultimately human prosperity in the early 20th century. And I do think a lot of people underappreciate the role that Alphabet is playing in driving innovation across so many different lanes, which drives prosperity, businesses, competition, all that stuff aside, the innovation that's being driven out of Alphabet continues to impress and benefit us all. And so I want to thank you for your leadership and the time.

Thanks, David. Real pleasure. [Music] I'm going all in.