Full Transcript for Amazon CEO Andy Jassy on the $220 Billion AI Buildout

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, health care, 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 because they'll pay us whatever.

Amazon expects to spend roughly $220 billion in CapEx this year. And according to CEO Andy Jassy, even that isn't enough to meet AI demand. In this episode, Ben Horowitz and I sit down with Andy to and pat that build out. Why he believes AWS could eventually become a $1 trillion annual run rate business. And why inference costs still need to fall significantly.

We also discuss how AI is reshaping Amazon itself. From smaller teams building faster to rethinking products from the ground up. And what Andy sees ahead for cybersecurity, work, robotics, and space.

Andy, welcome to the podcast. It's great to have you. Thanks for having me. So you're five years into running Amazon and you've done a lot as CEO. Right now, $220 billion in CapEx spend, AWS, 28%. There's a lot that 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? Oh, 2021 feels so long ago.

It feels like, you know, a decade ago. But as you know, the world changes fast. And if you compare it to 2021, our overall top line is about 80% higher. AWS business, almost three times bigger. Our ads revenue is about two and a half times bigger. So the business has just grown quite a bit. And I think there are a few things that have really happened.

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 that we'd 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 and our fulfillment network were not what we wanted. And 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, a little less than a year into starting the job, people said, boy, what a terrible time to start and what bad luck.

And 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 there 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 U .S.

We regionalized it so we got items much closer to customers, which turned out to both lower our cost to serve because we're 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 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.

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 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, with the broadest functionality by fair bid and stronger operational performance and security, and 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 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 I think if you layer on top of that the seeds that we have of new businesses, whether it's how the team has progressed our prime video business, which is now 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

healthcare business, or what we're doing in robotics. 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 that mission of trying to make customers' lives better and easier every day really unifies everything we're doing across the company.

So a couple of follow -ups on that. So one, 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, 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 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 we have this set of leadership principles that a lot of people know about. We have 16 leadership principles. And a lot of these leadership principles, 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 from 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 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. 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 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 now you're balancing the combination or the tension of bias for action or speed and relentlessly high standards, which means you deliver at the cost structure and the delivery and the design that you'd always plan for.

And so, again, you have to make tradeoffs. We made the tradeoff 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 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 re -architected key parts of the network after we got that capacity installed. That 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.

Fascinating. You kind of, like at the beginning, when you started AWS, you started AWS,

it was kind of build it and they will come in that, okay, you're building all this infrastructure and then you're going to rent it. And so they needed to come where you'd be in a lot of trouble. But this is that at a much bigger scale in that 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 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 when we just kind of saw that skittishness in like the core we've debt market and equity markets and so forth. And so how do you know how much is too much on that build out, given the demand is so steep and you're growing so fast? And then there's a contention for that supply chain that you're kind of fighting for to get your cooling and power and chips and all that.

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, we didn't know if people would use it. 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 percent of their time on the infrastructure pieces, which didn't differentiate them and only about 20 percent 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 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, 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 gender of 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 going to run 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 gender of 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 leadership team breakfast, you know, 15 years ago when we couldn't believe the telco company spent $50 billion in CapEx. We're like, I'm really 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 and start to monetize them.

The good news is you only make that investment once. It's a useful life asset. It'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.

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, you know, 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. 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.

But here we are. 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 kind of U .S. politics for whatever reason. So how do you, you know, how does that end up affecting this? I guess, is there any merit to the anti -data center argument? And then, you know, how does that kind of inform your strategy of what you built here, you know, and what you built in the other regions? Yeah.

Well, I mean, I 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, you know, stock market, banking, health care, entertainment, shopping, education, business, you know, ride hailing, everything we do is digitally driven. They're all using data centers in a very similar way.

No, everybody is, I'm anti -me. I don't want to use my phone anymore.

And 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'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.

It's, 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'd say the U .S. 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, 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. 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, it 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 to push it over. But building it out, of course, what you're going to find is once you actually get that capacity, and 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, the 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 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 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 highways and bridges in 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 built. Let's go back to the CapEx for a second.

The $220 billion, you know, that's $600 million a day. And just to put it in perspective, I'm curious how you think about AI demand right now. Offline, we were talking about how you see it as a barbell and maybe you can sort of situate or contextualize 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 big and fast AI has grown.

You know, it's, you know, 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, you know, a few runaway apps, chat GPT, cloud 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, there is a lot of growth in front of us. And how do you think about it? Because there's kind of this impending data center chip, memory, everything shortage that's upon us in really 2027, just because, you know, everybody's building now, but it's not going to be ready necessarily by the end of the year and demand's not slowing down at all.

And so when faced with, how do you think about it when faced with like 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 all been around a long time customers who are paying their regular rates, you know, to run their businesses.

Is there like any business pressure to go, well, maybe we should buy out the old guys and sell them to the new guys because they'll pay us whatever. And the old guys, I can't double their prices. That wouldn't be fair. Or how do you think about that? Because it's such a weird thing in that you've got people you've had in there for 20 years doing a thing that's kind of, as you say, probably going to inference.

And then you've got these new guys who are insatiable. They'll buy in. Yeah, it's a good question.

We don't think of it quite the way you're talking about, but I can completely understand. 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 believe one of the reasons, coming back to the capital investment, why I think we can invest a lot more and it wouldn't be a bad use of capital, is that 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. There's so much demand.

If you think about the AI business and what we call the core business, which think of core as non -AI, they're both growing at really rapid rates right now. And if you have only a certain amount of memory, you have to decide how much of the memory you want to allocate to HBM, to the AI accelerators versus to DRAM, into your core. So 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 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 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. 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 in the AI side. People want to use us in the AI side to be able to do so and do so in a way where, you know, 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 because we really continue, we're very passionate about continuing to be the significant leader in the infrastructure and AI space like we've been. But as I said, it's, we're spending $220 billion or so this year and we could spend a lot more and it wouldn't be a bad use of capital and 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.

So, kind of speaking of that, you did something back in 2015 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 because there just wasn't much AI demand in 2015. So, kind of what drove that decision and then how has it kind of paid off in this new world? So, yeah, I think, you know, the Annapurna team is one of the most talented teams and set of people I've ever had a chance to work with.

And 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,

we foresaw some pieces, but I don't think that we, we did not make the Annapurna purchase for AI chips per se. But, you know, when you get to a certain scale, you start to have access and visibility to patterns of customers at very large scale that you realize if you can vertically integrate and you could build certain capabilities yourself, you'd be able to provide more value for customers and, you know, and better economics for the business itself.

So what Annapurna started as, for us, was just 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 nearly 100% of the server for compute for customers. And so 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 collaborated with them on together. And 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 at 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 partner who has virtually all the share, they're not often thinking about how to drive price performance. 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 how markets work.

It's part of how the innovator's dilemma comes up. And so we had tried to push on price performance and it was hard for us to get attention there. But we had customers who wanted to move more and more of their on -premises workloads to 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 they built Graviton, which 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 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 who's very good.

And 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 lots of different products they're building and priorities. And we knew our customers needed better price performance. And that's why we spun out a separate team in Annapurna to build an AI accelerator, which we call Tranium, which is also about 30% to 40% more price performance than the other comparable processors.

And I think when you talk about that middle of the barbell we were just talking about, if you ask what are the things that will change that middle of the barbell to have more of those current enterprise production apps using inference, there are a couple things. You know, some of it is, you know, some of the talent is 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 is 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, you know, it has. I can tell you from our own Confirm that it can get very expensive. We had one employee spend $2 million in the last– doing good work. I was like, okay, this inference is not that cheap.

So, you know, one of the things that you've done is you've made some investments, particularly in Anthropic and OpenAI, like very large investments, I think. Like $18 billion in Anthropic and $50 billion in OpenAI or something like that. And so kind of what got you to want to do that? And 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.

So 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.

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 this 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. But we believe these will be two of them. And then, you know, the fact that we partner so closely. I mean, we– 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 in 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 Tranium for a while. And I think it's been very good for Anthropic. I think it's been very good for our Tranium 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 that 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 and 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 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 OpenAI 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 Tranium. 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's pretty different. And, you know, Amazon is kind of famous for the two pizza teams. So, like, 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 now, all of a sudden, like, everybody's a Renaissance person. Yeah. And so how do you think about that in terms of org design? Yeah, I agree with what you're saying.

I think that the Bedrock experience that you alluded to was very eye -opening for everybody here. And 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 happening– 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 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. 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, even just this past– if I look at the last week, 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 it operating in an 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 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, 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 development.

Like, you know, there are 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, I think you're going to have smaller teams that have– they have people who can work cross -functionally. 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 or 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 some of them will figure it out. 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 I predict that companies will have to rewrite almost all of their code to be agentically capable. They're going to have to allow agents to be updating different pieces of code. You're going to have to build constraints there. I think if you just look at security, 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.

And by the way, they're possible. Like, is it possible an evil botnet will take over everything? I mean, I guess it's possible. And it's possible you can infuse robots with malicious intent. They're all possible. I personally think we're going to figure that out together as an industry and private public segment. I think you've 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. These models, they find so many more vulnerabilities than you did before and that humans weren't finding. And maybe you could argue if they could or couldn't, but they just weren't finding.

And so if you fast forward them, I think that the way that applications are going to have to be able to respond to imminent security threats is going to be in minutes. And today, if you think about it, 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 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 shaped than those that don't.

Yeah. And it seems 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, people to do things.

But yeah, 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 partner and customer we talk to on the AWS site, every company of any size is worried about the security piece. You know, and, you know, we've we've got a professional services practice where we're helping our customers and we've built a service called Continuum that identifies a lot of these vulnerabilities and builds patches.

But it's, you know, it's it's it's easier to say than to do. And it actually because a lot of companies and a lot of engineers sometimes 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, also, I think in most companies, you know, going back to our earlier conversation, specialization is probably been, you know, the worst case in security 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 and most software companies, 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. 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 pen test your entire service. 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, 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 that kind of thing. And there's a lot of there's actually venture capital firms that now are like, OK, we're going to go buy old companies and cut their costs. And that's never been that interesting to us because we're like, OK, if that's your idea, you have very few ideas.

And then there is like, OK, 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 with Alexa where you've had a product, a successful product. And you said, OK, let's like rethink this whole thing from the beginning because we have this new platform. Or, you know, how do you get to that and where does that make sense? Yeah, well, I think that's actually an interesting delineation on the three elements.

And I think that organizations, as we just talked about, are going to really change and get more streamlined. And 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. And when you have experiences like I was talking about earlier, where in four days you completely build a new idea that's 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, 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 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 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, to me, in Alexa, 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 experience to 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 connected to what they've built and proud of what they've built. and um 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 our fire tv business or our prime

music those experiences they're not as obvious that the technology has a much better solution today but 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 new ideas and customer experiences that we think are very compelling and then what will 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 do think every one of these experiences has to be different in the next few years if you want to be a successful leader yeah that's such a tricky leadership problem you know it reminds me of um when i first began my career as an engineer my boss i got into some argument with somebody about something 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 and i

guess you experience that with uh alexa but you know it is um it is really interesting that because 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 uh 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 yeah every single big transformation at least my experience maybe you have some examples are but almost every big transformation require it's a huge change the technology and the product and the customer experience and the business elements have to be

compelling enough to want to change but then when that's true it doesn't just happen 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 now and they were in the middle of the action and they didn't want to learn something new or you know 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 as you know it's intellectually and emotionally hard to do yeah no no doubt no doubt and we uh just incubated harwitz and jason academy a few weeks ago to help young people prepare for this transformation we're talking about 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 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 uh 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's um uh it's going to be incredibly impactful in your life in 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 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 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 of work but i i don't think it replaces critical reasoning and thinking and i i think this is true for um 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 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 oftentimes there

were a bunch of people just didn't do the work 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 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 you're going 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 it's incredible to think this business started as uh you know selling books on on the internet um and uh and here it is now how do you think about sort of you know either what threads these these uh sort of different 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 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 how we use

robotics and what we're doing on the AI side, 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, our low -Earth orbit satellite, constellation that we're building for the 500 million households around the world that don't have broadband connectivity,

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 we do it in a way that helps customers but also lead to long -term sustainable businesses for ourselves as well. And I think we have a chance to be much more relevant in helping customers across many more areas.

And I'm not even mentioning what we're doing in grocery and what we're doing in autonomous driving, ride -hailing, and healthcare. And, 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 -threaded way, if you have the right leaders and the right builders on those teams and you have an experience that if it's big enough could move the needle and you have a differentiated idea and a 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, 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. It's 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.

Thanks again for listening. And I'll see you in the next episode.

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