Amazon AWS CEO: AI boom is just getting started

Matt, it's great to be with you. I feel like what's happening right now at AWS feels impressive and in a lot of ways. So you're the world's biggest cloud computing company. You're the highest capex vendor. You have an estimated, I think, like, 44 gigawatts of capacity, which is that's coming under development in the US, which is more than Google and Metacombined. And then what just happened was Andy Jassy on the earnings call saying AWS could be a trillion dollar revenue business in the next five to 10 years, which is a huge number, obviously. And my math is correct. I mean, based on the run rate you're already on, that requires roughly 40% compound growth over five years or 18ish over 10 years. And this is off of a massive, massive base. I think a lot of people and they heard that were like, wow, like what are they seeing? And then I was also looking at the way you all have been talking about the business over the last couple quarters. And I think it was at the towards the beginning of this year, Andy was saying, we think roughly 600 billion by around 20, 36. And then in Q2, he floated the trillion. And then in Q3 was like very, it felt very strong. It felt like a very strong declarative moment. And I'm curious, like, maybe we can start here like what happened between Q2 and Q3? Yeah. Well, if I step back, you know, I think it's, we have probably for 20 years thought that the AWS opportunity is just a massive business opportunity and unlike one that we've seen before. And we've been, so I've been with AWS for 20 years and we've been growing that whole time. And I think we over time have more and more and more gotten conviction that the business opportunity is just really, really, really large. And you know, it's surprising, but at the point that we're at now, we're at $182 billion. We're in right business. And so you're over $180 billion. And you're growing at 39%. That there's not many companies that hit that spot. And as we've seen, we've actually increased our year-over-year growth rate each of the last six quarters. And so as we see that acceleration, what happens there is we've always had this view that the majority of workloads still live in on-premise worlds. And as big as the cloud has gotten, there's still a lot of data centers that people have. There's a lot of workloads that live on-prem. And there are more workloads that are just being created every single day. And I would say over the last two years, really what we've gotten excited about is this flywheel where customers now see that there is a massive opportunity that AI affords them to gain efficiencies, to build new businesses, to create new customer experiences, to rethink how work gets done, to build faster for customers. And they don't get that benefit if their core workloads aren't in a cloud environment. If they're running in an on-prem database, if they're running in an on-prem mainframe server, if they're in a VMware cluster in their legacy data center, they just have a hard time having agents talk to those pieces, pulling data from all those different data stores. It doesn't work. And so we've actually seen is customers already saw benefits of the cloud. There's cost improvements, there's agility improvements. There's a reason why the AWS business has been growing great for the last 20 years. But it really accelerated when people realized they weren't going to get the benefit of AI if they didn't get those workloads in the cloud. Those core workloads is part of it. So we've actually seen our core business really accelerate over the last couple of years. And so the more core workloads get in the cloud, the more they can actually take advantage of AI. And so there's this real positive flywheel effect where actually the revenue growth that we're seeing is significantly accelerating. And I think that's just further bolstered our confidence in in the growth of AWS. I think the other things things that we're seeing is just what's possible. I think a couple of years ago, you saw incredible content generation. You saw incredible conversational chatbots being made. And then as we moved into worlds of agents where work was really done, we're now just seeing customers really scratch the surface of what's possible where you have a fully autonomous agent taking advantage of what we see are really powerful models out there that can run longer and longer and do harder and harder things. And they're really starting to think about and see how core parts of their workloads can be automated, improved, and built upon. And that opens up effectively all of the things that enterprises do. And it opens up the pie for everyone. It's not like you're taking from where they used to spend over A and bringing it to be. It's actually creating new businesses, creating new revenue streams for our customers. And they're seeing cloud as a way to get to those places. And so we see that growth across tens of thousands of different startups enterprises, large companies. And so when we see that growth where they're we see companies just really getting scratching the surface of the benefit that they're getting. And we're seeing this flywheel effect. It gives us a lot of confidence that there's a huge amount of business going forward for us. And it's far from certain. We've got to do a lot of execution. There's a lot of invention. There's a lot of things that we need to do between here and there, but the opportunity and the space for us to go after and help customers go and grow and take advantage of these technologies is bigger than it's ever been. This episode is brought to you by Granola, the AI notepad for people in back-to-back meetings. It works everywhere you do unless you focus on what matters. Try it at granola.ai/sources and use the code sources for three months off. This episode is also brought to you by Mercury, AI native banking that's loved by more than 300,000 entrepreneurs, including me. Visit mercury.com to learn more. Mercury is a fintech, not a bank. Check the show notes for details. This episode is brought to you by Jira by Atlassian, where teams get the context, visibility, and control to move work forward. Try it free at Jira.com. That's j-i-r-a.com. AI is causing companies to come from on-prem to the cloud. That's driving a lot of this growth. Absolutely. Interesting. I hadn't thought about that. I mean, you've been saying, and the company's been saying for so long, like the world, we underestimate how much of data is still on-prem, and for people who don't know, just means like it's not in a cloud. It's somewhere local. What you all are seeing is people who have resisted the cloud are going towards it because of AI. We'll use this as just take something like an analytics workload. An analytics workload, traditionally, a customer might say, great, I've got a data warehouse. It's running in my data center. I could move it to the cloud and take advantage of Amazon's analytics capabilities or move it to a snowflake or a data bricks or something like that. That would be great. I could do that. A lot of people did, but they're also like, yeah, but I'm still able to do the analytics here, and it's working, and maybe I won't move it, or maybe I'll do it next year or two years when I can break the project work. We saw those migrations happening, by the way, but they're slow and measured and scheduled out. Now we see people saying, okay, actually, I can ask an agent to go across all of my sources of data, and not just can I do my analytics faster or cheaper, but with new technologies that are available in a cloud world, say something like a duck DB data engine that's working across all of my different data sources, I can actually have an agent go and do all of that, and when I have an agent do that, it has to have all of the data sources accessible. They have to be available to the agent. It has to know where all the data is, but I actually get very new insights and actually can build new businesses, unlock new sources of revenue, new source of profitability when I can do that, except one of my sources of data is locked up in the mainframe. So it draws all of it in because they kind of can start to see where that benefit is, and I think there's a real risk that companies that don't make that move, they're getting passed by by the ones that are in the cloud. How much of the growth is big customers wanting to spend more versus the long tail? Well, look, big customers are big, so they spend lots. So a lot of it is that, but I will say that the long tail is growing bigger. It really is across the board, actually. And as we think about the long tail, I think it's both, you know, it's not a lot of our growth is driven by like, you know, dentist's office, like not that, that, you know, not really kind of real, real small SB's. Those are mostly served by a lot of our partners that actually run their software and services on top of AWS. But our customers, we have a really broad swath of whether it's, you know, global top 5,000 enterprises. Actually, just this week, I was in the Bay Area meeting a bunch of startups that many of them started less than six months ago. And they're really thinking about how do they use AWS to accelerate their growth, accelerate their technology development, accelerate their route to market where, and our customers love having access to that capability as well. And so it's across the board. Sometimes it's the very largest banks, the very largest healthcare companies, the very largest governments in the world. And sometimes it's a two person startup that has an idea and just wants to build. And we're seeing growth from all of those segments. In fact, it's never been easier to build than it is today to be a two person startup and go try something and actually get it to scale. And I do not think I've found a two person startup that goes and tries to find data center space and rack a server to start getting going, right? Like it just doesn't make any sense. And so that's been true for a long time. But now they can use AI to really further amplify the impact that they're going to have and how fast that they can grow. And we're seeing those, those new companies scale really rapidly when they get that product market fit. And AWS and the cloud gives them a chance to cycle until they get that product fit. Something that does feel new though about this moment, I agree with you, but when you scale right now, especially if you're AI heavy and you're doing a lot of inference, you can, I think it's lint of fireworks, but I've heard the concept of like you can scale into bankruptcy. There's a sense of like, you know, you can't scale because of the compute, which you have most of. So I'm curious how you think about that and how you advise companies and startups you meet with that are facing that issue. because I think it's - Yeah, it's a very real issue. And actually we're thinking deeply and actually investing in more robust cost management tools for companies so that they can do exactly that. Customers want tools that on the range from shut my account down when it hits this dollar amount. Like I don't care, it's okay. Like I'll figure it out, but I don't want to spend more than this to alert me when I hit this dollar amount. To if it's an inference, maybe ratchet my usage from an expensive model to a cheaper model or you know, and we're trying to think about what are all of the tools and controls that we can give customers so that they can actually feel confident about that cost management. And even larger customers, by the way, it's not necessarily with the small customers that are worried about, you know, spending $10,000 and not being afforded, but even large-cups companies are saying, you know what, I may want this particular person to be able to spend largely on control, but you know, I'll extend your personal account, you know, it's $100 and then I shut down what it is. And so really giving those fine grain controls so that companies of all sizes have that, I think is incredibly important. I think from a capacity point of view, that is it is a real concern. And it's part of why we're spending so much on CapEx and investing in this infrastructure. We're of this fortunate position. I think a lot of people are worried about things like concentration risk when you're spending capital and things like that for us. We spread that usage across, you know, we have tens of hundreds of thousands of millions of customers that are all using AWS. And so when I see all of, you know, a lot of that comes from a broad swath of companies, that's where we feel confident that we can go and put down a lot of capital and really grow the amount of compute that's available for customers because we get really good signals as to where they're all growing, what it looks like, what demand is gonna look like next year, what it's gonna look like the year after that. And we have good signals and many of our larger customers will commit to five, sometimes even eight, nine, 10 year commitments on their spend with us. But most of them are in the kind of, you know, three, four, five, six year commitments. But that gives us really good visibility as to what their growth is gonna look like. And oftentimes those customers, frankly, will outspend their commitments. But it's more a baseline, but it gives us a good sense of where that growth is gonna be. And unfortunately, you know, well, fortunately, unfortunately, but right now the demand is far outstripping the supply. And so even spending as much CapEx as we are investing as much as we are ahead in capacity, I anticipate that we'll be short of capacity for the next couple of years. - Can you see into 2030? - Some, you know, I think you can see trends. I can't personally otherwise, you know. - You're an alchemist. - The guy's a guy. - Yeah, I do some sports betting or something. But we get signals, right? And there's, you know, how we think about it. Actually, it a little bit goes back to the kind of the supply chain routes of Amazon, where we really think about, how do we manage our full supply chain. And then at AWS, we really think about, how do I spend money at the last possible minute so that I have as much possible information as I have, while giving myself option-valued to have, to hit the upside, right? 'Cause I'd like to, the optimal is I would have exactly as much capacity as I need, you're never gonna be perfect. You don't wanna overspend, and you don't wanna wildly underspend in that CapEx. And so what we do is you think about what are those long lead time components that you have to go spend on. You have to buy land, you have to acquire power contracts, you have to, you know, get permitting, things like that. Then as you get closer to like landing a data center, you might have to buy networking equipment, you might have to buy generators, you might have to buy some of those other pieces. So if you're going all the way up to 2030, I might be buying lands today that I plan to use in 2030. We're not buying servers, you know, we're not buying all of those things. And so I can wait for a lot of the CapEx expenses that I would need to support my 2030 proposed growth until much closer to 2030. And so I don't have to have perfect views into 2030. You can have directions on where they go. And if we're wrong, I could probably resell land, and it's, you know, worst case, you lost a little bit on appreciation or not, but mostly it's a durable asset that you can resell. And so we think about our investments kind of stage that way, and it takes some of the risk out of there as we think about it, but it requires a lot of long-term planning and thinking and watching kind of the various variables that you have in there. - Everyone in the markets feels like is looking for reasons to doubt the compute trade, and you've seen it with CoreWeave, you've seen it with the NeoClouds in particular and the debt markets. I'd be curious to hear what you make of the moment wherein you would, I think, kind of alluded to it earlier. But it's, you all are in a different spot, obviously, but I do think that there's systemic risk a little bit that everyone's trying to figure out, like how bad is it? - Yeah, I don't think it's systemic risk, actually. I do think that the risk is localized because the bets that people are making are materially different. And everybody has to kind of make their own bets. I think when you've completely debt-financed at a relatively high rate, you have a much smaller error bars as to when your cash flows are gonna land. You also have much smaller error bars when you have high concentration of customers and concentration of workloads. And so if you go and look at the workloads that are in many of the NeoClouds, they're really large training clusters for the most part. And those really large training clusters are largely being bought by a small number of customers. And they're all debt-financed at relatively high rates. That is not the business that we're in. We both have data centers and computers, but other than that, the business is actually quite different. Most of our businesses spread across the millions of enterprises and startups and small, medium-sized businesses and everyone governments that are running on top of us. And much of that business is core compute. It's compute clusters, it's HPC workloads, it's websites, it's backup storage, it's databases, it's a lot of that kind of workload. And then the AI workload, the majority of our AI workloads are inference today. And so that inference is effectively just, if you really look at inference today, it is just another building block as part of an application, right? Your applications all just have AI embedded in them. It might be AI that's helping somebody serve better ads. It might be AI that's helping you auto-suggest something in your email. There's a whole bunch of things that it might be doing. It might be AI that's helping traders figure out like what their next stock market trade is going to be. But it's just part of the application that they're using. And so that is a very distributed workload. And so even if the usage is concentrated on Anthropic and OpenAI and a handful of model providers, the actual end users are thousands and thousands and thousands of folks out there running inference for us. And so the systemic risk of one or two companies not doing well, even if it's one of those model providers, it'll fall over to another. People aren't going to stop doing inference. They'll find a different model to use. We might be an open-to-nate model. They're going to have computers. You can put it on those computers. And so for us, that risk is very different. >> You will have very large investments in OpenAI and Anthropic, and they're a huge partner, obviously. >> We want them to do well, just super well. Of course, I think everyone's with on pins and needles about Anthropic IPO. But I'm curious about the training versus inference workloads you're seeing because you have such great visibility. Do you expect training to be a smaller piece of the pie going forward? And it feels early on inference, but I think everyone's trying to figure out how big is inference going to actually be? I mean, you see stats like 2% of Americans have even paid for AI or companies are barely using it, and yet inference is exploding. It's like where does that tap out? And then what does that mean for the training piece of the pie? >> I think, by the way, that stat just seems a little bit misleading. It may be the 2% of the people have paid directly for a model usage, but a bit higher percentage people have a Netflix account. >> Sure. >> And it recommends a movie to them, so they actually have used it. >> Yeah. >> So it's like, it is yes and no. Like it's like how many, what percentage of Americans have paid for a database, like probably a really small percentage, but they all use it on a daily basis anytime they log in somewhere. And so I think that's a misleading stat into how integrated it is into every single application. People are going to the Amazon website every single day, and when they search for product, the AI is kind of embedded in how they're searching. >> Sure. >> They pay for it, but they kind of paid for it. And so from that perspective, I think inference is a huge business. It's massive, and you've seen in some of the anthropic filings, kind of what their revenue looks like. So it's a very good and large business. I think training is a good business too. It's just very concentrated. And so we'll continue to support training clusters. We do support training clusters today. But it's the minority of our business for the most part, just because the main source of our business we want to do is support that the millions of customers we're building and growing on top of us. >> How early is inference, do you think? >> Super early. I think you're almost doing a disservice to say that it's at the like 1% of what it's going to be. >> I don't think it's even 1%. I think there is a massive runway. And look, every time we see inference get 50% cheaper, we see an order of magnitude more usage. And so people kind of ring their hands. They're like, look how cheap it's getting. And then, and yet the revenue is higher every single year. I think that we're still very early on that path. And it's why we also feel good about the investment we're making with regards to infrastructure, because I just think there is so much more that can and will be done. >> Do you think about that span that's driving that inference is it coming out of existing budgets or is it net new? Like, where's that capital? I mean, if you're talking like potentially trillions of dollars in inference that's happening, I'm just like, where is that coming from? >> Yeah, I think it's all those things. I think that sometimes it's creating new value. Sometimes it's coming out of other budgets. And I do think that there's no one answer to that. I think it's a lot of those things. Sometimes it's allowing you to create products and services and capabilities much faster than you could before, which drives revenue. We'll call it like new sources of revenue. Sometimes it's driving efficiencies where it's supplementing. I don't need to necessarily hire a new person. I can get twice as much work done with the person the thing that I have, and I think both of those things are true. What do you make of where the country is on data centers, especially given the midterms? What happened in Texas, where Governor of Campaign on, you know, pausing, dolled-outs, one. And I know you guys have your approach where you do a lot of, you know, renewable energy. You know, you're very conservative with water, but at the same time, I just think it's such a lightning-rod issue. Yeah. And it seems like it's going to get worse before it gets better, maybe? Yeah, I think, look, there's a couple of things here. And I think it's incumbent on us to help educate folks as to that all data center producers are not created equal. And that's true for lots of industries. But today, it's largely painted with one brush. And I think that there are a handful of us in the industry that spend a lot of time thinking about how do we make sure that we are great citizens and great community members as part of this. And we learned this early on with some of our fulfillment centers and other things like that. And we learned with data centers, we need to work with local politicians. We need to work with community members. We need to show that we're bringing high-paying jobs in communities. We need to make sure that we have low impact on the communities that we operate in. Our data centers, you know, we bring power and pay for all the power we bring to the grid. We oftentimes in many of the communities we work in actually add, reduce the tax load by thousands of dollars per person in the communities that we operate in. As you think about the environmental impact, we continue where the single largest creator of new carbon zero energy sources over the last five years in the world. And we continue to bring more carbon zero energy to the grid every single year. Huge investments on that on that front. We were very early in thinking about how we use about water. Actually, we have a commitment that by 2030 will be water positive where we bring more water to every community that we operate in than we use. And even our data centers, our largest data centers today use less water than 40 US households. It's like a block, like a block of households. Like it's a very, it's actually a relatively small amount of water. I saw a stat that like golf courses use more water. By far, my golf friends don't like that I use that stat. But yes. Yeah. But yeah, I mean, there's actually, I mean, there's, there are a lot of things out there that use way more water than our data centers. There are ways to run data centers that use lots of water, so we don't do that. And not everybody takes that path that we do. And I think that it's brought a bad name to people that proudly stand up and say, you know, I go against regulations, I don't even listen, I don't get permits. I just built a data centers anyway. They kind of like proudly go against working with the community. That is just not how we do it. Unfortunately, it's kind of brought this bad name to data centers. And I think it's something we're going to have to work through. But I also think that many of the folks, and even you mentioned the midterms, we work quite closely with many of the folks that are both Democrats and Republicans. And we have. And we will continue to do that. For five different administrations. And they appreciate it. Like they will work with them. We don't always agree on all topics, but we work their debate there and figure out how there's win-win scenarios with those things, and we'll continue to do that. So I feel very good that about where we are, and I'm excited and congratulations to everybody who won in the elections and we feel good about what we've brought to communities and where we'll continue to go. I think technology is so important to our economy today that we can't just turn it off and decide to not have it. I don't think that that would be good for our country. They don't want that. They want that. They think they do. But then if you ask them, like, you're going to give up your Netflix subscription, they're like, well, no, not that. And be like, okay, you're going to stop like Instagramming your friends and like, well, okay, not that. You're going to stop asking Amazon to deliver stuff to you, no, okay, not that either. But those things all require data centers. Like it's like, there's a weird disconnect there. And so a lot of the things that people want, I think, is I think they want responsible. They don't want the kind of technology leaks to tell them what to do. But they do like it when people build good products for them that they want to use. And our view is that there is a great way to do both of those things. You don't have to do that. So when you woke up the next day after the results and you saw everything, was there anything you thought about like, man, what do we do? What do we do as an industry? I mean, you've talked about what Amazon does. Like does the industry as a whole need to do more? Is there anything the industry can do? I did think, and we've been working with several of the governors in the states that we operate at. And thinking about how do we help codify some of the stuff that we actually do and some of the things that they want to do into more structured programs that everybody has to follow. And I think that's one of the things that we need to do to vary to both clearly highlight why it's good to have a data center in your community because it actually brings a tech base, a tax base, it brings high-paying jobs, and why it's important for the country. Because the alternative is technology will still progress. We would go, they would happen somewhere else, and I think that would be bad for the security of our country. It would be bad for the economy of our country, but the world is not going to stop. And so kind of thinking about how do you balance what's great for communities, great for people, great for our country, and good and reasonable for companies so that they are still incentivized to go work on these problems. And I am very convinced that there is a path there. Framer is the AI website builder that powers the sources podcast website at podcast.sources.news. Framer brings AI agents into the same canvas where your website is designed, managed, and published. 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You get oversight, track agent progress right alongside human work, spot blockers sooner, and connect your work to goals. You don't have to choose between shipping fast and staying in control. I want to get your reaction to like three areas that people are talking about putting data centers right now. Other space, ocean and desert. These are people are excited about these two varying degrees. Maybe we can start with space. What do you think about data centers and space? I think that it is highly unlikely to be economically feasible or practical in the next decade or two. Decade or two? Yeah. Okay. So that's a no on space then. I mean, here's the thing is it sounds good. You get free energy and it's up there somewhere. That's great. Number one, launches are still relatively infrequent and expensive. You know, I'm trying to fix that, but yes. Okay. Yeah. To your point. Yeah. Yeah. Yeah. Like it's a great. I appreciate it. Yeah. But one person's not going to fix it. Right. And still, there's, we as a world don't do one launch a day today at the whole world. In the data center, we're landing in a data center today, one GPU every 14 seconds. Wow. So we don't do a launch a day for the world. And that's just us doing one GPU, you know, no, there's obviously like, so let's say one launch. And by the way, a rack of GPUs, you know, might weigh a ton. Yeah. So I don't even know if you can stick that on a launch vehicle or how many you can get up there. You know, people talk about gigawatt data centers and megawatts of racks and whatever, like most solar panels and things drive kilowatts, not gigawatts. So you need these gigantic things that are up there. They're still, they're really hot, right? And everyone's like, space is cold, space also does have air, actually dissipating heat and space is actually quite hard. And so that's actually, it's, it's just a really hard environment. There's radiation everywhere. So you have to build things. It's going to be way more expensive to build servers from a serviceability point of view. By the way, these are all technically solvable problems to take a while. And it's just going to take a while. I'm not saying any of the-- any one of these things that I can highlight are not impossible. And I just think that it's way far out. Oceans though? Oceans, there's some of those same problems. I think you get cooling, which is good. You have things floating in the water and moving. It's a little harder. So I find oceans to be also probably impractical. I think deserts of those three of you mentioned are by far the most likely. You have relatively good environmental areas. You actually have heat sinks. You have solar areas that you could do it. I think deserts are very interesting. And potentially, also when you go to liquid cooling, actually cooling the data center becomes actually a little bit of an easier problem in some dimensions with regards to energy usage at least. So ocean or deserts could be an interesting place, particularly because you can also lay a cable. It turns out that actually the data transfer and stuff is pretty useful. And there's no people there, which may be good for the climate we're in. Well, yeah, I mean, yes, although then there's no one to benefit from an increased tax base or anything like that, too. There are-- it turns out people like living in towns. They like where they have high-paying jobs. There's a bunch of things that like having those things nearby. It just means the high-paying jobs aren't going to be in your town. The tax base is not going to be in your town. There's a lot of those things that are not great. That people kind of say like, oh, actually, that do want that. I'm really curious what you make of all of these inference providers that are popping up. So based on fireworks, modal, foul, the list goes on. And they're servicing a very important growing part of-- I guess you could call it cloud-- stack, a very AI-native, very model-heavy tuning. And then there's a whole post-training inference world that's developing. When you look at that, and you all obviously have that as well, what do you make of that? In your mind, is that a new kind of cloud market that's forming, or is it just a moment in time? How do you think about that? I don't really view it a cloud offering. I think it's more like a kind of application or services that are built on top of the cloud that are both complementary and in some ways competitive, but mostly complementary to what we build. And so think about, in the database side, MongoDB building on top of AWS, or think in the analytic side of like a snowflake or a Databricks building on top of AWS, inference providers building on top of AWS. That is net positive to the customers. They wanted it in their-- they wanted it in AWS. They want to use that inference with the rest of their AWS stack. It's complementary. It may be on some dimensions competitive, but we've operated in that world for a really long time, where many of our products both compete and support customers. And we've built AWS from the very beginning knowing that partners building on top of us are going to be an incredibly important part of what we offer to our end customers and have always been a huge part of what we go and build. And our view is, if partners is successful building on top of us, then we're going to be successful. And it gives customers more choice. I kind of put them more in that bucket. I think they do a great job. They do a great job of helping customers use open-weight models, which are oftentimes hard to use, hard to optimize. You have to be kind of an expert. They do a great job of helping with that, where customers think they may have interesting data or interesting ways to use open-source or open-weight models, either to make them cheaper or different capabilities. And I think those folks all, and they all have different kind of slices as to how they help. But they help customers and we like working with them and they're a great part of our partner ecosystem. And I guess part of what I'm getting at there, too, is how much of the AWS advantage do you think about being the software layer versus the bare metal? And especially in a world where an agent can write the integration and recreate things. And essentially, you could really see a world in the near future where agents are picking clouds and doing everything on behalf of their companies. And in that world, what level of the stack do you need on? Have you been thinking about that? Yeah, I think we think it's important for us to play in all of the layers of the stack just to be super clear. And so I think all of those are incredibly important for us. I think at the very lowest layers of the stack, there's just not that many-- we often think there's three basically right now. And I don't know that that probably will continue to be roughly the same. Number of companies is still there. And then as you kind of move up, I think it's a funnel that kind of keeps going that way. And then you get to end user applications where there's thousands and thousands. There's bigger and smaller ones. But there's lots of those up there. And we like to play at all of those layers of the stack. We like to play at the unstructured layer. We like to make our own chips, make our own networking gear, like all of those kind of things at the very lowest layers. We have services. We have managed databases. We have managed storage offerings. We have managed analytics services. And then with the top layer, we have applications as well where there are applications for customer service with Connect. We have applications for worker productivity with quick and applications for developers with Ciro. And we try to compete at all those layers of the stack. And the inference, and as you go up, there are more people that also build on the lower layers of the stack that we have. And then compete at that layer. A lot of these inference players are binary. And so they're more in this middle layer, right? Where they might buy from AWS and build a service on top of that and run that. I think at the lowest level, we kind of don't really partner with many people other than hardware providers and things like that, because we're kind of the base layer of that thing. But then as you go up, it's more a competition environment where it's great. We encourage that. And so I think it's important for us to be at all of those layers of the stack. I think we want to help customers run inference directly on us. We want to, and for other folks that are building inference engines, whether it's OpenAI, whether it's Anthropic, whether it's Fireworks or Base 10 Optimized Skin open weights model, whether it's open weights models directly, whether it's our own model, whether it's all of those different things. We want to make sure those are all available to our customers. And in the fullness of time, we're not there yet, but we'd love to have all of those available in Bedrock. It's not technology wise able to do that yet, but we'd love to make them all available if they want to be there. Otherwise, if they don't want to be there, that's OK too. But we want to help it make it as easy as possible for our customers to use whatever price performance capability makes the most sense for their application. Because even if we don't win the upper layer stack, our view is, if it's built on AWS, we'll win. But we think we can do a really good job of offering kind of a bunch of those capabilities because we can control more of those pieces. And we think, you know, we have good teams to build services that are valuable to our customers. Speaking of that lower part of the stack, I'm very interested in your all's decision to start selling training chips externally. I haven't really heard you talk about that. Why are you doing that? I mean, Google started to sell TPUs externally. Is this just pricing power and keeping a market that's kind of like revolving around you? Like, what's the reason for doing that? Well, look, we've made a huge investment in training and it's gone really well. Like, you know, and people tell us that they want to be able to use training in more places. And-- But why not make them have to use it in AWS? There are only so many-- like, we're not going to build every single data center that exists in the world. Sometimes they're going to be on-prem, whether it's in a country somewhere, whether it's inside of a government somewhere, whether it's-- Oh, so there's like a Sovereign AI component. It could be, you know, there's lots of places. And if you look at, like, everywhere that Nvidia sells chips today, they sell a lot of their chips to, you know, us in Google and Microsoft. And they sell a lot of their chips to other places. And like I said, I think only, you know, 85% of workloads are still on-prem. There's still a lot of on-prem locations. And customers have told us, like, hey, we'd love to run a lot of our workloads inside of the cloud on training. But we may want some of it in our own data center. And we think there's a really good business there and we're excited about it. And so it's one that we will be investing in. And doing more and more over time. How do you decide compute allocation across the fleet now when you've got to deal with startups, the guys in the garage, you want to build something massive training clusters with huge equity partnerships you have with large labs. Like, how do you balance that? It's hard. It's-- we spent a lot of time doing it. I wish I had some, like, magic-- Do you have, like, daily allocation meetings? It doesn't need to be daily, but we have regular allocation meetings where we actively debate how we think about where we can. I'd love to say yes to everyone. But yes, there's a finite amount of capacity. We're at it. And I said, we're adding a ton every day. So there's oftentimes we're able to say yes. Sometimes we have to go back to customers and say, I know you want it in two weeks, but it's going to be in four weeks. Or I know that you want it in the East Coast, but it's going to be in the West Coast. Or it's going to be internationally, or it's going to be in India, or wherever you have capability, or wherever we have capacity. And sometimes they'll say yes. That works for me and sometimes no. And then you kind of go back and iterate on some of those. We're very intentional about making sure that we have a diverse customer base. And so I don't want to sell out 100% of my capacity to just the large labs or the large technology companies. I want to be able to say yes if an enterprise comes along and says, hey, I need 10 GPUs. And I think that's part of the AWS value proposition. And so we very intentionally set aside same for startups, by the way. There's going to be some startup. If I could choose to sell out every single accelerator that I have that I know is landing in 2027 and just be done with it. Yeah. We choose not to do that for exactly these reasons. We want to make sure that I can say yes to a bunch of enterprises that come in. We want to be able to say yes to a new startup that hasn't even formed yet that we'll have capacity for. And so we do, we kind of have a portfolio where we do commit a good chunk of our capacity. Well ahead and commit it for a long term and we keep some behind so that we can make sure that we can say yes To a bunch of those folks and then we of course save a bunch of capacity for bedrock so that we can support as much of that As possible last time you and I talked we we talked a little bit about Nova. This was earlier in the year Um, and you're also frontier model ambitions and you were very clear like no We need to be at the frontier. I want us to be at the frontier. Yeah, and then since then there's been some changes Some I can't I haven't been able to quite tell I wanted to ask you like what's going on because there were some layoffs and Nova models being sunsettered or put on ice or something and I'm curious like is this you all deciding that no like We don't need to have models at the frontier in house or what's the what's the strategy? Uh, no far from it. We were uh, we're just as invested in that Um, strategy as we've ever been and I would just say, uh, you know, stay tuned. We're quite excited about it. It's more of a reset It was more of a reset. That's right. That's right. I think we Identified some opportunities to learn from some of the approaches we had taken before and and uh, but it is just as big of an investment Just as important from our long-term strategy perspective We we think that um, customers are gonna want a first-party frontier model from AWS and um, and we're very invested in and go in that direction Anything on what you learned in terms of what's kind of driving this reset that you can be more specific on? Uh, no, I think we just you know, I think we um, did a reset and make sure that we had kind of the right talent in place Some of the the learnings with regards to making sure that we had kind of some of the right data pieces in place and The right infrastructure in place and learning a little bit And uh, about how to do some of those things at scale and I think we've we've made some good Made some good learnings there and and the team is cranking away and doing really well right now. How are you feeling about The safety piece of agents right now uh, and you know, what customers are saying about I'm worried about stuff going rogue And then also just companies, you know, banks are sending out emails to customers saying, you know, don't let your muse, you know log in blah blah blah Like there's this you all block news actually. I mean, there's this crazy moment where these agents are starting to traverse things and companies are going What do I do yeah, whatever your conversation's been like on that? Yeah, I'm totally with the team and customers Yeah, I think those are two different things. I think um one is uh when we talked to our customers that are building agents Uh, I think that very clearly they we have to help them with some more tools to help on on building Um, agent safety and ways to to to manage and control agents and I think at some point At you know agents and and um And AI models are just tools to get work done and we've got to do a good job of helping our customers make it easy for those tools to stay within the guard rails and and do the work that they want it to do Without you know too sharp of edges um so that they the you know, they don't cause harm to their companies or leak data or or have some sort of um, you know, big kind of security disaster Um, but um and so that that's on us to go and vent what those look like I actually think one of the big technology unlocks I that I feel really excited about is um, it's largely called neuro symbolic AI, but effectively it's this field where Um, actually amazon has been probably the the single biggest investment that we make internally in research is around we call automated reasoning And it's this uh, it's it's applying mathematical proofs to um computer science problems to prove things to be true And so um, a tangible example of that is when you go and create a new permission and I am permissions model in in AWS You run it through I am access analyzer and and and it basically looks at the whole possibility of all of the permissions that you have inside of your org And it can tell you is this going to conflict with some other permission right like this one says you should have access This one doesn't and it tells you those conflict you better go figure that out and or or we won't accept it And so if you just brute force try to go figure those things out It's like more computers than the earth has to figure out that calculation But through mathematical proof you can actually do it really fairly fast and get to a hundred percent Proof that this that these things verify ability that's right scale X plus Y equals Y plus X if you just brute force try to prove that you of course camp Yeah, there's an infinite number of combinations of that thing But math can prove it in like you know three steps or whatever it is right And so if you can take that and you can basically say I want a non-deterministic agent that's going to be able to go out and try to Process an insurance claim right? I want it to go figure things out like it's hard It's got to go out look at some data sources and try different things But I want to make sure that it never breaks this rule or this rule or this rule or follows the policies that I have if you could have Uh, an automated reasoning agent that basically helps steer and put and build guide rails to your Um, you're kind of core AI agent that basically could Deterministically prove that it would never break invariants I think you could actually build a set of tools there By combining automated reasoning with traditional kind of AI systems That could give provingly correct agents that would kind of follow rules And so that is an area that we're investing a lot in and uh, and it's showing a ton of promise and it's still early But tools like that can be really powerful and I think it's one of the things that could unlock where It can imagine an insurance company saying wow, I would love to just in a fully automated way let my agent process insurance claims now It's like people can submit insurance claims they get you get an answer back in 10 seconds Like it would be game-changing as opposed like wait a month to figure out if You know you get a claim from your insurance agent Because they'd be comfortable sending those things off in a fully automated way It's where you know actually the experience gets better for customers. There's probably more business that they can do But today they're not comfortable because you actually have to have a human in the loop That's that's one of the areas where I think if we can really unblock that and it's one of the areas we're spending a lot of time thinking about It has game-changing and it's one of those places where if you do unlock that The usage suddenly goes through the roof even more and it's why I think we're just that. That's the early stages of what you think about the timeline of deployment on that It depends. I think there'll be some next year. Okay, um, and we're gonna be keep iterating it and growing it over time You know, this is like the next big bat. It sounds like it's one of them It's one of the ways that I think you can add determinism to without taking away the benefits of the non-deterministic AI models And that could be the answer. Yeah, to a lot of these safety issues. That's right. That's right. That's right You can imagine a you know, you can imagine if you could if you could define a rule that tells an agent You will never try to escalate your privileges. Yeah, all of a sudden it doesn't matter if you have like a magic box that It could escalate as privileges. It won't try when you look out into the future And you're thinking about the mix of like open versus closed tokens Uh Do you how early do you think we are on open weights and open token volume? Do you do foresee that? the majority of that Infraints in the future will be open or do you think it's always going to be pretty balanced like have you if you feel much about that? Yeah, I don't know it's it's uh, I think there's a lot of great use cases for open weights um I do think that a lot of the frontier labs are doing a lot more work to lock down their models to make distillation harder Yeah, and so I think there's some chance that the trajectory of how fast open weights models keep progressing will slow down Because it depends on how successful they are at doing that what happens with China and yeah, yeah I mean look there's no secret that I'll almost all of those open weights models were because they were able to distill from from uh frontier models and so Uh to the extent that others are are able to the frontier labs were able to block some of that either by hiding a reasoning Tokens are encrypting or whatever it is um that you may see some of that slowdown You may you may not like there may just be others There's there's also plenty of folks that are working from first principles on building their own open weights models And so I expect that others both in China and around the world you're here in the USA other places And so expect that that um those will come up and and still be quite powerful because there's a lot of use cases that I think people want To country to really control the weights there. They just want to control You know either they want to do some things like mix in my own data Whether you know through through either fine tuning or post-training or whatever it is Although you know It remains to be seen that if the frontier models keep progressing how valuable that tuning is over time The other one is of course cost you you've you've already seen some of the frontier model folks uh Respond by offering like much like you know it turns out they can distill their own models much better than anyone's can kind of try to secretly still their model And so they can create a small cheaper version of their own model and offer it for cheaper You saw both opening an anithropic um do that yeah last couple months here And so that's a very interesting um market dynamic that I think we'll try to see play out Because I don't think they're just going to see 90% of the tokens to open weights models They're incentivized they've already made the fixed-cost investment They can just go make their own model and and sell a very powerful distilled model that they own Um at a cheaper rate So there there'll be some interesting market dynamics to see what happens there Um, I don't know our job our the way I think about it is we want to make sure that we support whoever's successful and whatever works out there And right the way hopefully as long as they're all running on bedrock where we're and we make them available and useful for customers that Yeah, um that they do well. Okay last question. Okay. AWS generates about 23% of Amazon's revenue, but I think like 63% of its operating income Amazon is a lot of things. It's a lot of companies within a company Yeah, but I'm curious about AWS. I mean you've been running it for and involved since the beginning Um What is it like being a CEO of a CEO of AWS inside Amazon this day? It's like like yeah, like for people who don't understand like There's this massive thing, but it's also part of a of a bigger thing like yeah, what is that like? Well, I don't know. It's just kind of what I do. It's your life. It's what I do. I don't know if it's that's but no, it's fun Amazon is a fantastic company to get to work through. I will say One of the things and one of the reasons I've of you know, just really enjoyed working here for last 20 years is that it is a company that lets you go build. It is a company that that encourages you to think big, that go after like a massive opportunity and go build and make it happen and some really ambitious things. Like no one would have thought 20 years ago that an online bookseller would be one of the largest technology companies in the world, powering governments and banks and but you know, when we pitch it, Jeff's like, that's awesome. It's a great opportunity, it's a good one to go after and we empower our teams to think big about what they can go do and no one would have expected that Amazon would be one of the largest database company in the world. Those are just things that you wouldn't have expected except we took a really smart team and said go invent a new cloud database and they came up with Aurora and it was really popular and here we are. And so that is a really fun environment to operate in. It's a great place to work where every day you can come and just say like, what are the customer problems that I can try to solve today? And I'm going to go after the very biggest customer problems that startups have, the governments have, that regulated industries have, that my own companies have. And operating inside of Amazon is great because you kind of have some built-in customers who can use your services from a database. And so I feel incredibly lucky that I have one of the largest applications in the world in the retail website. You know, when we were growing up and starting AWS, I mean, how fortunate are you to have one of the largest, most complicated technology applications in the world, banging on your services and telling you I need this, I need this, this doesn't work, make this better, this doesn't scale. They hit scale before every one of our customers did as you might expect, expect for many things that we do. And so it's a fun place to invent and sometimes it's crazy, but like it. >> All right, Matt, thank you. >> Yeah, thank you, thank you for having me. Granola is the best AI notepad I've tried. It works everywhere on a video or phone call in person or an Apple Watch. Try it now at granola.ai/sources and use the promo code sources at checkout for three months off. Banking should feel like modern software. Get everything you need in one place. Mercury's a fintech, not a bank. Jeremiah Atlassian gives teams the context, visibility and control to build with clarity and ship with confidence. Try it free at gira.com. That's j-i-r-a.com.