Amazon CEO Andy Jassy on the $220 Billion AI Buildout

Even spending 220 billion dollars 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 lap is 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 dollars 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 unpack that build out, why he believes AWS could eventually become a one trillion dollar 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 dollars 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? 2021 feels so long ago. It feels like 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 is 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 a tail end of the pandemic, in our retail business, we ended up with a real cost to serve challenge where we had built, we 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 in 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 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 U.S. We regionalized it so we got items much closer to customers, which turned out to both lower our cost to serve because it was 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 a fair bit and stronger operational performance and security. And I think a type of partner ecosystem is 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 $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 Zooks, or our 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. From a CEO perspective, how do you communicate what you're doing without lowering the cultural standard of – 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. The 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 tradeoffs. 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 will 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 tradeoff 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. 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 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 planned 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 like started AWS, it was kind of build it and they will come and that, okay, you're building all this infrastructure and then you're going to rent it. And so they needed to come or 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 buildout given the demand is so steep and you're growing so fast and then there is 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, 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, this was around the years, the early part of the 2000s. We had teams that were not moving nearly as quickly as we wanted them to, even though we felt like relatively we moved quickly. But for us, we just couldn't understand why it took so long. And it just turned out that our product teams were spending 70% to 80% of their time on the infrastructure pieces, which didn't differentiate them, and only about 20% of their time on what differentiated the product pieces. And so we realized every team was doing it and reinventing the wheel. And so we originally conceived of AWS to allow ourselves to move more quickly internally. And we figured if we built a set of high-scale, high-availability, low-cost, highly flexible building block services like compute and storage and database and content distribution, messaging, 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, as you know, there have been these different waves of people saying this is the time. AI is finally here. And 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 on the AWS side, but nothing like what we're seeing right now. In the early days of AWS, we felt like we were growing really fast and we felt good about it, and we were about a $60 million annual run rate business after three years. In the first three years of this 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 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 what I would tell you is 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 15 years ago when we couldn't believe the telco company spent $50 billion in CapEx. We were like, how are they spending that much on CapEx? So these numbers are 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. 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 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, 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. On average, they tend to break even in about three years, and they have useful... lives at this point of about five to six years. Most of the deals we're doing are about five-year deals. 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, 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. Because of the way the cash cycle works, it has headwinds in the early years when you're growing that fast. Actually, on that, 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. Particularly in the United States, it's funny and I'm sure you've experienced this, but nobody anywhere in the world is as anti-data center as the US politics, for whatever reason. How does that end up affecting this? I guess, is there any merit to the anti-data center argument? Then how does that inform your strategy of what you built here and what you build in the other regions? Yeah. Well, I just give you some of the context that I have, or at least I believe, which is, if you think about everything we do in the world today, stock market, banking, healthcare, entertainment, shopping, education, business, 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. They all use data centers, and then that's before AI. 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. The reality is the type of infrastructure build-out we need to support the rapid growth we were just talking about on the AI side, there hasn't been anything like it in the world and in this country since maybe when we built out the highways and bridges 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 see the US specifically cannot afford not to build data centers, or we will find that another country like China will establish all the leadership and all the norms there, and I don't think any of us are going to like that either domestically or globally. 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. 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 find that villain to point at, but 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, 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. That's a good example, or 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 percent of the year, like 10 hours a year for maintenance. You talk about the energy rates. 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. 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, mostly geographies, the rates will flatline or go down as they have in Indiana or in Georgia or Mississippi. There are things that we can do that are better. The other thing people forget about is in these geographies, the big data center providers are paying a lot of tax. If you look in St. Joseph County in Indiana, we've paid about $3 billion of tax. The communities get that money and they get to spend it on things that really matter for those communities. The communities get something out of it too. We've built programs like Built Together that we just committed to another billion dollars in the communities in which we have data center presences over the next five years. I think we have to keep working on it with the communities. It has to be a public and private partnership. But the build-out is so important to the future of the country. Imagine if we hadn't built out highways and bridges in what the economic prosperity and the capabilities of the country would be. We have to keep it in perspective and we have to be partners in the communities in which we built. Let's go back to the CapEx for a second. The $220 billion, that's $600 million a day. 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. Maybe you can situate or contextualize the state of AI demand at the moment. The first context piece is what I said earlier, which is just, I can't believe how big and fast AI has grown. It's a few hundred times faster than the beginning of AWS in the first three years, $25 billion run rate for us just in AI revenue in AWS in the first three years. It's really large and yet, it really is barbelled if you look at how demand is. 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. Then 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. 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. That I think is going to be the largest absolute segment of AI usage. I think you're going to see that. It's hard for me to predict the rate at which that'll happen. 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. There's a lot of growth in front of us. How do you think about it? Because there's this impending data center chip memory, everything shortage that's upon us in really 2027. Just because 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. How do you think about it when faced with the big labs being willing to pay maybe anything or almost anything for power, cooling, chips, the environment, and then you've got these all been around a long time, customers who are paying their regular rates to run their businesses, is there 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. The old guys, I can't double their prices. That wouldn't be fair. 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 as you say, probably going to inference, and then you've got these new guys who are insatiable. They'll buy in. 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. 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. These are real trade-offs you have to make, but I would say that where we start on this issue is being customer-focused. For us, we have deep relationships and partnerships with our customers in AWS. 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. 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. 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, to be able to do so in a way where it's sustainably economically for us as well. 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. I think that we're probably putting more capacity in than anybody else, because 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, 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 medium to long-term, and I think we still probably wouldn't satisfy all the demand. There is just incredible demand right now. 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. What drove that decision and then how has it paid off in this new world? Yeah. I think 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. We foresaw some pieces, but I don't think that we did not make the Annapurna purchase for AI chips per se. But 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'll be able to provide more value for customers and better economics for the business itself. What Annapurna started for us was just on the 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. We wanted to be able to use nearly 100 percent of the server for compute for customers. 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. We really liked the team and we really liked the product that we collaborated with them on together, and we realized 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 the 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 performant than other x86 processors. And so that has been hugely impactful for our customers and for our business. And 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 performant 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 of things. Some of it is 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, I can tell you from our own firm that it can get very expensive. We had one employee spend $2 million in the last, doing good work. But I was like, okay, this inference is not that cheap. So, 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 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 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 this space over a long period of time, and there are going to be lots of – not everyone is going to be a winner, but there are 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've 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 open AI side, we just had so many customers who wanted to use open AI 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 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 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 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, back-end engineers, and this and that and the other. And now all of a sudden, like everybody's 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, I 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 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. 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, you know, 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, you know, security is 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, you know, everything? I mean, I guess it's possible. And it's possible you can infuse robots with, 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, but like – 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 are so – like, they find so many more vulnerabilities than you did before, and that humans, you know, 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. You know, and 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 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 in much better shape 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 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 got a professional services practice where we're helping a lot of 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 easier to say – than to do. And 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, and also I think in most companies, going back to our earlier conversation, specialization has probably been 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 are scarce resource and this and that and the other. And as a result, in most companies, in most software companies, their security practices just aren't that good for writing software. And I think that obviously has to change. And 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 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 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 has been interesting in us looking at the market is you can do a lot of things with AI. One is you can cut costs. You can reduce the number of accountants you have or lawyers or that kind of thing. And 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 rethink this whole thing from the beginning because we have this new platform. 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 percent 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 are 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. 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 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. The teams are connected to what they've built and proud of what they've built. And hard-earned scars on the tenets of what works and doesn't work. And to try and 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. 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 that are totally reimagined. Now, do I think we'll get it 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 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 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 when I first began my career as an engineer, 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. And I guess you experienced that with Alexa. But, you know, it is really interesting because all the knowledge, all the experience, all the things that went into building that original thing are now you're saying, OK, we're OK giving a lot of that up and going to this new world. And by the way, with the customers, too, 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, but almost every big transformation, 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 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 10 years in these experiences. And we all have a hundred things that can make them better that we're all excited about. We spend all our waking hours working on to think about how do I completely start over? You know, it's intellectually and emotionally hard to do. Yeah, no, no doubt. No doubt. And we just incubated Harwitz and Dresden Academy a few weeks ago to help young people prepare for this transformation we're talking about. You've said how, you know, AI makes building easier, but young people still need to learn real expertise. I'm curious if you were 18 in 2026, what advice might you have for yourself about how to prepare 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 learned very quickly that you can't tell them not to use AI in education. Like that, you know, that ship has left the station or that train has left the station. And I think universities would be wise not to fight it. I just think it's a tool that people are going to use. And yet, at the same time, I think all young people and, you know, 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 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. It's going to be incredibly impactful in your life, in every experience that you try and use in your work. And it will allow you to invent in ways that just weren't possible for us, you know, before it. And so you really 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 doing things that are outside of your comfort zone. Neither of my kids were developers, but they have, you know, used AI to build applications and use it as kind of the focal point of how you operate your day and how you use tools at work. But I don't think it replaces critical reasoning and thinking. And I think this is true for universities. You know, in many ways, 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 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 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 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 human judgment and ingenuity and invention and experimentation and thinking about how all the pieces come together to end up building amazing experiences and businesses. And so 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. Gearing towards closing here, you guys are in businesses as diverse as satellite connectivity, robotics, healthcare. I mean, the ambition, it's incredible to think this business started as selling books on the internet. And here it is now. How do you think about sort of what threads these sort of different business lines together or how you think about what business lines are worth 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 vast range? Well, I'd say a few things. First, I think we are living 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 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 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, 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 help 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 health care. 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 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. There are some companies, very understandably, who say, we're going to stick to our knitting. 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. our customers and change our business long-term and we'll keep pursuing those. That's an inspiring note to end on. 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 for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. It should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com forward slash disclosures. Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify.