Building the Cloud for an Agentic World | AWS CEO Matt Garman

a16z’s Raghu Raghuram sits down with AWS CEO Matt Garman to discuss how AI is reshaping the cloud, from the needs of AI-native startups to infrastructure increasingly designed for agents. Matt explains how AWS is adapting as agents write code and manage infrastructure, why it’s reserving scarce GPU capacity for startups, and where custom chips like Trainium and Graviton fit into the AI stack. They also discuss Amazon's $220 billion capital investment, the shifting bottlenecks in the infrastructure buildout, what enterprises need to trust autonomous agents, and how AWS's own teams are building with agents.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Agentic workflows tend to perform better on AWS than anywhere else. Compute sandboxes, gateways, agent permissions versus people. A lot of those are things that we have built and are building and thinking actively about.

• 0:12Speaker 01

  The top frontier labs gobble up every available GPUs. At the same time, you want to promote newer companies that are going to become the enterprises of tomorrow. How are you thinking about balancing?

• 0:21Speaker 00

  From the very beginning of when we launched AWS, startups have been the lifeblood of the core of what we do. These are the innovators that are at the edge of technology, understanding what's possible. We're very intentional about keeping capacity available for the startups. We recently announced we're going to be buying 2 million Nvidia GPUs over the next couple of years.

• 0:38Speaker 01

  Your capex this year is what 200 or something?

• 0:41Speaker 00

  $220 billion for 26. And we don't anticipate slowing down anytime soon because the demand is just massive.

• 0:48Speaker 01

  With all the debate around AI extension risk and the hugging face attack, what are CEOs asking you about all these things?

• 0:56Erik Torenberg?

  Amazon plans to spend $220 billion in capital this year. And AWS CEO Matt Garman says they don't anticipate slowing down anytime soon. In this episode, E16Z's Raghu Ragaram sits down with Matt to unpack what's driving one of the largest infrastructure build outs in history and how AI is changing the cloud itself. They discuss how AWS decides who gets scarce GPU capacity, the shifting bottlenecks from power and chips to memory and construction, and the company's bet on custom silicon like Tranium. They also get into what changes when agents increasingly write code and manage infrastructure. And that shares what AWS is seeing inside its own teams, where agents now write code

• 1:38Erik Torenberg?

  and engineers increasingly manage teams of agents, accelerating how quickly new products can be built.

• 1:47Speaker 01

  Welcome to the pod, Matt. What a time we are living in, so I have a lot of topics to talk to you about.

• 1:52Speaker 00

  Awesome. Thanks for having me. I'm excited.

• 1:54Speaker 01

  Yeah, absolutely. So let's start actually right from the beginning. So you were the first GM for EC2 and that was 2006, right? And today you guys are what 160, 170 billion in revenue?

• 2:11Speaker 00

  About 169, 170 billion, yeah.

• 2:13Speaker 01

  Yeah, growing 35.

• 2:16Speaker 00

  37%.

• 2:17Speaker 01

  That's 37% at 169.

• 2:20Speaker 00

  Yeah.

• 2:20Speaker 01

  Crazy.

• 2:20Speaker 00

  There's a ton of, it's interesting to think about it from day one, when the first dollar revenue. But yeah, the interesting thing is we're still at the early stages of what the business can be and what the opportunity is for customers. Most workloads, there's a huge amount of workloads still that live on prem today. And the amount of compute that people are doing every single day is more than it was the day before. And so you see the tailwind from AI, you see the tailwind from migration into the cloud, and the business has grown really fast and it's been a super fun thing to be a part of.

• 2:49Speaker 01

  Yeah, it is, it is. I mean, they'll be writing history books about this and business books about this for a long time to come. I want to touch on the on-prem, that sort of boggles my mind. I obviously did my best to keep them there for a long time.

• 3:02Speaker 00

  You built a lot of stuff on-prem back in the day.

• 3:05Speaker 01

  Yeah, yeah.

• 3:05Speaker 00

  Trying to now get all of that to move into AWS.

• 3:07Speaker 01

  Yeah, so we could talk about that later. But so if you think about EC2 in the early days and you got your start with obviously selling to startups. And today, the AWS cloud service, I mean, as you reflect on the evolution, what stands out?

• 3:22Speaker 00

  Yeah.

• 3:22Speaker 01

  Part one, and then part two, we'll talk about how the nature of how you serve startups has changed.

• 3:28Speaker 00

  Sure. Well, like you said, actually, it's funny. So I actually interned for AWS in 2005 when we were first, kind of was an internal project. It was my business school internship. And my project was actually to come up with an analysis of who we thought AWS would be most interesting to. And the answer was startups, like probably not, surprisingly. And so from the very beginning of when we launched AWS, startups have been the lifeblood of the core of what we do for a number of reasons. One is the value proposition is just so attractive, what AWS provides to startups. And we spend a lot of time and effort making sure that we're great partners to the startups, helping them not just provide infrastructure, but also advice on how to get your company up and running and how to think about your architecture so it'll scale eventually, and a whole bunch of things that we do for the startups.

• 4:09Speaker 00

  But we also think that for us, it's just good business because the startups today are the enterprises of tomorrow. And it's an imprecise number, but today we estimate that maybe 30, 40% of AWS revenue comes from companies that was a one-time startup in AWS's lifetime. So it's fun to see for us to see the companies grow over time and to get bigger and become enterprises effectively. And so that's why we invest so much to startups and why we pay so much attention to the the brand new two people in a garage type startups. And, and not just because of business outcomes, but also because that's who we learn from. These are the innovators that are at the edge of technology, understanding what's possible, pushing our services to say what they'd want more of, what can help them go faster,

• 4:52Speaker 00

  what can help them achieve their outcomes. And a lot of times they're pushing more than the banks or the healthcare companies or the governments, the startups, the ones that are pushing that envelope. And it really helps us to be better and make sure that we're ahead of that wave where the banks are going to want some capability five years down the road that startups want today.

• 5:09Speaker 01

  Yeah, yeah. And compared from then to now, how have the startups changed and what they want from you? Obviously, they all want a lot of GPUs and we can talk about that, but besides that.

• 5:21Speaker 00

  Well, I will say there's a couple of things that have changed. One is startups started at a much smaller size than when we first started, right? They might've gotten $10 million of funding and they had an app idea that they were kind of slowly iterating on. Now it's from day one, they're valued at $1 billion, they have $200 billion of funding. It's a team and an idea and all of a sudden they're worth $1 billion.

• 5:42Speaker 01

  It's go from our offices to your offices.

• 5:44Speaker 00

  That's right, that's right. And like the size and scale and ambition of the ideas, I think, requires more capital. They're bigger to start with. They're also more expensive too, right? To go after kind of a training, a model, or doing something that a lot of the folks are doing today. So that's number one, which is just the size that they start from is really big. But I would say number two is some things haven't changed, right? They actually are still thinking about, okay, once I scale, how do I think about an architecture? How do I think about security? How do I think about performance? How do I think about having all the capabilities that I need? How do I think about setting up my I am setup so that when I have more than three employees, this thing is going to work and scale? Which is a lot of times why they like AWS as opposed to just going to a NeoCloud or something like that.

• 6:27Speaker 00

  They actually need all of those other security and, and capabilities that come around from, from training. And so I think that's something that hasn't changed and is exactly the same. I think the scale at which the ramp up is definitely different today. I also think increasingly we're seeing teams that want a cloud that is great to work with agents and not just with people. And I think that's where we've spent a lot of time. How do you think about, exactly, how do you think about broad scale? How do you think about performance? How do you think about an interface that is a well-defined API interface that agents can actually easily traverse and work across? And so that's also something that we both, I think we're naturally set up to do well, but that we've actually doubled down on to ensure things like how can you start a database in three seconds?

• 7:11Speaker 00

  And really get to some capabilities that agents are excited about.

• 7:14Speaker 01

  Have you introduced any specific new services that are explicitly targeted at agents? Are people building agents?

• 7:22Speaker 00

  I'd say what we've done is we've optimized some existing services so that they can both work for people and agents. And so the answer is we definitely have some services designed for building agents. So we have things like Agent Core and Bedrock and pieces like that. But when we look at, just taking an underlying component like S3, right? Which is where the vast majority of companies store their data and have their data lakes. It turns out like a lot of the use cases for people and agents are similar. But you want to have, and one of the things that we have in preview right now or in, in beta is called AWS Context that allows you to build a context layer so that agents can actually more easily find all of the data they want across your various data lakes. And so whether you have your data stored in Aurora or you have it in S3

• 8:04Speaker 00

  or you have it in somewhere else in AWS, can kind of build this context layer where when people aren't necessarily going to access data in that way, but agents actually are happy to go across lots of those different things. And so there are some services that we're building like that, but for the most part, you also care about what does latency look like? What does throughput look like? How do you make sure that the underlying engine is actually fast, scalable? They actually care a lot about tail latencies, which is interesting. Where people are, they don't always care about the P999 S3 latency, like agents do care and get blocked by that. And so that's the thing that we've cared about for a long time. And from a performance perspective, it's one of the things that really pop. That's why agentic workflows tend to perform better on AWS than anywhere else.

• 8:43Speaker 01

  Yeah, obviously we see a lot of companies starting out and the very common refrain, of course, is that agents are writing all the code for them, right? And agents are selecting the databases and the email servers and everything that you can name it, right? So has there been a lot of thinking that, and obviously they read the documentation, has there been a lot of thinking on the AWS, like it feels funny to say it, quote unquote, legacy services that have been around for a decade on how do you re-architect them?

• 9:15Speaker 00

  I think, so yes, I think there are some things that we've thought about. Actually, the core underlying building blocks, I think, are in really good shape. I think there's a usability layer that we're thinking about of how we make easier to use for, we'll call it the very simplest, the use case you call out where somebody is just coding up an app really quickly and they ask it to deploy. We find the vast majority of our customers will go and tell their coding agent, whether it's Kiro, whether it's Claude, whether it's Codex. They'll say, I want to build on AWS. Here's my credentials, here's my stuff, this is how I want to deploy it. And then agents are great and they go do it. There are use cases though where you're just, you're already not on a cloud. You're just trying something, you say deploy. And frankly, a lot of times that'll go to some of our partners that have an easier to use layer on top. True love, by the way, too, and we love our partners on those fronts too.

• 9:57Speaker 00

  But I do think that there are some things that we're doing where if you're brand new, you don't already have an AWS, you already haven't set up your I am. A couple of months ago, it was much harder to start up an account, right? It's been like this for 20 years, but you have to define your VPCs, you have to define your I am roles, all these kinds of things, which are actually super important things once you get to be large. And what we hear from customers is like, it was a hard trade-off because they know that they're going to want those in months or years down the line, but for now, they just kind of want to use the services, not worry about that. And so what we've actually launched is if you go create a new AWS account today. And we're slowly rolling this out. I don't know if it's actually full all the way out yet now. But you don't have to do those things. You don't have to give it a credit card. You can sign up with your Gmail account.

• 10:40Speaker 00

  You got to just, all of those things are handled in default behind the scenes and within less than 30 seconds, you're up and running and can be operating an AWS, a full AWS account, which is, you know, for those type of systems, much more what the agents want to be able to do because they don't want to have to go through all of that, setting up your VPCs and your,

• 10:57Speaker 00

  and pieces like that. So there are some things where we're adding kind of some ease of use to that, which we're quite excited about. And I've seen some really positive feedback from customers on it. And we'll keep doing more things like that over time. The great part about that, though, is it's not like a simplistic account and then you have to migrate. If you basically say, okay, now I actually do want to scale into the, or I want to develop an organization. I want to go and kind of fine tune some of the things. You can easily come just, you're, you're already in a real AWS account and you can actually then go and do all of those things later when you need them. And so there's no migration or move later. And so that's part of the hard work that we really think about, which is how do we make it not a choice for the customer, but an easy on-ramp into the depth of features that we know customers and startups are going to want when they start scaling.

• 11:40Speaker 01

  Yeah, yeah, got it. And

• 11:45Speaker 01

  what has been some of the hardest things to accommodate as agents have taken over? I mean, you made a name, this, the way you guys became the phenomenon that you became was serving developers, right? And then infra teams. Now developers are substituted by agents and pretty soon infra teams are being substituted by agents.

• 12:10Speaker 01

  Are there, what has been some of the hardest things for you as the largest service provider in the world to handle that transition?

• 12:19Speaker 00

  Well, you know, look, I think, like I said, much of our infrastructure was, is

• 12:24Speaker 01

  Yeah.

• 12:24Speaker 00

  It's actually pretty well set up to handle the scale, which is good. I think there's been a couple of things which are interesting, which are, I think, interesting paradigm shifts where you could argue that some of our systems are,

• 12:39Speaker 00

  I don't want to say over engineered, but as an example, many agents want to create a database, do a little bit of work and then have the database go away. Really need that database to have five nines of durability.

• 12:51Speaker 01

  Exactly.

• 12:52Speaker 00

  Right. And so there's some things that we rethink there where when you're creating an Aurora database, like does your production database, like you do want five nines of data, you know, you want, you need durability, you want availability, you want all of those things. And for the agent use case, that's, that's arguably, or maybe not arguably, like over engineered for, for what we need. And so, you know, it's hard, we don't, and we kind of have this, this

• 13:17Speaker 00

  like a belief that we don't really want to have like a non durable option that's, that's going to cause problems either. Cause you never really quite know if the database that's created is, wants to stay around for a long time or a short amount of time. And so we're, we're trying to do the hard work to think about how do you accomplish both of those things where you can create it quickly and throw it away. And you don't really waste a lot of resources, but, but, but if you do want it and it can be durable and stay for a long time and actually grow into a big production database. So those are some trade offs that we think about actively as we think about how does the, the more traditional kind of, this is going to be my production system kind of capabilities match with, with, with some of the more transient nature of the infrastructure that agents want to use.

• 13:59Speaker 00

  So that's one, I think, but there's, there's, there's a bunch. I think the scale and speed and latency of creation of durable resources is another one that's interesting. You know, I think the other one that we actively think about too, is just, are there new building blocks that agents are going to want that we just didn't really need before?

• 14:16Speaker 01

  Exactly.

• 14:16Speaker 00

  And so compute sandboxes, gateways, agent permissions versus people or, or service role permissions. A lot of those are things that we have built and are building and thinking actively about because they are just brand new building blocks. It's not using the existing building blocks differently, but it's brand new ones where pretty, pretty clearly you want a different permissions for agents. You don't just want to give it RAGU's permissions.

• 14:40Speaker 01

  Exactly.

• 14:41Speaker 00

  And let it go do whatever you can do. You actually want, you know, very time box, short term permissions to just go do a task. You actually may not want to give permissions to a whole tool at all. You may want to get very fine grained commissions, what an agent's able to do from a sandbox, right? You actually want a sandbox that, you want to

• 14:59Speaker 01

  Virtualization is having its act too.

• 15:01Speaker 00

  That's right. That's, that's right. Exactly. And they're, they're different. It's similar. It's the same idea, but it's, you know, how do you have that lightweight and, and think about? Unfortunately, we've done a lot of work with Firecracker, our, our kind of micro VMs.

• 15:14Speaker 01

  In fact, a lot of the sandbox companies, startups use Firecracker.

• 15:17Speaker 00

  They all use Firecracker, which, which we invented 10 years ago, maybe or something like that. And it's, it's, it's really purpose built for, I mean, it's not, wasn't purpose built for agents, but it's actually quite good for agents because you can spin them up really rapidly. They have a great security boundary and you don't have a lot of that virtualization overhead that you have from a traditional large VM. So, you know, I think those are some, some new building blocks that we're thinking about and, and more emerge every single day, you know, and

• 15:43Speaker 01

  So I've rattled enough into agents. I'll come back to it later, but it's a fascinating topic. But let me switch gears a little bit and ask about something that every one of our startups face and we get asked most frequently, which is how can we get GPUs, right?

• 16:02Speaker 00

  Yeah.

• 16:02Speaker 01

  I mean, obviously you guys are running massive GPU farms and increasing it every day. But the top frontier labs gobble up every available GPUs, more power to them. How do you deal with the internal, at the same time, you want to promote newer companies that are going to become the enterprises of tomorrow, like you said? How are you thinking about balancing?

• 16:25Speaker 00

  Yeah, it's, it's a great question.

• 16:27Speaker 01

  Needs and these small companies that don't have a lot of credit and whatnot. So.

• 16:31Speaker 00

  Well, yeah, it's a great question. And, and a couple of these things make it more challenging too. One is

• 16:38Speaker 00

  the, the capex expense needed to go deploy kind of all of the compute that everyone needs right now is massive, right? And so we are, I think.

• 16:46Speaker 01

  Your capex is the same as what, 200 or something?

• 16:48Speaker 00

  $220 billion for 26. It's, I, I, at one point I saw that it's, you know, it's, it's a pretty, I mean, that is a larger expense than we've ever had, maybe any company has ever had in a, in a single year. And, and we don't anticipate slowing down anytime soon because the demand is just massive. And so at some point you're limited by how fast can you build data centers? How fast can you deploy capital? How, how fast can you get memory and chips and all of those kinds of things? And so all of those are different points, constraints that were, you know, whether it's power, data centers, capital, memory, chips, that are all kind of constraints at various times, construction, people that build buildings, like, you know, construction people are, are at a premium today. And so all of those, we, you know, we, we work really hard to make sure it happen.

• 17:33Speaker 00

  And then with the capacity that we are able to deploy, which is still a huge amount and, and not enough, we know. We really think intentionally about what is that allocation strategy? And so we're great partners with the, the large frontier labs, the anthropics and open AIs and Meta and other large customers. And so those folks are really good customers of ours.

• 17:52Speaker 01

  Yep.

• 17:53Speaker 00

  And, and we want to make sure that we invest in them. We have large enterprise customers, whether it's Salesforce's or JPMC's or, you know, other large companies that, that have demand for fewer numbers of GPUs or accelerators. Sometimes they want Tranium, sometimes they want Nvidia GPUs. But we want to make sure that we can support them as well. And we're very intentional about how we make sure that we have capacity for startups. And, and so what we do is we actually do allocate and we basically say, okay, we're going to keep, we could, you're right, we could sell every single

• 18:24Speaker 00

  GPU or AI accelerator we had to probably just the, the big frontier labs and call it a day. We choose not to do that because we actually want to keep growing that the full ecosystem. They get a large number, but, but we want to keep supporting a broader set of customers because we actually think that both, the whole ecosystem will be more healthy for us. There's some diversification, but it's also just, we know these are going to be big companies over time. And so we try to support them. I saw recently that we say, you know, yes, in some way, shape or form, but something like 60% of the, the requests we eventually get. Sometimes it's a little bit later. Sometimes it's in a different region. Sometimes it's a slightly different configuration than the customers are looking for. But we really try to lean in and, and try to allocate as much as we can. And every single startup that you have wants more.

• 19:08Speaker 01

  Yeah, exactly.

• 19:10Speaker 00

  And so, you know, so we're working hard to try to make sure that we have all of those. But, you know, it's, it's hard. Like that's, that's, it's, you know, like this is, as a lot of people say, it's a good problem to have.

• 19:20Speaker 01

  Yes, it is.

• 19:22Speaker 00

  But it's, it's a problem nonetheless. And, and we're, you know, we continue to look at, at, you know, we, we recently announced we're going to be buying, you know, 2 million Nvidia GPUs over the next couple of years. Like we're, we're landing a massive amount of capacity.

• 19:38Speaker 01

  And 2 million.

• 19:38Speaker 00

  2 million. So, you know, it's a lot and it's over the next couple of years, but, and, and who knows if that's enough. What kind of, I don't know, but that's, at some point, again, we're limited by other, other components as well. But it is, we're, we're very intentional about keeping capacity available for, for the startups. And I know it's, it's painful to not have enough, but we, we keep pushing.

• 19:58Speaker 01

  When did your capex cross, I mean, pure AWS, I mean, Amazon was a bigger company.

• 20:05Speaker 00

  Yeah.

• 20:06Speaker 01

  Cross like, let's call it even 1 billion or 10 billion a year.

• 20:10Speaker 00

  Oh, I don't know. I'd have to go back and look. I'm not sure about that, but it's definitely scaled up over the last couple of years in a pretty meaningful way. The, the, yeah, the AI build out has, has definitely ramped our, our capex spending. And so I'd say the last, you know, call it three or four years, our, our capex has definitely accelerated pretty, pretty meaningfully. I don't know when we crossed a billion, but given we're 220 now, it's probably a while. I mean, we've, we've been spending capex for a while and the, and the company has been really good about funding that. And obviously the AWS business is a, is a good one that we like to invest in.

• 20:40Speaker 01

  Yeah, yeah, of course.

• 20:41Speaker 00

  And, and, you know, I think we were, for the longest time, actually, we, you know, we, we were investing ahead of where the demand was and and I think one of the, one of the most painful things is that with the real ramp of, of GPUs, like a lot of the elasticity has unfortunately, you know, kind of gone away. And so hopefully we'll get back to, and in our core compute and, and storage and things, that elasticity is still there and that, but, but, you know, the, the, but, but, so we've been spending for, for quite a bit of time and, and we feel really good about the spend that we're making now. Which is, you know, I think I get lots of questions sometimes about how you feel about that spend and like, are you nervous about a bubble and other things like that?

• 21:21Speaker 00

  And I will say, you know, we, because of the position we have, like one, we do take this diversified approach. And so not all of our capacity is bundled up in one customer. And I think you go to a, some of these, whether they're Neo Clouds or some of the, the other providers out there and, you know, you'll see sometimes concentrations of 30, 40, 50, 60% with one or two customers. We're nowhere near that, obviously. We're, you know, single-digit percentages at the highest and usually it's less than that. For one, I think we have a lot less risk on one particular customer, but also because we have that rich set of services, like AWS is where people really are coming to launch their production workloads. And so the majority of our usage today actually is either core compute and storage and inference, which is

• 22:04Speaker 00

  part of that application. And so those are the workloads that I think just aren't going to go away. Just because we see enterprises getting positive ROI, you go talk to the customers and you say, at the capability today and the cost today, are you seeing positive returns to your business? And almost to a person, they'll say like, oh yeah.

• 22:20Speaker 01

  Yeah.

• 22:21Speaker 00

  And so you're like, well, that's not going to go away. There's no bubble in which they stop spending on that, you know? And so, you know, and you know this, like the VC model, like is every billion dollar startup going to make it? No, they won't. But you know, that's, that's kind of the game. And that's been, that's been true for 50 years.

• 22:35Speaker 01

  Yeah.

• 22:36Speaker 00

  They haven't always been billion dollar startups.

• 22:38Speaker 01

  The numbers have changed, but the principles haven't changed.

• 22:40Speaker 00

  The principles are the same, right? You bet on 10 and one makes it and pays for the other 10 or whatever, whatever the percentage is, hopefully it's higher.

• 22:45Speaker 01

  Famous power law.

• 22:46Speaker 00

  Yeah. Right? And so that, that there, you know, you saw that with the internet, where there was a bubble and a bunch of, of internet companies didn't make it and the internet's still a thing. And a lot of the companies that had durable businesses, the Google, Amazons, the others like that, they did pretty well. And so for us, we think we'll feel really good about that investment and, and the continued investment going forward.

• 23:07Speaker 01

  You guys have a view of the demand that's unparalleled, right? Because you're seeing across the globe, you're seeing across every segment, enterprise and the big labs and the native companies and so on and so forth. So yeah, if anybody should call it, you should be able to call it first.

• 23:25Speaker 00

  I'd hope so.

• 23:25Speaker 01

  Yeah.

• 23:26Speaker 00

  That's our, that's our plan. And honestly, we spend a lot of time thinking about it. We're very intentional about how we spend, you know, our shareholder capital. And, and we think that we're making great investments. We have a lot of good protections of how we intentionally invest that money. But, but yeah, we're, we're very bullish on, and you know, I think Andy's been public about saying this, we, the potential for AWS is really, really large. And over the next decade, the potential is there. And anytime you have an opportunity that's that big, like you want to make, you want to, you want to invest to go after it.

• 23:55Speaker 01

  As the scale of these numbers go larger and larger, right?

• 23:59Speaker 00

  Yeah.

• 24:00Speaker 01

  Has your planning and process dramatically changed in terms of, I mean, you're not writing

• 24:07Speaker 00

  Yeah.

• 24:08Speaker 01

  billion dollar checks, you're writing like 50 billion dollar checks or

• 24:11Speaker 00

  Yeah.

• 24:11Speaker 01

  20 billion dollar checks or whatever it is.

• 24:13Speaker 00

  Yeah. Um, yeah, yes and no. I mean, I think, look, a lot of times we're the, we're still very bullish about the investments and lean forward, but there, there, the process, so there's a lot of things that have completely changed.

• 24:24Speaker 01

  Like, how do you even

• 24:25Speaker 00

  If I think about

• 24:26Speaker 01

  estimate 2028 demand or 29 demand?

• 24:27Speaker 00

  And there's things that we have to think about now that we just never had to think about. And so if you go back 15 years, if we needed more power, we asked the power company to give us another, you know, couple of megawatts or whatever. That's right, tens of megawatts, like, and they would just give it to us because 10 megawatts wasn't that much. And, and that was plenty for us to keep growing. Now we have to bring our own, we bring our own power. And so we, we pay for power projects, we pay for renewable projects. We're one of the biggest renewable power purchasers each year for the last 10 years. And so it's, you know, we're regularly bringing on new solar projects, new nuclear projects, new

• 25:05Speaker 01

  So are you talking about behind the meter or are you talking about working with the operator?

• 25:08Speaker 00

  We'll do both. And so both of those things. And so oftentimes with these power projects that we bring on, we'll pay the, the capital and pay for the project and then it'll go into the grid and then we'll get credit for that. So we'll bring those on. Sometimes we'll do behind the meter too. Like what's, it's a mix. At the, at the scale that we're doing, you have to think about all of those things. But, but you know, that's, that's planning where you're thinking, you know, 20 years out of how you're going to think about power, how you're going to think about transmission, how you're going to think about that capital and, and that stuff we'd never had to think about before. That's planning we just never had to do. But you know, we always had to, it's, you know, the, the other things is when we used to think about server demand that we needed, we, you know, we'd have, you know, multiple quarter demand things and we'd talk with our, our suppliers and

• 25:53Speaker 00

  things. Now we, we work multiple years out, just because the, the size is so much that we have to think about kind of what do we need for 26, we need for 27, what do we need for 28? And so that's, but you know, but it's also one of the value that we bring to customers, right? That's a thing that legitimately customers can't do themselves. Like they're not going to do power planning. They're not going to plan their memory footprint in 2028. Like they're just, they can't do that. And so that's one of the real values that we bring to, to our broad set of customers is just, that's just a whole set of things that you don't have to worry about and that, that we spend a huge amount of time thinking about.

• 26:26Speaker 01

  Yeah, yeah. You guys are generally, I think far that I could, the largest buyers of practically every component of a server, correct?

• 26:38Speaker 00

  I, I don't know that. I don't, I am sure that we're one of the, the, the bigger purchasers of, of components out there for sure. And who knows about everyone, but, and that kind of depends on how you think about them and how you measure. But

• 26:53Speaker 01

  So the value I was leading to is where do you see the, the constraints being most severe and let's call it 27, 28? And where do you see the constraints easing up?

• 27:04Speaker 00

  Yeah. It's funny. It's, so I'll answer this a roundabout way, but you know, I remember that in undergrad, we actually, this is so this is a long time ago, and I never thought this would be like a useful book that I read, but we read, we read the goal and then I read the goal, right? And so it turns out there's never one constraint. There's always just the latest constraint. And so you have to think about all of them. And as soon as you hit one, there's another one, right? And so, you know, what do you use? Like, what is the constraint that's going to happen in 20 and 28? Like, I actually don't think there will be one. I think it's like every month for us, it's, do you have enough power? And then as soon as power is no longer the constraint, you know, it might be memory, it might be TSMC capacity, it might be, you know, HBM, it might be networking components.

• 27:48Speaker 00

  It could be, you know, there's a blip somewhere in the supply chain and like, you know, connectors or whatever it is, right? At some point, you have to think about all of those pieces. And it's, you know, it's, it's not also the, kind of where they happen matters too, you know? So it's, it may be like, hey, we have a ton of power in Indonesia,

• 28:12Speaker 01

  Yeah.

• 28:12Speaker 00

  but we don't have enough in Germany.

• 28:14Speaker 01

  Yeah.

• 28:14Speaker 00

  Right? And so you, you think about kind of where in the world you want that capacity too, because it's, turns out that not everything is totally fungible. Some is and some's not.

• 28:22Speaker 01

  Yeah.

• 28:22Speaker 00

  But maybe disk drives, it might be SSDs, it might, like, we, we think about every single component and we have, you know, tens, hundreds of thousands of components, all that we track and think about. And, and some we rely on our, you know, our suppliers to, to manage, some we direct, much of it we directly manage. And, and yeah, we have a whole team that does that and they're fantastic. They're, they're industry leading and we, we kind of, we, we saw this problem coming probably a decade ago and really started not just thinking about, okay, how many servers do we need to track? But just thinking all the way through the supply chain, you know, four tiers, five tiers down.

• 28:59Speaker 01

  Yeah.

• 29:00Speaker 00

  What is the component that, that could cause an issue for us and making sure that we had kind of guaranteed supply on that? If you think, if you remember, I don't remember when this was, this was over a decade ago, when there was like the floods in Thailand and, and no one had disk drives anymore.

• 29:14Speaker 01

  Yeah.

• 29:15Speaker 00

  And so that was

• 29:16Speaker 01

  There was disk drive crises and then there was memory crisis.

• 29:18Speaker 00

  Exactly. And so, you know, it's like, I think we think through all of those things. And so we also think about where's there diversification in manufacturing, like all of those kinds of pieces we try to work through and, and we're never, we're never going to be perfect on it, but there's always a different supply constraint.

• 29:31Speaker 01

  Yeah. So, obviously there's a lot of wide-ranging debate about data centers, right? And it's clear that folks like us, where we stand, but do you think as an industry, we have not done a good job

• 29:48Speaker 00

  Yeah.

• 29:48Speaker 01

  of explaining why data centers are good for America, generally the world, but

• 29:55Speaker 00

  Yeah.

• 29:56Speaker 01

  And how do you, what's the internal talk

• 29:59Speaker 00

  Yeah.

• 30:00Speaker 01

  amongst Andy's team on how do we deal with this?

• 30:03Speaker 00

  Yeah. Well, look, I, I think, and I think you'll hear more from us over this. And I agree. I think we need to be more vocal and be more upfront. Cause we actually do a ton that's, that's really beneficial, both for the communities we operate in, for the, you know, we, we think a ton about how do we bring renewable energy to these data centers? How do we think about being water positive? Actually, our, our, our data centers use a really, really small amount of water. We mostly use free air cooling. How do we think about being great participants in the communities where we are and, and how do we bring high paying jobs to the communities we operate in? And not all data center operators do that. I think there are some, well, there are some well chronicled examples of others out there that are not great at that. And they just don't really pay attention to regulations.

• 30:45Speaker 00

  They think that the rules don't apply or they just, you know, launch really quickly without thinking about those. And I think that it causes problem for the whole industry because everybody kind of gets lumped into that. And so, so like, I think we'll be, we, I think you're right. We, you know, vocally self-critical, we need to be more vocal about the benefits that we do bring and think about additional ways that we can help communities understand the benefits that, that we bring to them, both for the, the services they use, right? If you usually go to a community and say, well, do you not want to use Netflix? And they'll be like, no, no, like I still want Netflix, like I said. And, you know, like it's important for us to think about and highlight the benefits that we bring where I recently saw a report where one of the communities that we operate in, the,

• 31:29Speaker 00

  everybody in that county pays $5,000 a year less in taxes because of the taxes that we bring to that. And we don't, we don't tell them, they don't even know it.

• 31:37Speaker 01

  Yeah.

• 31:37Speaker 00

  Right? It's just invisible to them. And so I think we just need to be more clear about those benefits that we bring. Cause I think if you told the communities, by the way, your tax bill is $5,000 less than it would otherwise be if we weren't here, they might have a little bit of different thought about the building that's over there.

• 31:53Speaker 01

  Yeah.

• 31:54Speaker 00

  Yeah. So, and, and, but, but not everyone does that. Not everyone kind of

• 31:57Speaker 01

  Presumably there's no transparency that's needed in the plans.

• 32:00Speaker 00

  Look, I think much of the data center community, not just us, is actually pretty good actors. And there's just a few that aren't, that, that, that kind of, I think have, have caused some of the angst recently. And I think we just need to be a better job of highlighting, you know, who's, who's being good citizens and who's not.

• 32:16Speaker 01

  Yeah. And before we leave the hardware topic, I want to touch upon Trinium.

• 32:22Speaker 00

  Uh huh.

• 32:22Speaker 01

  And your whole history with building your own Chips.

• 32:27Speaker 00

  Yeah.

• 32:30Speaker 01

  We were one of your first partners using Nitro a long time ago. And since then, Graviton and we made tremendous progress. So what was the thinking that led to saying, look, we're going to do our own thing? And then how has that progress been and whether?

• 32:45Speaker 00

  Yeah. It's actually a fascinating story, and I think it's a great example of where Amazon AWS will innovate and will iterate over time and continue to think bigger about what we can do. But but but kind of prove our way there as opposed to, you know, and so to use as an example. It's probably now 10, it's probably about 13, 14 years ago. We were seeing that there was a pretty significant virtualization tax on the overall number of resources. And and we were kind of thinking about how do we? Our customers were telling us, I want bare metal performance and they, you know, they're comparing to having all the resources of a of a server.

• 33:27Speaker 00

  And so the first thing that we did is that we took a network offload card and virtualized all of our network virtualization and pulled it off onto an offload card. So that network virtualization got closer to to bare metal performance. And back then it was not quite bare metal, but it was close, it was closer. And then we got really excited about that. And we said, OK, what if we could move storage virtualization off as well? Right? And and and none of the network offload cards could do that. And then we found this one company who had some ARM cores on an offload card. And they were doing it for other reasons. I can't remember their original purpose, but we're like, could you use those to do storage virtualization and some of these other functions? And they're like, maybe. And so we really iterated with them. This was the Anapurna team.

• 34:07Speaker 01

  Yeah, this is the Anapurna team.

• 34:08Speaker 00

  And and just love that team, like really innovative, really mission driven, really wanting to solve problems. And so we acquired them and we said, look, could you build us a slightly bigger card that actually could take all the network virtualization off? And basically, give us a bare metal server that has no virtualization on it, no VM virtualization. Everything is through APIs on the card. And because we had this view that, one, performance would be much better, research, a resource utilization would be better. It'd be we do.

• 34:35Speaker 01

  The security isolation.

• 34:36Speaker 00

  And security isolation would be much, much better. And we tell people, you know, could then legitimately tell people, we have no access to any of your VMs that are running there. And and and this has been a huge benefit for us for the last decade, where frankly, like we've been leading and and others have been kind of slow to do this because they, this is not a generalized thing that people can do. But so we got to that and we basically said, look, we're, we're making a lot of progress here.

• 35:02Speaker 00

  What if we take, you know, and there's a bunch of ARM cores that run this offload card? And we said, what if we turn that into a server? And we did that first with Graviton. It was a very underpowered, very small server that we launched. And customers were excited. They're like, I'd love to have an ARM server. This is super interesting. And so we went down the path and and Graviton. You know, and part of what we did is we looked and saw that there's the the slope of, you know, ARM cores were getting faster.

• 35:28Speaker 01

  Yep.

• 35:28Speaker 00

  And where the you saw the power utilization and the graph where they, and you knew the intercept was going to happen for where this architecture was going to be really good for for parts. And they just needed somebody to drive the ecosystem and get some of the pieces in place. So we did that with Graviton and Graviton has been a runaway hit at this point.

• 35:46Speaker 01

  Have you been public about what percent of your fleet is?

• 35:48Speaker 00

  We land more Graviton chips every year than

• 35:54Speaker 00

  than than any other type. So it's, it's, it's very popular.

• 35:59Speaker 00

  And it's, you know, look, we, they're 20% cheaper and a 20% better performance and have been like that for the last kind of five, six years. So that's a, it's an easy value proposition that, and I think the vast majority, something like 90 plus percent of our top hundred customers all use Graviton in some way, shape or form across their fleets. And so that's been a huge win for us and for customers. It's been, it's probably the single big, the single easiest way that customers lower their bill is to move to Graviton.

• 36:27Speaker 00

  They can off, we've, we've had examples where people have moved their whole fleets and cut the number of servers they had in half. The performance is so much better.

• 36:34Speaker 01

  It's amazing.

• 36:34Speaker 00

  And so half as number of servers, each server costs less, like it's, it's, it's a big win. And so then about five, six years ago, we said, you know, we saw the rise of of AI compute happening. Not nearly expecting what it was today, but still saw it was going to be a big material mover. And so we went in, built our first chip in in Tranium and we're now, you know, in market with the third generation in Tranium three. And I've seen fantastic results. So it's, you know, we're, we're sold out

• 37:04Speaker 00

  for capacity through probably maybe towards the end of next year or something like that. And and we were trying to, again, we're trying to figure out how we can save some capacity and get startups to be able to use some of the capacity. Cause it's a, we see great results. So most of, you know, the, the, the majority of of traffic on Bedrock all runs on Tranium. And we have great deals with both Anthropic and OpenAI to build on top of Tranium. As well as a, a, a set of of smaller startups. And, you know, I think we have half dozen to a dozen startups that are building on top of Tranium now too. And.

• 37:36Speaker 01

  So now, the name suggests it's a training chip.

• 37:39Speaker 00

  Yeah, we're bad.

• 37:40Speaker 01

  Everybody is using it for inference too. So where is the lean architecturally and where is it going?

• 37:47Speaker 00

  It's a good point. Look, the vocally self critical, we're terrible at naming. And so it's not, it's not our strength. Originally, we had a chip called Inferentia for inference and training for Tranium for training. And then as the models got bigger and bigger, and it turns out you actually want to run the inference on these really large systems. It turns out.

• 38:02Speaker 01

  And training requires.

• 38:03Speaker 00

  Turns out that Tranium is actually maybe the best inference chip on the market right now. From a absolute performance and cost performance point of view. And so.

• 38:13Speaker 01

  Is it, it's, it's the better memory, memory bandwidth? Where is the?

• 38:18Speaker 00

  It has, it has, it's just the architecture is a little bit different than than than others. And it's much cheaper. And so from a cost performance perspective and absolute performance perspective, Tranium is great.

• 38:30Speaker 00

  And so we're, you know, we, we use it a ton for for inference. And as I said, Bedrock, it drives much of the Bedrock

• 38:39Speaker 00

  inference today. And but it's also a good training chip too. And it's, you know, I think we're, we, we, you know, a lot of our broad set of customers usage is not in training models, but is in using it. And so that's where a lot of people get to use it under the covers. And that's where we're excited about it. But but a lot of the big customers are interested in it for training clusters as well. And particularly as you get to Tranium three and four, which we've announced, we haven't, we haven't launched Tranium four yet, but announced it. Folks have kind of looked at that architecture and said, yeah, that's the future of where all my training clusters to be as well. So we're, we're quite excited about the future, where that goes for these really broad scale training clusters also. But, but yeah, it's both.

• 39:16Speaker 01

  Now, let's get back to talking about agents, but from a perspective of large enterprises or large medium enterprises.

• 39:24Speaker 00

  Yeah.

• 39:25Speaker 01

  Where are they in their adoption? And have they, what sort of benefits are you seeing them reap already? And what is the roadmap for them as far as you can tell from your vantage point?

• 39:39Speaker 00

  Yeah, it's, it's a really good question. I think it's one that

• 39:43Speaker 00

  that we've spent a lot of time thinking about. And when I talk to customers all out there today, they view, you know, they're getting a lot of value out of what they've done today. And I would say the agents that most enterprises have built are relatively simple and straightforward. And they're starting to think about, and they're mostly non-autonomous, right? They're still kind of people in the loop, if you will. And so I think we're, and by the way, there's, customers are still getting lots of value out of that today. And so they're really thinking about, how do I have these be autonomous, but in a safe way? And I think there's two things that that I think hold customers back today from just continuing to to scale.

• 40:24Speaker 00

  And it's, it's already a pretty big business today, but I think it's, it has a massive opportunity to really change every single customer out there and every single workflow and then really thinking about it. And so number one is just how to think about it. I think what we virtually originally saw was that enterprises had a workflow and they're saying, great, I would have the enterprise, I would have an agent go do the same workflow.

• 40:44Speaker 01

  Yeah.

• 40:45Speaker 00

  And what we encourage them to really do is think, not just replicate, you know, Bob does step one, two, three, four, five. So agent is going to do one step, one, two, three, four, five. And then Bob's going to check it at the end. That's not really the model you want. You want to actually step back and say, if I want to accomplish something, how can an agent do it differently? It can do it in a massively paralyzed way. It can try 50 different things and get to that. And and how do you help it get to that right outcome? And rethink how a computer would solve a problem versus a human solving a problem. And so one of the things is us just helping customers understand how to think about that and really kind of have that blank slate. Because that's where you really get value is not just replicating what you're doing today. But but thinking from a greenfield approach about how you go solve a problem completely differently. And I'm sure that's how many of your startups are thinking about this too.

• 41:30Speaker 01

  Yeah.

• 41:31Speaker 00

  How do you help customers greenfield solve a problem, not replicate the thing that happens today? So that, that is number one.

• 41:37Speaker 01

  People running fleets of agents and swamps, whatever you want to call them.

• 41:40Speaker 00

  Yeah.

• 41:41Speaker 01

  Very common these days.

• 41:42Speaker 00

  And you just want to think about it, you know, and and so enterprises are not as, as, again, this is one where you learn from the startups and you try to, how do you apply that to an enterprise world where there, you know, an insurance company is not necessarily as forward leaning, but they would love to figure out how they can have a better, you know, approval workflow or something like that. So that's number one. But then the second one is this, this, how do you turn those into fully autonomous workflows? And how do you actually trust the agents? And so we're spending a lot of time thinking about, how do we build services to help enterprises feel like their systems are secure? And that they can trust an agent to make a decision, that they can have the right guardrails, that they can have the right permissions on their data? That it's not going to delete production systems, that it's not going to make tragic mistakes.

• 42:25Speaker 00

  And right now, I think that nervousness is probably holding people back. That maybe appropriately, by the way, is holding enterprises back from just saying, OK, go nuts. Like, you don't actually want an agent to just go crazy and accidentally delete a production database. That's going to be pretty bad. And so you, that's how we're, we're kind of actively working through this with customers on how do we both help the architect and frankly, invent new technologies and capabilities that are going to help them solve that problem? And so that, that is one of the areas where I think we'll continue to innovate and and and and we'll get there. I think we have some really good ideas and some good technologies brewing that I think can really help.

• 43:02Speaker 01

  So are enterprises learning how to do eval systems and so on and so forth to keep the agents?

• 43:08Speaker 00

  They need help. Honestly, like you, you, like both, like evals, how do you have a constant loop of testing? How do you, how do you think about, you know, goal seeking in a, in a reasonable way? How do you have your data labeled in such a way that it actually even makes sense that the eval can actually kind of approximate what you're going to be doing in production? How do you measure in production and back test it so you're not seeing drift? All of those things are problems that that enterprises don't know how to solve today. I actually don't know if anyone really is great at solving these today. It's why you've seen so many FDE teams kind of spin up and AWSs and our partners are really leaning into the the FDE motion to go and help. And this is the single biggest area where customers need help. And and when we think about how you, you, and.

• 43:53Speaker 00

  My view is we want to train our customers to be able to go and do this themselves. Right. This is not the traditional motion where I want to have a people driven business that goes on forever, where you just keep paying consultants over and over and over again. Our, our view on how FTEs should work and, and, and is, um, we want to go into a customer who's ready to accept the, you know, to really kind of accept owning this when we're done. And in 45 days, do work where we can teach them how to make an eval, teach them how to get their data in a labeled way. Do work alongside with them. And then at the end of 45 days, you leave and that customer is, is good and ready to go and trained up. And that's what our customers tell us they want. They don't want to be beholden to a external workforce for the next five years.

• 44:38Speaker 00

  And, um, but they need help today.

• 44:40Speaker 01

  Yeah. You've made a massive investment in FTEs.

• 44:42Speaker 00

  Yeah.

• 44:44Speaker 01

  So taking that even one step further, right? Some of your industry peers have said, look, you can't have all of your data going into a big frontier model. What enterprises should really do is to take an open source model and then post train on your own data and work close and phrases and whatnot. Where do you stand on that? Are you seeing customers actually trying to do that? Or do you guys, how do you, how do you think about that?

• 45:09Speaker 00

  It's great. The first point, I, I wholeheartedly agree on that first point, like the customers and like enterprise data is their most valuable asset. And so from the very beginning, it's why we built Bedrock like we did. We have a guarantee that your data never leaves your EPC. And so your, if you're running inside of Bedrock, your data doesn't go back to the model provider. They never see your prompts. That stays inside of your own trusted environment. And so that, that is why, um, enterprises kind of run, they, they prefer to run on top of Bedrock. And it's why you see that business growing massively, like hundreds and hundreds. I mean, it's every, every month, we see that just business, just every week, we see that business exploding.

• 45:50Speaker 00

  And then, and it's why, um, you see OpenAI workloads migrating to Bedrock. It's why you see Anthropic really growing really rapidly. I mean, so whether you're using open models or, um, uh, or, or, um, closed frontier models. I think Bedrock is a great solution that our, our, our customers told us, by the way, like, you know, we, if you remember three years ago, I got a lot of heat.

• 46:11Speaker 01

  Speaking of bad names, that's a good name though.

• 46:13Speaker 00

  Yeah. Bedrock is a good name. That's good. But we, we got a lot of heat actually for being slow to the AI world because we actually built the foundations of this where we said, look, we're not just going to rush out of service. We really want to think about how do we make sure that we protect our customers' data? And build a service that we think is going to be durable for the use cases that we knew about. And if you remember, we got a lot of heat and we said, look, we're going to go build the right thing. And now as people move from proof of concepts to production, vast majority of them are landing in AWS on Bedrock. For, for much, one of the reasons is because of this. It's also because of the, the set of services that we have. We also offer open models. We offer proprietary models. We offer, um, a whole set of capabilities around those agent core. We build these building blocks. So it's easier to build agents with any of the models that you want.

• 46:57Speaker 00

  Whether they're in Bedrock or out of Bedrock, for that matter, you can use Gemini or other things for it. But, um, but I think that's a, it's a, it's a differentiating piece for us. And it's a super important thing to think about because having that data go back into the, the model provider, I think is a, is a dangerous thing. You talk about open weights models though. I do think that there's a scenario that I'm excited about. Where we're really ramping up our support of open weights models and trying to build a good,

• 47:24Speaker 00

  environment. And frankly, this is where today, I think, and I think it's true, a lot of customers believe that they have meaningful proprietary data, that if they could mix in, you know, do some post-training, do some fine tuning, to an open weights model that they could distill down, they can actually get a better performing model at a lower price. There's a bunch of pieces here that have to work out. Well, they actually have a good eval to actually prove that that's true. Most people are doing that in SageMaker today. I think there's more that we can do to make that easier. But actually like, if you go look at where are people doing that, they actually do it in SageMaker on AWS today. And they actually host the inference via SageMaker.

• 48:00Speaker 01

  SageMaker is getting a new lease of life as a.

• 48:02Speaker 00

  I mean, it is, it was always kind of a model building platform, right? And now, you know, there's, if, if you think about what enterprise are doing in this world, that's what they're doing is they're basically effectively building their own custom models. And so that is, SageMaker is a, is a great place of doing that. And I think there's some things that we need to keep building on to make that easier and easier to do. And to test across different open weights models, things like that. But that's a, um, it's a, it's an evolving space. And I think it's a super interesting one. And it's one we want to make sure that we have all the right things for, um, for customers to be able to do if they, if they have the, the right data and expertise to actually go down that path.

• 48:36Speaker 01

  And with all the debate around AI extension risks and this, that and the other, and the security,

• 48:46Speaker 01

  vulnerabilities and so on and so forth, and the hugging face attack. How are enterprise, what are, what are CEOs asking you about all these things?

• 48:55Speaker 00

  Yeah. That, there's a bunch and they, look, they, they mostly want to say like, and it goes back to this, like when I launch agents, how can I trust that they're going to do what I want them to do? And so we're in increasingly, uh, we have been for a while, like we're basically, we're heavily investing in building capabilities that allow people to deploy agents safely into their environment and think about those controls. And some of those are, how do you make sure the agents have the right permissions? How do they have the right sandboxing? How do you make sure that you have the right set of guardrails? How do you, how do you really intentionally think about what you want the agents to do and not do? Is there a human in the loop or not? And so we, we spend a lot of time with our customers thinking about how do you think about safe agent deployment? And get better over time and, um, and what other capabilities do we need to go build to help people deploy agents safely into their environment?

• 49:40Speaker 00

  So there, there's a lot there. Um, the other age, the other angle on that, which, um, a lot of people are worried about, which is just, are some of these really powerful models going to be, um, attack services and, and kind of mythosphere? And so I, I kind of have a view on their, their, yes, that is a real risk, I think, to, to customer environments. But it's also, um, a real opportunity. And so we, we recently launched a service called Continuum. They uses these powerful models to help customers go secure their environment. And so we'll look across their environment, uh, look for vulnerabilities with them. We'll use some of these powerful models and, um,

• 50:19Speaker 00

  and help customers find vulnerabilities they haven't found before. And most importantly, by the way, prioritize which ones, because we know context about their environment, how it's set up, where their permissions are, where they may have compensating controls that, that make it harder or easier to do. Um, and so Continuum is incredibly popular with customers. Actually, we're, um, um, um, really bullish about what's possible from AI to help with AI powered security. Cause look, at some point, customers are going to need security at machine speed, not at, at human speed, not at like an alarm, someone goes in, look at it. And so that, you know, we're running fast to go help build that for customers to help them protect their environments. And, and I'm very excited about what the, the Continuum team is building on that front too.

• 51:01Speaker 01

  So within AWS itself,

• 51:04Speaker 01

  like how, what's the state of the usage of agents in the bra and.

• 51:11Speaker 00

  Yeah. Well, Continuum is basically us trying to expose what we do internally. And so we, we use, uh, AI extensively for our own security. We use AI extensively for our own software development. Uh, we use, uh, agents. Actually, one of the things that's really cool is we use agents across our entire business. And so we, we rolled out, um, Amazon Quick to every single Amazon employee. And now I see HR teams building agents to, to help drive what, what used to take teams of people weeks to do that a single person can now do in a couple of hours to think about kind of team planning and, and, and resource management. I have finance teams that are building, um, agents to, to go think about how do they go pull tax rules from everywhere and ensure that we have compliance on a bunch of different pieces?

• 51:55Speaker 00

  And, um, super cool to see that things that used to be blocked by software developers. Actually, the, the, the line of business folks are able to go and, and unblock themselves and, and innovate more quickly. And so Quick has been a, an enormous blowwork and, and that has, has grown like wildfire. We see, customers like small startups using it all the way to the, the largest enterprises in the world, rolling it out to their entire customer base to get the benefit of kind of being able to access all of your enterprise data and easily apply agents and, and capabilities to, to help you accelerate your jobs. And so, I guess we use it across everything from software development to security to, um, to, to driving HR policies.

• 52:34Speaker 01

  I mean, you guys are notorious for measuring every, everything about your operation. Where have you seen the biggest gains?

• 52:42Speaker 00

  Yeah, it's.

• 52:42Speaker 01

  I mean, obviously.

• 52:43Speaker 00

  You know, the answer, software development. Like that's the real answer.

• 52:45Speaker 01

  The derivatives of that.

• 52:46Speaker 00

  But yeah, um, the software, I mean, the speed of software development has been, uh, and, and really product development, uh, as a whole, not just coding.

• 52:54Speaker 01

  So you've seen actuals.

• 52:55Speaker 00

  Absolutely. The pace, the pace at which we're deploying new products is, is, um, is massively different than it has been in the past. I think you can see this where, you know, AWS has always been known for rolling out features really quickly. And, uh, and it's, you know, we've, we've seen that turbo boost on that in the last year or so, as, as we've got, we call them frontier teams. As they think about agentic development as opposed to kind of traditional development. And it's, you know, it's not code completion. It really is agent first, the agents write all of the code. You're just managing a, a team of agents and, and driving that. And it's, it's been fun to see the, the, the pace at which, um, innovations for customers is, has been massive. And, um, and it has to be, because that's the, that's the.

• 53:37Speaker 01

  That's a promise.

• 53:38Speaker 00

  Our customers out there have a, a, a, almost insatiable appetite for new capabilities. And, and that's what we've got to do.

• 53:45Speaker 01

  Yeah. So organizationally, are you like, do you have any insights on how organizations should change and how?

• 53:53Speaker 00

  I don't, uh, I'll say that.

• 53:54Speaker 01

  Agents manage people, people manage agents.

• 53:58Speaker 00

  There's like, there's going to be, there's going to be lots of people for a long period of time. I do think organizations will change. I don't know the magic answer yet. Um, but, uh, but we're actively thinking about it.

• 54:08Speaker 01

  Inside, you're just trying various experiments or what?

• 54:10Speaker 00

  Yeah. We're trying experiments. We're thinking about pods. Um, as you think about, you know, here's one example is in a product organization, you used to have a team that would own a particular kind of capability for a long time. And you might have 10 people working on that thing. Well, today you can innovate so rapidly that one, doesn't have to be 10 people. It can be three to four people. And they build something so fast, you actually want to move them to different projects and, and problems. And so thinking about how do you both operate and, and maintain the things that you've built while being agile and flexible to move around on an organization that's as big as AWS is, is active things that we're experimenting with and playing with. But it's, um, it's fun and it's enabling for our employees. They actually love it because they can build faster and do more. But, um, but there's real work there.

• 54:52Speaker 01

  Yeah. It's a fascinating time. And, uh, so thank you very much for your time. We could be talking about this for hours together, but, uh, thanks for all your insights.

• 55:00Speaker 00

  Yeah. Thank you for having me and, uh, and thank you. We, we, we love having, uh, all your, uh, your companies as customers and, uh, we love learning from them and, uh, appreciate having me here.

• 55:09Speaker 01

  Yeah. We'll keep sending them your way.

• 55:10Speaker 00

  Excellent. Thank you.

• 55:11Speaker 01

  Thanks.

• 55:14Erik Torenberg?

  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 at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 55:40Erik Torenberg?

  As a reminder, the content here is for informational purposes only. 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/disclosures.