亚马逊CEO安迪·贾西：AI算力需求、芯片战略与多元业务布局

Even spending $220 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. The big labs being willing to pay almost anything for power, cooling, chips. Well, maybe we should buy out the old guys and sell them to the new guys cuz they'll pay us whatever.

>>: Andy, welcome to the podcast. It's great to have you.

>>: Thanks for having me.

>>: So, it's you're you're 5 years into running Amazon and you've done a lot as being here and a lot is is is coming out as CEO. You know, right now, you know, 220 billion dollars in in in CapEx spend, AWS growing 28%. You know, there's a lot that you've you've achieved. How do you think about the ambition in 2026 as it relates to Amazon relative to 2021 when you took over formally as CEO?

>>: Uh 2021 feels so long ago. It feels like, you know, a decade ago, yeah. But, as you know, the world changes fast and you know, if you compare it to 2021 you know, our overall top lines, you know, about 80% higher. AWS business about, you know, almost three times bigger. Ads revenues 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 built we had basically doubled the size of our fulfillment network uh that we'd built over 25 years. We doubled the size of it in about 18 months. And then we built a last-mile transportation network the size of UPS in about 20 months. And whenever you build that quickly, um you end up with all sorts of things that you you don't have optimized. And we 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 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 and 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 network a fulfillment network in the US. We regionalized it so we got items much closer to customers which turned out to to both lower our cost-to-serve cuz with shorter transportation distances while getting items to customers much more quickly. And that reevaluation of our most closely held beliefs during that time when we were struggling on cost-to-serve really unleashed um speed of delivery and a lot of customer man in our retail business and 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've been lots of waves of AI that were supposed to be the real tipping points. But you know, you you get to 2023 and and you really see with generative AI a huge difference. And I would say our AWS business was growing really quickly at that point. Um you know, we have the you know, with the broadest functionality by fair bit and stronger operational performance and security and um you know, I think a 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 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 um 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 I think if you layer on top of that the you know, 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 Leo or autonomous driving ride-hailing service with Zoox or health care business or what we're doing in robotics, you know, we just we 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 and and that mission of trying to make customers' lives better and easier every day really unifies everything we're doing across the company.

>>: So so a couple of follow-ups on that. So when you know, you kind of mentioned when you're building out the fulfillment network so fast that it's going to be a little inefficient. It's going to be you know, when you go that fast there's some sloppiness to it. How do you and then you mentioned there was kind of angst around that internally like from a CEO perspective like how do you communicate what you're doing without like lowering the cultural standard of you know I mean Amazon's known for its excellence in operating efficiency, being frugal, all these kinds of things, but here you are going like we're going to build UPS and like that. And so how do you reconcile those?

>>: Well, what's interesting is you know we have this set of leadership principles that a lot of people know about. We have the 16 leadership principles. And a lot of these leadership principles uh you know they 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 with about that, but the reality is it just reflects the way the real world works. It nothing is 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 from the you know 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. Um we will probably not get everything as optimally as we would if we went slower, but customers and sellers really need it. Like so are we going to actually shade on the side of uh enabling Remember back then in the early part of the of the pandemic, we there no physical stores were open. I mean if you look at the role Amazon played in everybody's lives during the pandemic, it it was 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 that um sustain themselves or not. That's the first decision you have to make and we decided as we we almost always do to to shade on the side of customers. You know and then it was um it was fairly obvious uh uh, 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 and remote. And, uh, you know, and then now you're balancing the combination or the the tension of uh, bias for

action or speed and, um, you know, relentlessly high standards, which means you deliver at the cost structure and the delivery, um, and the design that, you know, you'd always planned 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, um, that we didn't have optimized and 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, um, 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, uh, 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 re-architected key parts of the network after we got, you know, that capacity installed that, you know, was probably at the time for that team was 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.

>>: All right. Now, fascinating. Um, you know, you kind of like at the beginning, you know, when you started AWS, you like started AWS. It was kind of build it and they will come and that, you know, okay, you're building all this infrastructure and then you're going to rent it. Um, and so they needed to come or you'd be in a lot of trouble. Uh, but this is that at a much bigger scale in that, um, you and others, but like I would say primarily you, or at least you're certainly one of the biggest players, are building data center, uh, and chip capacity so fast that like the financial markets are saying things like "Oh, there's not enough money in the world to finance these things." And then, uh, you know, and we just kind of saw that skittishness in like the corporate debt market and equity markets and so forth. Um, and so how do you know like how much is too much on that build out? Given the demand is so steep and you're growing so fast, um, and then there's a contention, um, for that supply chain that you're kind of fighting for to get, you know, your cooling and power and chips and all that.

>>: Yeah, it's a very good question and I wish there was an equation, um, that would make it simple. It would save us a lot of time as a team. Yeah, I think I would say in the first wave of cloud, you know, when we started AWS, uh, 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, um, 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, um, high-scale, high-availability, low-cost, highly flexible building block services like compute and storage and database and content distribution, messaging, 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've been these different waves of people saying, "This is the time. AI is finally here." And you know, even maybe 6-7 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 in 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 3 years. In the first 3 years of this generative AI wave, our business, just our AI business in AWS is over $25 billion in run rate. So, it's several hundred times faster. So, it's, um, uh, 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, um, $220 billion of capex. By the way, I'll just tell you that I remember being at 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, um, even spending $220 billion of capex this year, we're leaving a meaningful amount of demand on the table. The thing that helps us here is that we have really good visibility, uh, 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, uh, for servers and networking. The data center piece, you have to invest, you know, about 2 years in advance of when you can put servers in the data centers and 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, um, and networking gear, 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. And if it turns out you don't actually need the capacity to fill those data centers, you won't buy the server networking gear. That on average, they tend to break even, uh, in about 3 years. And they have useful lives at this point of about 5 to 6 years. And most of the deals we're doing are about 5-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.

>>: So, actually on that, you know, data centers going back when you started AWS were probably the last thing in the world that you would believe would be a political issue. Um, but here we are. Uh, and particularly in the United States, you know, it's funny and I'm sure you've experienced this, but nobody anywhere in the world is as anti-data center as the, uh, kind of US politics for whatever reason. Um, so how do you, you know, how does that end up affecting this I guess is there is there any merit to the anti-data center argument and then, um, you know, how does that kind of inform your strategy of what you build here or you know, and

what you build in the other regions? 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, health care, entertainment, shopping, education, business, you know, ride hailing, like everything we do is digitally driven. They're all using data centers in a very similar

>>: Yeah, well, no, everybody is I'm anti-me. Yeah, it's [laughter] I don't want to use my phone anymore.

>>: And uh, and they they all use data centers and then that's before AI. And AI is the biggest technology transformation of our time, which I think is saying something since we lived through the internet and the cloud and and, um, and mobile. And so, you know, the reality is the type of infrastructure buildout we need to support the rapid growth we were just talking about on the AI side, it's there 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 buildout we're going through. And the country, I think the world in general, but I'll say the US specifically, cannot afford not to build data centers, or we will find that another country like China will will establish all the leadership and all the norms there. And I 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 I 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, um, myths and mistruths right now being propagated because it's it's one of those issues where everybody it the you have an easy villain. And politically, and 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, um, farming industry consume way more water than data centers do. Just as a data point, our data centers, like 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. And what happens is when you when you register new sites and you have backup generators, you have to register uh, and show the amount if you ran all of those generators full-time at full capacity. But, of course, it never happens. You only run backup generators is 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, I 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, uh and so significantly, is the thing that pushed it over, but building it out, of course, um you you 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 that, you know, the better. I mean, the other thing people forget about is in these, um, geographies, the big data center providers are paying a lot of tax. So, look, if you look in Saint Joseph County in Indiana, we 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 5 years. And so, you know, I think you have to 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 our highways and bridges in in what the economic prosperity and the capabilities of the country, um, would be. And and we have to kind of keep it in perspective, and we have to, you know, be partners in the communities in which we, uh, build.

>>: Let's go back to the the CapEx for a second. You know, the $220 billion you know, that's $600 million a day. Uh, and you just to, you know, put it put it in perspective. I'm I'm I'm curious how you think about AI demand right now. Off-line we were talking about how you see it as a uh a barbell and and maybe you can sort of situate or conceptualize sort of the state of AI demand at the moment.

>>: Well, I mean, the first you know, the first context piece is what I said earlier, which is just I can't believe how how big and fast AI has grown, you know, it's it's you know, a few hundred times faster than the beginning of AWS in the first 3 years, $25 billion run rate for us just in in AI revenue in AWS in the first 3 years. So, it's really large and yet it really is barbeled if you look at how demand is.

And what I would say is on one end of the barbell are the the AI labs who are consuming gobs and gobs of compute unlike anything I've seen. And and then if you know, a few runaway apps, chat GPT, code code, things like that. On the other end of the barbell are enterprises who are largely using AI um 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. Um uh many of which are using AWS and 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 5 years, 5 to 7 years and and you look at what it'll be like and I don't think it's going to wait for 5 to 7 years. It never happens in one fell swoop, but if you got if you just zoom out 5 7 years, I think that um 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 there is a lot of growth in front of us.

>>: And how do you think about it cuz there's kind of this impending data center chip, memory, everything shortage uh that's upon us in really 2027. Just because you know, everybody's building now, but it's not going to be ready uh necessarily by the end of the year and demand's not slowing down at all.

And so, when faced with how how do you think about it when faced with like um the big labs being willing to pay maybe anything or almost anything for power, cooling, chips, you know, kind of the environment. And then you've got these kind of old, been been around a long time customers who are paying their regular rates um you know, to run their businesses. Uh is there like any business pressure to go, "Well, maybe we should buy out the old guys and sell them to the new guys cuz they'll pay us whatever." And the old guys, you know, I can't double their prices. That wouldn't be fair. Or like, how do you think about that cuz it's such a weird thing and that you've got people you've had in there for 20 years doing a thing that's kind of you know, as you say, probably going to inference. And then you've got these new guys who are like insatiable. They'll buy anything.

>>: Yeah, it's a good question. It's um you know, we don't think of it quite the way you're talking

about, but I can completely understand. There's If you think about there's only so much data center and power today. There's only so much memory and I don't think that that shortage ends in 2027. I think you know, I I I believe you know, one of the reasons when coming back to the capital investment why I think we can invest a lot more and wouldn't be a bad use of of capital is that you know, 26 is obviously this point sold out. 27 is largely sold out. We always leave a certain amount for on demand and and our inference service bedrock or 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 um you know, it is uh and and you can't uh 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 if you have a only a certain amount of memory, you have to decide how much of the memory you want to you know, you want to allocate to to HBM, you know, to the to the AI accelerators versus um to um DRAM, you know, into you know, your core. So, it's uh these are real tradeoffs you have to make, but I would say that where we start on this issue is um being customer focused. And for us uh we you know, 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 how to optimize within quarters to drive revenue as much as possible in a in a world where companies were going to cost optimize, we took a different approach. We you know, we proactively had our go out and help customers figure out how they can cost optimize even though we knew it would diminish our revenue um short and medium term, but you know, we're going to help our customers be successful cuz 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 and in their willingness to pay would pay more to squeeze out your core customers, but that they're running their businesses on top of AWS. They're running their 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 in the AI side and people want to use us in the AI side um to be able to do so and uh and do so in a way where, you know, it's it's sustainably economically 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 cuz we really continue we're we are um very passionate about continuing to be the significant significant leader in the infrastructure and AI space like we've been. But uh as I said, it's uh and we're spending $220 billion so this year and we could spend a lot more and it wouldn't be a bad use of of capital and that 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.

>>: Let me see. So, kind of speaking of that, you you did something back in 2015 uh when you bought Annapurna Labs which was probably before you probably weren't really thinking, I mean, maybe you were, but about like giant AI demand in 2015 cuz there just wasn't much AI demand in 2015. So, kind of what drove that decision and then how is it kind of paid off in this new world?

>>: Yeah, I think, you know, Anna the Annapurna team is one of the most talented teams and set of people I've ever had a chance to work with, and I it's one of the very best acquisitions we've done at Amazon in the history of the company, if not the best. And it was uh we foresaw some pieces, but I don't think that we we did not make the Annapurna purchase for AI chips per se. Like what what um you know, when you get to a certain scale, uh you start to have access and visibility to patterns of customers at very large scale that you you you realize if you can vertically integrate and um and you could build um certain capabilities yourself, you'll be able to provide more value for customers and uh you know, and and better economics for the business itself. So, what Annapurna started as for us was just um on the kind of core EC2 server. We felt like we were using too much of the server for virtualization and for networking and for security and for storage. And we wanted to be able to use as nearly 100% of the server for compute for customers. And so, we we had been working with Annapurna before we acquired them on this NIC that offloaded those components to the NIC to allow us to have as much of the server for compute as possible. And we really liked the team, and we really liked the product that we collaborate with them on together. And we we realized we were going to buy that was going to end up being in virtually every EC2 server, and it's turned out to be in every server we have. So, we originally bought it because that was going to be a core capability of our compute capabilities, and because the team was so talented building chips, we felt like they could probably build other chips, too. And we at that time we had this very deep partnership, which we still have a deep partnership with this company, but with Intel. But, Intel had virtually all of the CPU share, and when you have a a partner who virtually all the share, they're not often thinking about how to drive price performance. They're they're trying to think about other features that allow them to get more share and drive more revenue. By the way, it's totally understandable. These are some of the dynamics of of how markets work. It's part of how the innovators innovators dilemma, you know, um comes up. And so, uh we had tried to to push on price performance and was hard for us to get attention there, but we had customers who wanted to move more and more of their on-premises workloads the cloud and they wanted better price performance. And so, the next thing we turned that Annapurna team loose on was to build a CPU chip. And that they built Graviton, which um uh 99% of our top 1,000 EC2 customers use it very pervasively today. It's about 40% more price performance than other x86 processors. And so, that has been hugely impactful for our customers and and for our business. And we just a few years ago we saw that same movie in the AI space where you really had one dominant provider um who's very good and we have a we had a deep partnership with. We still have a deep partnership with. We will for as long as I can foresee. But, they have giant markets and and and lots of um different products they're building and priorities. And we knew our customers needed better price performance. And that's why we we spun out a separate team in Annapurna to build an AI accelerator, which we call Trainium, which is also about 30 to 40% more price performance than the other comparable um um processors. And you know, I think when you talk about that middle of the barbell we were just talking about, if you ask what are the what are the things that will change that middle of the barbell to have more of those current enterprise production apps using inference, a really big There are a couple things. You know, you some of it is, you know, some of the talent that's comfortable doing post training and fine tuning and some of the talent and organizational muscle that knows how to prioritize what to go after and how to do it, but a big big chunk of it is the cost of inference. The cost of inference has come down quite a bit in the last few years, but it's still an order of magnitude too expensive in my opinion for it to be used as pervasively as possible because if you're asking enterprises to rewrite their applications that use CPU today with

AI accelerators, if the difference in those costs are very significant, the amount of incremental benefit they have to get from having the AI accelerators on there is a much higher bar to want to rewrite those apps. But if you can take the cost of inference down, that bar is much lower and all applications are going to be better and smarter and more capable with AI accelerators and inference. So, that is a really important part for customers to being able to use AI pervasively is driving the cost of inference down and our chips business is a big piece of that.

>>: Yeah. Yeah, no, it has uh I can tell you from our own firm that it is it can get very expensive. We had one employee spent $2 million in the last Yeah, doing good work, but you know, I was like, okay, this inference is not that cheap. Um.

>>: Yeah.

>>: So, you know, one of the things that you've done is you've you've made some investments, particularly in Anthropic and OpenAI, like very large investments, I think, of like 18 billion in Anthropic and like 50 billion in OpenAI or something like that. And so, kind of what got you to want to do that? You know, that and that you know, that and kind of similar investments from Nvidia and so forth kind of got Wall Street up in a tizzy because it reminded them of the bad old days of the dot-com boom and so forth. How did you think about that, particularly since they are also customers and you know, why did that make sense?

>>: Both of those companies are really impressive teams and really impressive companies and the rate at which they've grown their businesses, at least in the top line, I don't know if we've ever seen anything like it. It's really unbelievable and you know, so to us it's a combination of who these teams are, the technology and the models that they've built and some of the applications they've built, our belief in how transformative AI is, the fact that we believe these are going to be two of the most successful companies in the space over a long period of time and they're going to be lots of Not everyone's going to be a winner, but they're going to be a lot of very successful companies in this space, you know, but we believe these will be two of them.

And then, you know, the fact that we partner so closely, I mean, you know, I would say that if I start with Anthropic, where we've had the longer partnership, you know, we have so many customers that run Anthropic in Bedrock and our inference service and you know, I think that it is I think customers are most successful running Anthropic in Bedrock. We have had a deep partnership with them on chips, you know, where they have been building on top of Trainium for a while and I think it's been very good for Anthropic. I think it's been very good for our Trainium team. I think we both learned quite a bit from each other. So, we have you know, we have a deep partnership with them where you know, both our belief in the future of AI, the belief of those companies and the partnership it made sense for us to make investments as part of those partnerships.

You know, and then on the OpenAI side, we just had so many customers who wanted to use OpenAI in AWS and they weren't able to do so because of their prior business arrangements that they had. And you know, for a long time I think the companies had a lot of mutual respect and and a desire to work together. And so this is another one of those where over a series of conversations and months the ability to be able to run Open AI in Bedrock. You know, we built a unique product together in the stateful runtime environment, which is how I think all agents are going to be built moving forward. You're going to need state. Like Anthropic, they made a multi-year, multi-gigawatt commitment to Trainium. And we just believe that that is going to be one of the most successful companies in the space over a long period of time. All those made it make sense for us to have deeper partnerships and investments.

>>: Switching over to kind of how you use AI to build a company. So I think you said the Bedrock team rebuilt your inference engine with six engineers in 76 days. And that would have taken 40 people a year. So that that's pretty different. And you know, Amazon is kind of famous for the two-pizza teams. So like what is What is your current organizational design thinking given the technology because it does seem to really change things. And it's making you know, we got like super super specialized over the years with you know, designers and product managers and you know, front-end engineers and back-end engineers and this and that and the other. And then all of the sudden like everybody's a renaissance person. Um.

>>: Yeah.

>>: And so how how do you think about that in terms of org design?

>>: Yeah, I agree with what you're saying. It's I think that the Bedrock experience that you alluded to was very eye-opening for everybody here. And when we we spent a bunch of time sharing that story internally to inspire people and remind people what's possible because over time what ends up happen I mean I started the company 29 years ago. I started as an assistant product manager and I was the product manager for I don't know five products. Today when when companies get successful and they get bigger and the products get bigger you end up where you know a product manager works on one product and they just own one slice of it. Like they own pricing or they Everybody becomes more specialized and everybody accepts that that's the way you have to do business if you're building something big and successful and it's not. And it's definitely not when you look at what's happening with AI and you know I even just this past if I look at the last week we're you know we're working on some things you know around agents in our consumer business. We have a whole bunch of investments there but we had an idea that we really liked that we agreed on Thursday we were going to try and have like a real working prototype for that again we had four or five people who over the weekend had a had it operating in a 80% shippable way by Sunday. Like that is a really those are different worlds that you live in.

And so I think what it means for the future of organizations is I I think number one I think that we're going to have fewer humans doing the work that they're doing today in the future than we have today. You know I just if you look at the amount of coding that's being done agentically you look at the amount of customer service and business intelligence and research and you fast forward to just thinking how spreadsheets are going to be built like the number of lawyers you have like I just think you're going to have you're not going to have no people, but you're not going to have as many humans as we've all thrown at scale as businesses have gotten bigger in the future. There are going to be different jobs and new jobs, some of which we know about, some of which we don't know about, that will arise as AI and robotics and space develop like you know, there going to be all sorts of new jobs, but we're going to have fewer humans doing what they've done today.

I think the teams will be much smaller than they are right now. You know, like I think you're going to have smaller teams that have they have people who can work cross-functionally. They don't only one you they don't only own one sliver because the models are going to be able to get them 80% of the way there on a lot of these different functions. I think that every team will start at a much more advanced stage than we have in the past. And so I think you're going to be able to invent much more expansively and much more quickly. I do think there's a bunch of things that have to change. I think that you know, the startups starting today, most of them are starting agentically and agent native, you know, agent native AI native right from the start. All the

larger companies who've been in business for a while are going to have to figure out how to actually build at that same pace. And I think that I I I think that some of them will figure it out and um and you always like when you have a big business with a lot of capabilities and a loyal customer base. Some of them will move very slowly to figure it out and they will be more vulnerable in my opinion to startups, but you're going to I think you're going to have to I predict that companies will have to rewrite um almost all of their code to be agentically capable. Um they're going to have to allow agents to be updating different pieces of the You're going to have to build constraints there. I think if you just look at security, uh you know, security is I mean, it's kind of interesting if you look at all of the nervousness of the last month or two about models. They are focused on the more fantastical scenarios. Um and by the way, they're possible. Like, is it possible an evil botnet will take over, you know, everything? I mean, it's I guess it's possible and it's possible you can infuse robots with um you know, with malicious intent. They're all possible. I personally think we're going to figure that out together, you know, as an industry and private public segment.

>>: I think you got to start by paying attention to some of the things.

>>: Yeah. But I mean, the thing that's interesting is that I don't think that there is enough discussion about the more immediate term risks, which are really around cybersecurity.

>>: Yeah.

>>: These models are so that they find so many more vulnerabilities than you did before and that humans, you know, weren't finding. And and maybe you could argue if they could or couldn't, but they just weren't finding. And so, if you fast forward then, I think that the way that applications are going to have to be able to respond to imminent security threats is is going to be in minutes. You know, in today, if you think about it, you know, you find vulnerabilities, people classify the vulnerabilities, people develop patches. And then, even if you do that part quick, most companies take a long time to deploy because there are all these dependencies. You're going to have to deploy in minutes. And so, if you don't actually have your applications rewritten to be able to be updated by agents, you're going to have no chance in in that new world. And so, I just think, you know, all organizations are they're going to be reinvented and I think the companies that prioritize are going to be much better shape than those that don't.

>>: Yeah. And I it seems [clears throat] like, you know, we as an industry did not do a great job of architecting everything from the big systems to, you know, embedded devices with like good security architecture. And you know, the day of reckoning is here. And I mean, I think the good news is that's a big opportunity for new companies, new efforts, um people to do things. Uh but yeah, it it it's from what we're saying it is the threat. Like the theoretical, the thought experiment threats are, you know, right, maybe they'll happen, maybe they won't, but but this is clear and present.

>>: Yeah. Almost every uh partner and customer we talked to on the AWS site, every company um of any size is worried about the security piece. You know, and and you know, we've we've got a professional services practice where we're helping a lot of our customers and we've built a service called Continuum that identifies all of these and builds patches, but it's you know, it it's it's easier to say than to do, and it it actually because a lot of companies and a lot of engineers sometimes it isn't exciting sometimes to focus on the security piece. It's easy to wait until there are problems. And if you protect data that matters, it's a bad idea.

>>: Well, I'm also thinking in most companies, you know, going back to our earlier conversation, specialization has probably been uh you know, the worst case insecurity where you've got these very, very esoteric security engineers who don't like working with the other engineers, who you know, are scarce resource and this and that and the other, and you know, as a result like in most companies, in most software companies their their security practices just aren't that good for writing software. And I think, you know, that obviously has to change. And you know, the good news is we have tools that will help with that. But

>>: Yeah, and the good news part of it to your point is that you're going to be able, you know, you have to hire pen testers, there's only so many security people you're going to hire, there's only so many applications they can get to. The models coupled with automation are going to allow you to get, you know, to be able to better uh um pen test your entire surface and you're going to find things that you might not have otherwise found outside of a fluke.

>>: So, one of the things that you know, it is has been kind of interesting and us kind of looking at the market is you can do a lot of things with AI. One is you can like cut costs, you can reduce the number of accountants you have or lawyers or or that kind of thing. Um and there's a lot of there's actually venture capital firms that now are like, "Okay, we're going to go buy old companies and cut their costs." Um and they have that that's not really that interesting to us cuz we're like, "Okay, if that's your idea, you have very few ideas." Um and then there's like, "Okay, we're going to go build something the world's never seen before." and that's super exciting. But then there's a third thing which you've done um with Alexa where you've had a product, um a successful product and you said, "Okay, let's like rethink this whole thing from the beginning um because we have this new platform." You know, how how do you get to that and and where does that make sense?

>>: Yeah. Well, I I think that's actually an interesting delineation on the three um elements and I I think that organizations as we just talked about are going to really change and get more streamlined. And if you if you do it right, it will enable your really good builders and these small teams to build that second bucket you talked about which are things that people haven't seen before. You're going to be able to do it so and when you have experiences like I was talking about earlier where in 4 days you completely build a new idea that's, you know, 80% of the way there. It's so inspiring to people that they actually spend more of their free time and free thought cycles thinking about new ideas. That is to me is it's it's one of the most exciting parts of what's happening with AI, but I also think that for existing companies who have successful customer experiences at scale that if they think about AI as primarily just incrementally improving the experience, they will very likely not have a have the winning experiences three to five years from now. Because I think every single one of these experiences we know, even the ones that seem the most difficult to imagine changing are going to be meaningfully different with with AI in a few years. And so, it means that if you want to think more than incrementally, you have to find a way to get teams to reimagine from scratch what those applications look like if you were building it today with all the new technology available like AI. And in some cases it's to me in Alexa it was I felt like it was fairly obvious that we had to do that. It was you know, the brains of Alexa were really inventive at the time it was built, but the capabilities of what the brains could be were so different because of generative AI that you had to rewire the brains of Alexa if you wanted to be, you know, provide great customer experiences, be relevant over a long period of time. And so, that was fairly obvious. By the way, even though it was fairly obvious, it was difficult actually with, you know, the teams, you know, are are connected to what they've built and proud of what they've built. And you know, hard-earned scars on the tenets of what works and doesn't work. And to try and get get teams to completely start over and reimagine, it takes time because teams will say, "Well, that's not how we've done it.

We've tried that and that hasn't worked." Or, you know, so um but I think what's harder is to take experiences, you know, take our other consumer experience Take our retail business, or take our Prime Video business, or Fire TV business, or your our Prime Music. Those experiences, they're not as obvious um that the technology has a much better solution today.

But, I I actually believe every single one of those businesses we need to reinvent. And we have teams of people working on versions of those businesses they're totally reimagined. Now, do I think we'll get it right um uh right from the get-go? Probably not, you know, we'll find ways to iterate our way into it, but it's already yielding all sorts of uh new ideas and customer experiences that we think are very compelling. And then, what'll be interesting is the race between Do you Do you want a completely different experience that's like something like a new app, almost? Or, do those advance quick enough and you add them into the existing experience where you end up meaningfully changing the overall customer experience bit by bit over the next couple few years as you find elements that work. But, I I I do think every one of those experiences has to be different in the next few years if you if you if you want to be the successful leader.

>>: Yeah, that's such a tricky leadership problem, you know, it reminds me of um when I first stuck began my career as an engineer, my boss I got into some argument with somebody about something. And my boss said, "Look, one thing you have to understand, Ben, is everything is up for discussion until the first line of code is written, and then it's religious."

>>: [laughter]

>>: And I guess you experience that with uh Alexa, but you know, it is um it is really interesting that cuz all the knowledge, all the experience, all the things that went into building that original thing are now you're saying, "Okay, we're okay giving a lot of that up and and going to this new world." And by the way, with the customers, too, you know, you're going to have some big set of customers that loves the current experience.

>>: Right. Every single big transformation, at least my experience, maybe you have some examples that aren't, but it almost every big transformation requi- it's a huge change management. Like moving from on-premises to the cloud, same type of transition where lots of infrastructure leaders were really proud of what they built and they knew it, you know, inside and out and they were in the middle of the action and they didn't want to learn something new or, you know, it it it's going to be the same thing. It's the same experience I was just mentioning. It's going to be true for you know, trying to reinvent all your customer experiences. We're all proud of what we've built over the last, you know, 10 years and these experiences and we all have a hundred things that can make them better that we're all excited about and we spend all our waking hours working on. To think about how do I completely start over is, you know, it's intellectually and emotionally hard to do.

>>: Yeah. No no doubt. No doubt.

>>: A- a- and the we just incubated Andreessen Horowitz Academy a few weeks ago to help young people prepare for this transformation we're talking about. You You You've said how, you know, AI makes building easier, but young people still need to learn, you know, real expertise. I'm curious if if you were 18 in 2026, what advice might you have for yourself about how to prepare for for this transformation that we're entering?

>>: Well, I have a 25 and a 22-year-old. And so, I guess the 22-year-old was 19 when this was starting to really take off and I I learned very quickly that you can't tell them not to use AI in education. Like that that you know, that ship has left the station or that train has left the station and uh I think universities would be wise not to fight it. I just think it's it's a tool that people are going to use. And yet at the same time I think all young people and and you know, and I would say for us where we have a lot of people who you know, we have a writing culture who are using AI to write. You can tell when something's been written by AI. You know, it's it's uh it's not quite right. The pieces aren't put together quite right and you know, and so the advice I would give somebody young and I gave my kids was you can't run away from it. You know, and you can't wish it away. Like you it it's um uh it's going to be incredibly impactful in your life and every experience that you that you try and use in your work uh and and it will allow you to invent in ways that you that just weren't possible for us, you know, um before it. And so you really you need you need to learn about it. You need to educate yourself about it. You need to use it a lot. You need to try it. Try Try doing things that are outside of your comfort zone. Neither of my kids were developers, but they have, you know, used AI to to build applications.

Uh and use it as kind of the focal point of how you operate your day and how you how you you use um tools at work.

But I I I don't think it replaces critical reasoning and thinking. And I I think this is true for for universities. I you know, in many ways if I if I think about if I think about business school as an example you would get these three cases that you had had to analyze every day. And you had you had to do a lot of reading and you had to take notes and you had to figure out what the key issues were and then you got all these exhibits and you took notes on those and then you had to kind of put together what you thought the plan should be for that company.

And you know, often times there were a bunch of people just didn't do the work. And you know, they just didn't do the reading or the research or the note-taking. And that all that kind of goes away with AI. AI is going to be able to kind of get everybody to that starting that advanced starting spot much more quickly. But, I would also say that the actual putting the dots and the pieces together either to build a strategy or an approach or a plan or a new product or a new customer experience, I don't think AI is going to, you know, do that for most people most of the time. I It's not that it'll never happen. I still think you're going to need um human judgment and ingenuity and invention and experimentation and thinking about how all the pieces come together uh to end up um building amazing experiences and businesses. And And so, I I I think you want to use the tools and they're going to be huge aids, but I still think critical reasoning and thinking is going to be very important in everything we do.

Yeah. Yeah.

>>: I During During towards closing here, uh you know, you guys are in businesses as as diverse as satellite connectivity, robotics, health care. Uh I mean, the ambition is an incredible thing. This business started as a, you know, selling books on on the internet. Um and uh and and here it is now. How do you think about sort of, you know, either what threads these these uh sort of different uh sort of business lines together or how you think about what business lines are worth, you know, committing for years or how do you make sure you don't spread yourself too thin or how do you make sense of just the the vast range?

>>: Well, uh I'd say a few things. You know, first, uh I think we are living in um in an era of three of the biggest transformations in our lifetime and maybe ever between AI, robotics, and space development. And they're all huge and they all are going to completely change um a number of customer experiences that we all encounter and we are in the middle of all three of those big transformations. If you look at what we're doing in our retail business and you and how we use robotics and what we're doing on the AI side, you know, both in making it easier for internal and external builders to be able to build successful AI applications and agents as well as reinvent all our consumer experiences. If you think about Amazon Leo or our low earth orbit satellite constellation that we're building for the 500 million households around the world that don't have broadband

connectivity.

You know, there is there's an incredible set of transformations that are happening that we have a chance to help customers leverage in a way that makes their lives better and, you know, and we do it in a way that help customers but also lead to long-term sustainable businesses for ourselves as well. And we've, you know, I think we have a chance to be much more relevant in helping customers across many more areas. And that, you know, I'm not even mentioning what we're doing in grocery and what we're doing in, you know, autonomous driving, ride hailing, and health care. And, you know, this, you know, the way we organize—we've often had over the years this internal debate, but also a lot of external folks have asked the same type of question, which is what's being spread too thin.

I think some of that depends on a combination of how you organize yourself and what you're willing to invest. And so because we don't try and have the same group of people manage multiple businesses and we organize in this decentralized single thread away, if you have the right leaders and the right builders on those teams and you have an experience that if it's big enough can move the needle and you have a differentiated idea and the space is not being that well served, you have the opportunity to pursue a lot of these things simultaneously. And we have over a long period of time.

And then it just comes down to how much you're willing to invest. You know, there are some companies very understandably who say we're going to stick to our knitting. You know, we're going to stick to the core business that we have and we're going to grow that. And that by the way can be a very successful strategy. And we are very intent on our existing businesses continuing to grow them and build them. I think we've done so pretty successfully at a good clip. But there are so many other customer experiences that we know customers really want us to help solve that we can. They're going to help our customers and change our business long-term, and you know, we'll keep pursuing those.

>>: That's an inspiring note to end on. Sort of endless ambition. Andy, thanks so much for coming on the podcast.

>>: Yeah, thank you, Andy.

>>: Thank you for having me. I appreciate it, guys.