Amazon CEO Andy Jassy on Reinventing Amazon for the AI Era

Even spending $220 billion this year, we're leaving a meaningful amount of demand on the table. We always leave a certain amount for on demand, but '26 is obviously sold out, '27 is largely sold out, a good chunk of '28 is sold out. The financial markets are saying, 'Oh, there's not enough money in the world to finance these things.' Everything we do in the world today — stock market, banking, healthcare — they're all using data centers. Our job is to try to figure out how to have as much capacity as we can. We have an opportunity to help across so many more customer experiences and make their lives better and easier every day.

Thanks for having me.

Oh, 2021 feels so long ago. It feels like, you know, a decade ago. Yeah. But, you know, it's as you know, the world changes fast. And, you know, if you compare it to 2021, you know, our overall topline's about 80% higher, AWS business about almost three times bigger, our ads revenue is about two and a half times bigger. So the business has just, you know, grown quite a bit. And I think there are a few things that have really happened. You know, I think pretty quickly after I started in the role, which was on the tail end of the pandemic, in our retail business we ended up with a real cost-to-serve challenge where we had basically doubled the size of our fulfillment network that we had built over 25 years. We doubled the size of it in about 18 months. And then we built the last-mile transportation network the size of UPS in about 20 months. And whenever you build that quickly, you end up with all sorts of things that you don't have optimized. And our cost to serve in our fulfillment network were not what we wanted. And, you know, it created a lot of challenges and angst and a lot of people at that time — I remember it was just a year or so, or a little less than a year into starting the job — people said, 'Boy, what a terrible time to start and what bad luck.' And, you know, I think in retrospect it was really a great time to start and a real blessing because it allowed us to consider a bunch of things that we probably wouldn't have considered for many years, but they were really good ideas for our customers and for the business. And the biggest of which in our fulfillment network ended up being that we regionalized a flat national fulfillment network in the US. We regionalized it so we got items much closer to customers, which turned out to both lower our cost to serve because of shorter transportation distances while getting items to customers much more quickly. And that re-evaluation of our most closely held beliefs during that time when we were struggling on cost to serve really unleashed speed of delivery and a lot of demand in our retail business, and a big piece of why we've grown as fast as we have there. You know, and of course pretty quickly thereafter just how AI exploded further on the scene. It's not like AI was new. I mean, we have been all doing machine learning for a long time and there have been lots of waves of AI that were supposed to be the real tipping points. But, you know, you get to 2023 and you really see with generative AI a huge difference. And I would say our AWS business was growing really quickly at that point. You know, we have the broadest functionality by a fair bit and stronger operational performance and security and, you know, I think a type of partner ecosystem that's different. We were the clear leader and we felt like that business could be a few hundred billion dollars of revenue over a long period of time — figure over 10 years. Now we look at what's happening with AI and how, you know, every customer experience we know of is going to be reinvented with inference and AI. We actually think the AWS business — not only is it growing really rapidly. I mean, last quarter we grew 36.7% year-over-year on a $169 billion annual revenue run rate business. Just the opportunities today are really striking. And, you know, I think if you layer on top of that the seeds that we have of new businesses, whether it's, you know, how the team has progressed our Prime Video business, which is now, you know, a large and profitable business, or the low-Earth-orbit satellite we're building with Amazon Kuiper, or autonomous driving ride-hailing service with Zoox, or healthcare business, or what we're doing in robotics — you know, we just have an opportunity, I think, to help so many more customers across so many more customer experiences and make their lives better and easier every day. And that mission of trying to make customers' lives better and easier every day really unifies everything we're doing across the company.

Well, what's interesting is, you know, we have this set of leadership principles that a lot of people know about — the 16 leadership principles — and a lot of these leadership principles, you know, they're supposed to really, and they do, govern the way we think about operating as a team and culturally. But there are several that are in tension with one another, and sometimes people are confused about that. But the reality is it just reflects the way the real world works. Nothing is really perfectly clean. You're always making trade-offs. So if you go back to that time when we made the decision to build out the fulfillment network and the last-mile transportation network that aggressively, that quickly, the question the team was facing is: we know if we go this fast it's going to be really hard. It'll probably be more expensive than if we went slower. We will probably not get everything as optimally as we would if we went slower. But customers and sellers really need it. So are we going to actually shade on the side of enabling? Remember back then, in the early part of the pandemic, no physical stores were open. I mean, if you look at the role Amazon played in everybody's lives during the pandemic, it was very seminal. And so we had to ask ourselves: are we going to build fast and maybe not as optimally as we can, with the trade-off of serving customers and sellers who are trying to, you know, also have businesses that sustain themselves, or not? That's the first decision you have to make. And we decided, as we almost always do, to shade on the side of customers, you know. And then it was fairly obvious that as we were building, it was obvious that not everything that we were planning was going perfectly. We had a lot of — you know, our fulfillment team was more in the office than most of the rest of the company, but still a lot of people that have to participate in a lot of the designing and the planning and the assessing. You had people all over the place and remote. And, you know, and then now you're balancing the combination or the tension of bias for action or speed and, you know, relentlessly high standards, which means you deliver at the cost structure and the delivery and the design that, you know, you'd always plan for. And so again, you have to make trade-offs. We made the trade-off to side with customers and to move quickly to be able to serve sellers and consumers, and we thought there'd be some amount of cleanup. Now, did we know exactly how the topography would work out in the way it did? No. And there were certain things that we didn't have optimized that I'm not sure we foresaw wouldn't be optimized. But if you have good metrics and if you're a culture that's a truth-telling culture — which, you know, that is another really important part of our culture, is that we get the data on the table, we get the issues on the table, we're direct with one another, we don't allow people to paper over issues — if you have that type of culture, it's fairly obvious in the metrics and it's fairly obvious in the discussion. And then you just have to go about figuring out how you want to improve it and which pieces are just kind of small productivity things and which are architectural pieces. We went back and really rearchitected key parts of the network after we got that capacity installed, that, you know, was probably at the time for that team maybe eight or nine on their list of priorities, but became a much higher priority in a way to actually get the combination of speed and cost.

Yeah, it's a very good question, and I wish there was an equation that would make it simple. It would save us a lot of time as a team. Yeah, I would say in the first wave of cloud, you know, when we started AWS, it was — I mean, there was a little bit of, uh, we didn't know if people would use it. Um, so 'build it and they will come.' But we had very good signals inside the company, which at the time, you know, this was around the years, you know, the early part of the 2000s, we had teams that were not moving nearly as quickly as we wanted them to, even though we felt like relatively we moved quickly. But for us, we just couldn't understand why it took so long. And it just turned out that our product teams were spending 70 to 80% of their time on the infrastructure pieces which didn't differentiate them, and only about 20% of their time on what differentiated the product pieces. And so we, you know, and we realized every team was doing it and reinventing the wheel. And so we originally conceived of AWS to allow ourselves to move more quickly internally. And we figured if we built a set of high-scale, high-availability, low-cost, highly flexible building-block services like compute and storage and database and content distribution, messaging and things like that, we would be able to build much quicker in our consumer business. And then we just said we're pretty good, relatively speaking, technically. Probably other companies have the same issues. But even before we launched externally, we had so many teams internally interested that we knew we were on the right track. I don't think any of us anticipated it would grow as fast as it did externally. On the AI side, there were, you know, as you know, there have been these different waves of people saying, 'This is the time. AI is finally here.' And, you know, even maybe six, seven years before the generative AI wave, people felt like it was AI's time. We built a whole bunch of AI services that actually was a pretty good business for us on the AWS side, but nothing like what we're seeing right now. Like in the early days of AWS, we felt like we were growing really fast and we felt good about it, and we were about a $60 million annual run rate business after three years. In the first three years of this generative AI wave, our business — or just our AI business in AWS — is over $25 billion in run rate. So it's several hundred times faster. So it's, we just haven't seen anything like it. And then the question becomes how much capex do you want to invest to go after the opportunity? And, you know, what I would tell you is, you know, we've said this year in 2026 we expect to spend about $220 billion of capex. By the way, I'll just tell you that I remember being at a leadership team breakfast, you know, 15 years ago when we couldn't believe the telco company spent $50 billion in capex. We're like, 'How are they spending that much on capex?' So these numbers are, you know, they're kind of mind-blowing numbers. And even spending $220 billion in capex this year, we're leaving a meaningful amount of demand on the table. We just — the thing that helps us here is that we have really good visibility into customer demand. And so if you think about the capex investments in AWS and the way the cash cycle works, there's two macro areas. There's the investment for data centers, and then there's the investment for servers and networking. The data center piece, you have to invest, you know, about two years in advance of when you can put servers in the data centers to start to monetize them. The good news is you only make that investment once. It's a useful-life asset that's about 30 years. But you make that a couple years in advance. The server and networking gear, it's only a few — depending on the component, it's only a few months in advance. So you have very good visibility into customer demand before you pull that trigger. If it turns out you don't actually need the capacity to fill those data centers, you won't buy the server and networking gear. On average, they tend to break even in about three years, and they have useful lives at this point of about five to six years, and most of the deals we're doing are about five-year deals. And so when you are building as fast as we're building with so many data centers simultaneously, you're going to spend a lot of capital, and your free cash flow is going to have headwinds in those years until you get to those later stages, you know, a few years down the road, where all those data centers are online and they're all being monetized and you're making back your money from the server and the network investment. But it has, because of the way the cash cycle works, it has headwinds in the early years when you're growing that fast.

Yeah. Well, I mean, I'll just give you some of the context that I have, or at least I believe, which is, you know, if you think about everything we do in the world today — well, you know, stock market, banking, healthcare, entertainment, shopping, education, business, you know, ride-hailing — like everything we do is digitally driven. They're all using data centers in a very real way.

And they all use data centers, and that's before AI. And AI is the biggest technology transformation of our time, which I think is saying something since we've lived through the internet and the cloud and mobile. And so, you know, the reality is the type of infrastructure build-out we need to support the rapid growth we were just talking about on the AI side — there hasn't been anything like it in the world, and in this country, since maybe when we built out the highways and bridges, you know, in the 1950s. I mean, that's the type of build-out we're going through. And the country — I think the world in general, but I say the US specifically — cannot afford not to build data centers, or we will find that another country like China will establish all the leadership and all the norms there. And I don't think any of us are going to like that either domestically or globally. So we have to build data centers. Now, do I think there are ways that we can be doing it better, all of us as an industry, than we have? Of course. You know, I think it's happening quickly, and I think everybody's learning together. But there are a number of what I would consider somewhere between myths and mistruths right now being propagated, because it's one of those issues where everybody — you have an easy villain, and politically and sometimes opportunists like to kind of find that villain to point at. But, you know, I'll give you examples. The golf industry or the almond farming industry consume way more water than data centers do. Just as a data point, golf courses consume 200 times more water than all our data centers, and they consume more in two days than we do in a year in our data centers. So there's just a lot of — like, that's a good example. Or you can talk about, you know, people talk about pollution. What happens is when you register new sites and you have backup generators, you have to register and show the amount if you ran all of those generators full-time at full capacity. But of course that never happens. You only run backup generators 0.1 of 1% of the year, like 10 hours a year for maintenance. So like, you know, or you talk about the energy rates. You know, if you look in the different geographies in which we have a big data center presence, some of the energy rates have gone up, some of the energy rates have gone down. But the real issue is that as a country we have not invested in the infrastructure of our grids the way we've needed to. Like, there's going to be meaningful growth, and so something was going to push it over to investment, and it just turns out that AI, because it's growing so rapidly and so significantly, is the thing that pushed it over. But building it out, of course, what you're going to find is once you actually get that capacity in most of the geographies, they're going to end up — you know, the rates will flatline or go down, as they have in Indiana or in Georgia or Mississippi. And so, you know, there are things that we can do that are, you know, better. I mean, the other thing people forget about is in these geographies, the big data center providers are paying a lot of tax. So, like, if you look in St. Joseph County in Indiana, we've paid about $3 billion of tax. The communities get that money and they get to spend it on things that really matter for those communities. So the communities get something out of it, too. We've built programs like Built Together that we, you know, we just committed to another billion dollars in the communities in which we have data center presences over the next five years. And so, you know, I think we have to keep working on it with the communities. It has to be a public and private partnership, but the build-out is so important to the future of the country. Like, imagine if we hadn't built out highways and bridges, what the economic prosperity and the capabilities of the country would be. And we have to kind of keep it in perspective, and we have to, you know, be partners in the communities in which we build.

Well, I mean, the first context piece is what I said earlier, which is just I can't believe how big and fast AI has grown. You know, it's a few hundred times faster than the beginning of AWS in the first three years. $25 billion run rate for us just in AI revenue in AWS in the first three years. So it's really large, and yet it really is barbelled if you look at how demand is. And what I would say is on one end of the barbell are the AI labs who are consuming gobs and gobs of compute, unlike anything I've seen, and then a few runaway apps — ChatGPT, Claude Code, things like that. On the other end of the barbell are enterprises who are largely using AI for cost avoidance and productivity. So automating customer service or fraud or business process automation. In the middle of the barbell are the current production workloads of enterprises, many of which are using AWS and using inference today. But I would say the overwhelming majority of which still aren't in any pervasive way. Like, you know, and that I think is going to be the largest absolute segment of AI usage, and I think you're going to see that. It's hard for me to predict the rate at which that'll happen. It's, you know, it's happening to a greater extent every year. But I think if you zoom out five years, five to seven years, and you look at what it'll be like — and I don't think it's going to wait for five to seven years. It never happens in one fell swoop. But if you just zoom out five to seven years, I think that quite a few, if not most, of the enterprise applications that we use and that exist today are going to be using inference in a pervasive way. And so there's just a lot of growth in front of us.

Yeah, it's a good question. It's, you know, we don't think of it quite the way you're talking about, but I can completely understand. If you think about it, there's only so much data center and power today. There's only so much memory, and I don't think that shortage ends in 2027. I think that, you know, I believe, you know, one of the reasons coming back to the capital investment why I think we could invest a lot more and it wouldn't be a bad use of capital is that, you know, '26 is obviously at this point sold out, '27 is largely sold out. We always leave a certain amount for on-demand in our inference service Bedrock or EC2, but largely sold out, all the big chunks. A good chunk of '28 is sold out. Like, there's so much demand that, you know, it is — and you can't — we have, if you think about the AI business and what we call core business, which think of core as non-AI, they're both growing at really rapid rates right now. And, you know, if you have only a certain amount of memory, you have to decide how much of the memory you want to allocate to HBM, you know, to the AI accelerators, versus to DRAM, you know, into your core. So it's — these are real trade-offs you have to make. But I would say that where we start on this issue is being customer-focused. And for us, we have deep relationships and partnerships with our customers in AWS. And in the same way during the pandemic, unlike our competitors who I think tried to figure out how to optimize within quarters to drive revenue as much as possible in a world where companies were going to cost-optimize, we took a different approach. We, you know, we proactively had our field go out and help customers figure out how they can cost-optimize, even though we knew it would diminish our revenue short and medium-term. But, you know, we're going to help our customers be successful because our very strong belief is if we help customers over a long period of time, our business will come along with it. And that's the same thing here on the core. You know, I guess you could do calculations and decide that you have customers because of the scarcity and their willingness to pay would pay more to squeeze out your core customers, but they're running their businesses on top of AWS. They're running all their core applications. We're not going to squeeze our customers, and we're going to make sure that they can scale. And then our job is to try to figure out how to have as much capacity as we can to allow not just our core customers but all of our customers who want to use us on the AI side, and people who want to use us on the AI side, to be able to do so and do so in a way where, you know, it's sustainably economic for us as well. And so, you know, I wish I could tell you that I thought that the supply constraints would be just a 2027 issue. I think we'll see them in different components for several years. We are — I think that we're probably putting more capacity in than anybody else because we really are very passionate about continuing to be the significant leader in the infrastructure and AI space like we've been. But as I said, we're spending $220 billion this year, and we could spend a lot more and it wouldn't be a bad use of capital, both, you know, medium to long-term. And I think we still probably wouldn't satisfy all the demand. There is just an incredible demand right now.