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Matt Garman

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2025-01-13
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2025-01-13
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  1. That's a good question. I don't know that there was any one product that I got excited about. At the first product that I ever used that said, hey, I think this is real. It's just like everybody else. I think ChatGPT was just a transformational product. It was a great UI and it really unlocked for everyone what was possible. So the first time that I really kind of realized that this was going to take off, right? And we were making investments internally, but I think we were hopeful that they would get there. I think that's the first one that I use that I was that I really understood. Now it's hard because I use thousands of them and I think all of them are really cool. And I think there's a lot of startups from people that are building AI products that are making new proteins, which is incredible to folks like Perplexity that are making search engines that are much more interesting to context centers, to banking applications. Like there's a whole host of them now that are incredible. I think Amazon makes some and many of our partners make many. So those are all incredible. But it really was like just like the rest of the world. I think Chat.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  2. From a software perspective and then reselling those foundational models, it's a good question. I don't know the answer to that of when that investment kind of fully pays off for an open AI or an anthropic or I think Amazon and Google probably have a different math of when we can make those pay off because you get kind of an internal usage of them from your own use. I don't know that, but there's a lot of smart people investing in continuing to put investment in a broad swath of AI companies. And you have to believe, which we do, that there is a massive economic benefit from many of these AI capabilities that are orders of magnitude bigger. And I do think it really does play into that math equation. As inference gets cheaper and more capable, that there are multiple orders of magnitude, more inference to be done, and that is kind of when it ultimately starts to pay off, I think, for a lot of those model providers and in a huge, massive way.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  3. If you think globally, I think it's ROI positive now. I think the question is when does it become more evenly distributed? Look, I think the hardest question of that, honestly, is for the model producers. I think that's the single hardest question. I actually think it very quickly today, or if not today, very soon, is going to be ROI positive for the broad swath of customers using AI and building it in banks and insurance companies and pharmaceuticals and others like that. You can make that ROI positive story today. And I think it will continue to get better. And I think for infrastructure providers like Nvidia, of course, it's very positive. I think the question is, when does the, because the folks that are making the most huge investments are the ones that are building foundational models?

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  4. I think it's ROI positive. Well, it depends on what you mean by ROI positive. I think there's a lot of investment in the world.

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  5. Can build them and Amazon can take advantage of them. That's fantastic, and both of those things are true. So, yes, it's a big forward investment, but we also have Amazon still using these. And we're in a different place now. When we started in 2006, we had zero external customers. So we now have a million external customers or multiple millions of external customers, right? And so that is a huge customer base that is ready and willing and excited to buy and use the products that we have. And so that investment, it is forward investment, but you also have a really big base that you can amortize across and go offer it to, which makes that investment thesis a little bit easier to get over.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  6. Early customer to learn from the components that they would need, but we built them from the ground up to support a broad range of customers. And so AWS itself was a big investment by Amazon to go after a broad new business. As you think about now, we had Amazon as a big customer of ours for sure, and they were a super helpful customer for us to learn about what large enterprises would need from services like AWS and continue to be. I think AI is not that dissimilar. Like Amazon needs AI. And I think if you mention that you watched my reInvent keynote, Andy was up there for 25 minutes talking about all of the cool things that Amazon, the rest of Amazon, is doing with regards to AI. And you're talking about Rufus or talking about how we're thinking about our supply chain, a fulfillment centers, and across the whole scope of an Alexa, we actually, that business desperately needs AI capabilities to, again, reimagine our business, get more efficiencies, deliver new experiences for customers. Like we are, Amazon is customer number one for a bunch of these capabilities. And so if AWS,

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  7. It's not the right characterization of it. So there's a couple things I would say is number one is AWS was never about excess capacity about Amazon. Just like math doesn't work, right? You can imagine, like I've heard that narrative, it sounds nice. And as soon as Christmas time comes around, if I have to take Netflix's servers away so that we can support retail traffic, that doesn't really work as a business. So it was never the idea nor the intent nor the goal of AWS. And we built the businesses from scratch, right? They weren't like reusing Amazon components. We learned from that. They were an incredible...

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  8. Let me just put that in a framework that makes it maybe a little bit sharper, right? You've been at AWS since the beginning. AWS started, and I'm going to flatten this narrative and you can correct me for it being a little too flat. But just in the flattest possible way, Amazon is building a bunch of these services. Hey, we have excess capacity. Hey, we want to build microservices for our own components. We can resell those. So you get a bunch of benefit along the way of just building Amazon. And you can turn that into a business. AI right now, it feels like there's a bunch of ideas for products that might be useful. Inside Amazon, outside of Amazon, for AWS as customers, whoever, but it requires a massive amount of forward investment, right? It's not just we're kind of doing it anyway. It's much more, hey, there's a huge opportunity here we need to leap rug ahead and maybe get some more customers or maybe there's a platform shift or whatever it is. We all see the huge promise and that is happening at a subsidy. And that subsidy.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  9. Are enabled today that can deliver real value. And some of those are kind of broadly reported around things like modernizing your contact center and we think Connect is a great offering for customers to do that. And we're actually seeing huge number of customers move to Connect in a cloud contact center to take advantage of many of those AI capabilities. You see some of that in optimizing some of your back office projects. And I think increasingly as the Agentic workflows really get much more powerful and as we think about collaborative agentic workflows and longer running agentic workflows, you're going to see more and more value come up through these. And as the models get more capable, you're going to see more value coming up through those. And so it's on us. It's incumbent on us to make sure that these are very profitable for end customers to go and implement.

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  10. I think, look, and for customers, they're increasingly looking at it this way. Like, it's not just us. And I said this a little bit ago. If you talk to customers, they are very focused. And how do they have ROI positive AI projects? And I think the cloud has already proven across a broad swath of industries to be ROI positive for many different industries. I think we're kind of like moving your data to the cloud, your compute to the cloud, you gain agility. That, I think we've proven that we can deliver great ROI for customers in moving to the cloud broadly, broadly speaking, and taking IAS side. And so what we're increasingly seeing customers say is I want to see the ROI of these AI projects. And I do think that that is an important shift where it is not cool. It's not just the cool, it's not just the shiny object factor. How do I make sure this makes sense? And we spending time with customers thinking about that. How do you work through the use cases?

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  11. Where you need the power to be. Another of the bottlenecks that we run into is around transmission. It's not just power generation, but it's transmission. So you can have a solar farm out in the desert. But if you don't have transmission to get it to where your data centers are, then it doesn't do a lot of good. And so those are both problems that need to be solved. And it's not just data centers, it's electric cars, it's electrification of all of our businesses. Like there's a bunch of these things that are going to need to happen. And so I think nuclear is going to be an important part of that. But post-mall modular reactors, and I think the world's going to have to build more of these large industrial scale nuclear plants as well. I think a lot of people's heads are in the scary back in the 50s when the technology wasn't as safe today. It's a very safe, scalable technology, but it's, you know, it's something we have to keep spending and scaling.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  12. Yes, it is. We've made significant investments there. And it's a range of things, by the way. It's a portfolio. And this is not a new plan for us. Over the last five years, we have commissioned more renewable power projects than each of the year, each year for the last five years, we've commissioned more than any company in the world. And that's bringing on new power into the grids and whether they're new solar farms or the new wind farms. And now we're adding nuclear to that. And so it's just a portfolio of that. I think the world is going to need more carbon-free energy. And compute and data centers are a big portion of that. And we're pushing hard to make sure that the world has enough sources of that. And I do think that nuclear will be an important component of that plan over the next couple of decades. And so we're excited about small modular reactors. I think that it's a technology that's a little ways away, by the way. It's solved for the next couple of years, but past 2030 and beyond, I think it could be a very important component. One, because you can actually put it near.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  13. Don't have anything to announce there, but we partner with lots of folks. We partner with Samsung, we partner with Intel, we partner with others that have their own fabs as well and buy lots of other stuff from them from memory to CPUs and other things like that. So we buy parts from lots of different fabs around the world.

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  14. I mean, that's where they are right now. But I'm saying I think, I mean, they're making lots of investments, and I think they're scaling, and I think others are looking to catch up in that space too. And they have a great lead. And this is also true in technology and has been for a long time, is that somebody jumps ahead and figures out a gets a lead and it's a benefit for them for a while and others catch up. And I think you can look at some of the HBM memory and some of those other fabs that are coming up and they're catching up and finding other new ways to do that. And there will be other inventions that leapfrog over time. But obviously fabs are hugely capital intensive investments and others like that. And so I am sure that others will eventually find new and different ways to innovate around that too. It has always been true in technology.

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  15. They figured out parts of that. I mean, they figured out the layout chip. And by the way, CC and the team did a fantastic job of figuring it out. So yes, but the world figures in it. But Intel Felfel.

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  16. Figure out technology to get past a couple of those, right? I remember somewhere around 10 nanometers, people were like, I don't think you can get past this. And now we're building three nanometer chips, right? And so you keep getting smaller because there's new technologies in there. You had to figure out how you deal with interference and you had to think about how you think about actually stacking the memory and different structures of the chips and other things like that. But you work through those. In the meantime, you kind of figured out how to do more compute on an accelerator like a GPU that then gave you a huge step change in compute. And so no longer people are worried about, did you, you know, are we hitting the limits of what a 17 nanometer Intel chip from 10 years ago was doing, right? Now we're orders of magnitude more compute than that. Well, hold on, hold on.

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  17. I think people like to talk about scaling laws because, again, it sounds fun to talk about. But I think that it probably just means there has to be more levels of invention. Like, I think if you look over in any technology ramp, you kind of see one particular technique ramping like this, and then it slows down, and then somebody says, oh, how about you try this? And then it goes back up again, and then you try something else. And so there's going to have to be software and all algorithmic changes. I think it's not a blind, like, dump more data and more compute. you know, close your eyes and you'll get a bigger model next year. You're going to have to have smart people looking at it and driving it and figuring out new ways to help that. But that doesn't mean that you've hit a limit. I think it's just you're going to have to keep innovating on different ways. And you think about, again, think about number one, how long, and it was a longer than a decade that people were saying that we were hitting Moore's Law scaling limits. That was just, you know, can you take 17 nanometers and make it 15 nanometers and 13 nanometers and you're saying like, okay, there's going to be a limit. Number one, they had to.

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  18. Chips and chip investment is a long-term decision, right? You're making decisions now. You're allocating capital now that might not pay off for a decade or more. Do you think that model training is hitting a scaling limit? That it's going to plateau the way that some people are saying it's plateauing.

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  19. And so that additional choice is nice. And that means part of that choice is we want to really lean in and make sure it's the best place to run NVIDIA GPUs and AMD and Intel and others. And so we think it's, but it's a big opportunity for us. And if you do think, which we do, that AI is going to disrupt all of those different industries. It's a massive opportunity where it's not one player is going to be the only compute platform that all of those things run in over time. And we think that we have an opportunity to build some of that and provide differentiated choice for customers that choose to run AWS.

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  20. Don't think that it has to meet every single use case for every single customer. We think that NVIDIA GPUs and AMD GPUs and others are going to be super interesting. Like they have good platforms. Both of them have very good teams that are executing really, really well. And I think they will continue to do that. I don't see any reason why they wouldn't. And we plan to be a great partner of theirs for a really long time and support that and offer it to customers for when it's the right technology choice for their use case. And we think that we can offer interesting choice. And we've done it with Graviton. We've proven that we can launch a processor at broad scale that is very useful for a set of workloads, a broad set of workloads for our customers. And it doesn't, in Graviton's case, it doesn't mean we don't buy a ton of Intel and AMD chips and offer those to customers. We, of course, do. And those are growing businesses for us as well. It's just more choice. And we think that choice makes AWS a more attractive platform for customers because they have more choices than they do other places.

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  21. And so it turns out we've been making ships now for over a decade. So we've been making silicon chips, our own custom silicon for more than a decade. And so we have one of the most experienced teams in the industry doing this. And so it's not a new thing. It's not like we delve in here and said we have no idea what we're doing, which is, by the way, some of those others are learning it for the first time, not NVIDIA's and of course AMDs and Google's been doing it for a little while too. I think Microsoft is pretty new to this space. But we think that that is a big advantage for us as we understand how to do this at scale. We understand how to do it in the cloud. I think we have some advantages in that we don't have to do it for a broad set of customers. We have to deploy our chips in exactly one environment, right? We have to deploy them in an AWS data center. We have to do them deploy them in exactly one server. We don't have to support a whole OEM infrastructure. We don't have to support a set of different drivers and a bunch of different things. It's just in our environment and we know exactly what that's going to look like. And we think it's a choice.

    2025-01-13 · Decoder with Nilay Patel · Why CEO Matt Garman is willing to bet AWS on AI · IDENTIFIED FROM THE TRANSCRIPT

  22. Well, I'll tell you in my keynote and reInvent, I talked about another thing that I like to do in Amazon that we do here, which is we refuse a thing that we call the tyranny of the ore, which is forcing someone to pick A or B stifles innovation because it means that you don't go out and invent how to do A and B. And so you can't pick. I'm just telling you, like, it is not an A or a B chance. It's an A and B. And we have to push our team to figure out how to do both, which is enable bigger training. And we have to lower the cost of that, by the way. It can't just keep scaling linearly, which is all part of the Silicon Investments that we're making and networking and things like that, which is how do you make the cost to train these really large models lower so that

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  23. No longer interested in bright shiny AI proof of concepts. They want something with real ROI associated with it. And the way you deliver great ROI is you either have more value and or less cost. And I think both of those are going to be important to keep raising the level of ROI that you can deliver on. And so if, as we think, there is this massive ability to transform organizations, you have to keep increasing what models can do and decreasing how much they can cost. And so I don't see how you pick one of those. I think you have to do both.

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  24. I don't think you can pick one or the other. You absolutely like the world is going to deliver more capable models and they are expensive and they require a lot of compute and it's an area of investment for us and it's an investment for many of our customers. And I think it's the right area of investment for a lot of those because I do think you don't get more capable smaller models if you don't have the large model to start with. Like it is that is just how it works. You can't come out with something that's a really, really powerful small model if you didn't also build a frontier model or start with frontier model. So you have to have those large frontier models. And I think we're going to need those to be more capable. And there's a lot of innovation and inference and how you can drive cost down. And some of that is a systems problem, some of that is a hardware problem. Some of that is algorithmic problem. You can think about model distillation. There's a whole bunch of techniques that you can do to get these smaller, faster inference models, which I think are going to be hugely impactful and important to delivering real value to enterprises. I think you go talk to customers now and they are

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  25. A little bit less on the like, can I have a stake in the ground around marketing? Because I think at the end of the day, customers actually care about that first one, not that second one.

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  26. Find cures for cancer faster. Awesome. Like, that is a thing that I'm focused on. Was that AGI that did or not? I don't know. I'm not interested in that per se. I'm more interested in can I actually help our customers deliver value to their businesses?

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  27. I understand there are contractual terms that they're working through. They have some motivation of reasons to do that, from my understandings, but it's not about declaring anything. Like it is just, let's go figure out what you as a customer. This is, again, I am less interested in puffery in the press and more interested in how I can help customers achieve actual outcomes. And so it's fine. There can be marketing statements. There can be like, I have the biggest compute cluster in the world. I can be, I have AGI. Like, okay, at some point, I want to help a bank figure out how they can reduce the amount of fraud that they're seeing or improve the speed of which they can approve loans or whatever the thing is that actually goes and helps a business. I want to help biotech find cancer cures faster and better and figure out how they can significantly shrink and or improve the efficacy of what they find. And so those to me are interesting and useful outcomes. And so if you tell me like, hey, can you help a customer?

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  28. Next generation of compute will be, you know, it's going to deliver somewhere between and whatever the current generation is going to, that we just announced with Tranium 2 and eventually with Blackwells and GB200s, I think we'll give customers a 2 to 4x boost in compute capability per dollar. And we announced Tranium 3, which will give another 2x compute boost to compute by the end of 25. And so that is going to help that you will continue to get more and more and you're going to be able to do bigger and bigger things. And you're absolutely going to need algorithmic improvements as well, which many of the teams ours included are very focused on doing.

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  29. Software algorithms are going to play a big part of that, and you're going to need both of those. And so I don't know when you reach AGI. I don't know what that means, but I do think that

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  30. Number one, it's literally impossible question to ask because there's no definition of what AGI is. So when you reach it is also an impossible definition because I don't know you can't define when you reach an undefined thing. What I would say is I think that it's just a continuum and I think that AI, we'll call it AI inference, right? The ability to go do work is going to continually get more capable over time. And I think that there is a long road of this to get much, much, much more capable over time. And it's going to get much less expensive to run over time, which I think then explodes the number of ways in which people will find ways to make it useful, whether it's running agents, whether it's doing other workflows, whether it's long running reasoning tasks. I think there's a whole host of things you can imagine. And so there's just a continuum of where eventually the things land and where you're able to ask the computers to do more for you at lower costs. And I think hardware platforms are going to play a big part of that.

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  31. I think you and I are both about the same age, and you describe the typewriter workforce with the same sort of like, I think that's what it was like. I never had a job like that. It's the same way I did it. I'm like, I think typewriters, people had him.

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  32. It's not like multi-touch. It's not like that. I don't know if it's like an iPhone either, though. It may be more akin to the internet disruption. That's what I'm saying. I don't know if the internet is a platform per se. It's like a shift as to how you would deliver an application. So maybe it's a platform, but it is, I think it's more akin to like that where there will be fundamental shifts into how you deliver products and offerings and services and do your work on a daily basis. And so the internet has been hugely transformational on how you do your work on a daily basis. You used to sit there on a typewriter or, you know, I don't know, write memos or do whatever, and now you're on a computer all day and you're interacting on either SaaS applications or your emailing people or there's just a fundamental connectivity. And I do think that AI is more akin to something like that, where it kind of has that fundamental shift into how you're going to get work done.

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  33. That is a more open question. It's a huge enabling technology. And whether you build on that AI or that AI is just kind of embedded in everything that you build with and is a core component of what you build with and how you think about it's a tool that is really meaningful and impactful. I think it remains to be seen as exactly what that means, but it is a transformational technology. Can I make that simpler?

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  34. Good question. I'll start with. I believe that AI is incredibly transformational. Whether you call it a platform shift or not, I'll get to in a second. But I think it's an incredibly transformational technology that more than kind of, you know, look, these things come around every decade or so. And I think it is one of the technologies that has the ability to be completely transformational, whether it's transforming industries, transforming companies, transforming jobs, transforming workloads. I think it has a real potential to have a material impact on every single piece of how we think about work and life and user experiences and the like. I'm a full believer that that is true. And I think there's a timeline question, right? Like, is that going to be in the next 12 months or the next 24 months or the next five years? But I do think it is going to happen and it's going to be a real change on a lot of pieces of business. Platform shift is an interesting question because platform assumes that like AI is not yet a platform. And I think

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  35. All right, so I think this does bring us straight into AI because this is a bunch of decisions that everyone has to make. And the outcomes are, I would say, still uncertain. As an industry, everyone is telling me this is the core enabling technology of the next generation of computing. This is a platform shift is the phrase that a bunch of CEOs have used with me. Do you think AI is a platform shift? Do you think it's that big of a deal or is it just another suite of capabilities that AWS will for people?

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  36. A marketing decision and didn't know about a new product that we were delivering over there. And so I try to make sure that as an organization, we've connected those dots and then ask the right sets of questions. And then if, look, if there's a tiebreaker decision, I'll have to do it so that we can move fast. Because I don't, I think the place we don't want to be in is sit there and just debate forever. At some point, you need a tiebreaker decision, and that's kind of what I view my job as doing as well.

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  37. Better or worse, my take is I am rarely, if ever, the expert on any particular subject that we're working on, and whether if we're working on compute, if we're working on storage, if we're talking about hypervisors, if we're talking about sales compensation, if we're talking about power contracts that we're signing, if we're talking about whatever it is across technology, if we're talking about go-to-market efforts, if we're talking about marketing, I am almost never the expert in the room on that. And so I make sure that I listen and leave space for those experts that spend all of their day thinking about that to weigh in as to how they've come up with their recommendation, how they think about what we should do. And then the part that I bring to that is to, one, take a view of a non-expert and ask some questions and understand how they're thinking about the problem, and then also help connect the dots to the other part of the organization that they may not have visibility into and understand if there's trade-offs that they've maybe not thought about because, you know, they're making...

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  38. Part of my job is to make the one way door decisions. I think that framework is a useful one to think about. And just to clarify in case you're not aware of it, it largely is that's how you go fast, is you try to define what are those decisions that are, they can be important decisions, by the way. I think sometimes misunderstood. It's like, what are the important decisions and not important decisions? It's not that. You want the people that are owning those teams at the edges of the organization that really own those products to make important decisions because they know best about their product. But there are also decisions that could be undone if we decide that it wasn't the right thing to do. And then the bigger kind of, you know, I'm going to go invest a billion dollars or some decision. I'm going to launch a new service that is hard to kind of pull back or is painful to pull back. Those are the one way to do decisions that I think we want to have a little bit more inspection on. And even those, though, I think we're trying to figure out how do we make those faster too and enable a broader swath of people to make those. But you asked how I make decisions. You know, I think for.

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  39. Actually, I don't think Is the other big Dakota question it's going to lead us right into AI because I think you have a lot of decisions to make here Amazon famously has the one-way door versus two-way door decision making framework. Everyone kind of applies it differently. Every Amazon executive I've ever talked to, they hold on to that idea and they apply it differently. What's your decision making framework? How do you make decisions?

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  40. Why would a bookseller ever run my computers? And that question we get less and less today, actually. I don't think I've gotten that one for a while. But now we have to deal with scale and you have to think about enterprise requirements and you think about how do I mean auto requirements and how do we support governments and how do we think about scale and how do we make sure that we have enough electricity in the world and all of those kind of questions. But all good problems for us to solve so that we can take them on so that customers don't have to.

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  41. Yeah, exactly. I have the same job now. And I kid, there was a couple other product managers at the time too. But the frustrations now are also similar, different. It's obviously a different scale that we're operating at. But one of the things I was frustrated at back in 2006 was I knew a ton of things that we needed to go deliver for customers. Like I just had a huge list. And it was all about prioritizing that list, but I wish that we could deliver them faster and do more. And even at the scale of AWS's today, that's still true. Like I wish we could do more and do it faster. And that's part of why focus on that organizing principle of how do you make sure that you can get out of the way of the teams to move fast. And so my job today is a little bit more of how do I remove those barriers and help teams move fast. But that's it. I think it's a lot of, we want to make sure that we're innovating. We want to make sure that we're leaning ahead. Some of the challenges we have today are different than we had in 2006. 2006, we had to answer the question of like.

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  42. Respond to customer feedback really quickly. And I think that is the other secret to that, that it's not just an organizing principle, but it is also you teach those teams to really listen to the customer. And truly, I'm sure every leader you have on here says they listen to their customers. And I don't believe that they, like Amazon does a really good job of actually internalizing that all the way down to every individual contributor. And we think about how do we go solve customer problems. And when you're small and you're agile and you can make decisions,

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  43. Like that is the organizing principle we have those small teams and we continue to drive them. So that's it. And then we organize our sales and go-to-market teams and marketing teams kind of separate from that, but from the core product side, that's how we think about it. And it works well for us. I think the positives are, you know, like there's pros and cons to any organizational structure from our side. The pros significantly outweigh the cons from the con side. Sometimes, and you've heard, I'm sure you've heard this criticism and feedback of AWS, which is sometimes it seems like it's not perfectly consistent or, you know, this XYZ feature is not supported across every single service yet or things like that. And that is the downside of that organizational structure is your fit and finish across every single service is not always perfect. And sometimes it takes a little while to catch up to all of those things, which is expected as you're having a thousand different teams kind of run at different paces on different things. But the trade-off is we get to move really fast. We're super agile.

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  44. 200 different services. I'm not going to go through all of them. But that is it, and we'll continue refactor and we think about them. And so from a technology point of view, we think about a compute service, right? You can think about EC2, and then you can think about EC2 networking, and then you can think about how do we make sure that it's optimized around containers. And then all the way down at the bottom, you think about, you know, how do we have teams of 10 to 20 people that are focused on a subcomponent of that that is fully separable? And so we have thousands of developers that are all organized on that principle. Sometimes we'll move them around organizationally, but it's not really the org structure, like the key piece is that really ownership at the bottom. The top part is just how are you efficient on management and how do you make sure that you're managing the teams well and doing that high level coordination bit. And that's actually where you move around. But at the core, those teams are pretty solid. And as you find a new opportunity, you spin up a new team that goes after it and figure out, you know, where does it make the most sense in org structure?

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  45. Every time they were going to go launch a feature to make sure it worked together, we would move really, really slow. But we don't. And so the teams can move really fast. And then we make sure, like we have, it's kind of part of the leadership and the product leadership team to get together and say, okay, we think going after this space is super important. And some of that is customers are going on this use case. And so broadly, we're going to have to go after this thing, but we can still then have the teams go out and run fast. That is an organizing principle that, and then there's other parts of the organization where we have teams that run kind of the data centers and other global, and some of those are separate teams. But if you think about the product and organizing around the product and technology, that's how we think about it.

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  46. Those programs, you continue to refactor, right? That's how you build modern technology systems. And you can think about containers as the current way of doing that, which is really small, independently running systems that can kind of talk to each other through, again, through APIs. Now, if you think about ORIG structure, it's not that dissimilar from that. If you think about how do you have teams that can run really fast, and there is going to be coordination, but what you want to do is minimize that coordination tax as much as possible. And so if you have kind of a well-defined API between them effectively, right, which is like I build a service over here, you build a service over here, we can go innovate. Occasionally our teams will get together and make sure that we broadly know what our vision is, right? We want to know what the thing is that we're running towards. But then I can go and my in particular my service or my organization or whatever it is or my feature can run independently and not have to have a coordination. High level, if the EC2 team and the S3 team had to talk.

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  47. I will say that an arc structure number one it's a living thing so you know whatever I tell you today may not be true tomorrow and and I think you have to be agile there but broadly how we think about structuring our teams I think is is pretty well documented in the industry around Amazon which is we want single threaded teams that can focus on a particular problem and move fast What that means is you really want a team who can own a problem and not be matrixed across 10 different things where they have to coordinate a bunch In some ways I think about it like a computer program where when you have a big monolithic computer program it's very efficient as long as that monolithic computer program is small and as it gets bigger and then you have multiple people working on that program then you get a mainframe and it's very slow and it's you can't iterate on it and you can't move fast and so what you do is you decouple you then build services and they talk to each other through well-defined APIs and then you continue to decouple

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  48. And so I have seen early signs that that is to your question on organization, they're very complementary. It's not A or B. It is kind of all pushing in the same place. And so we'll have to have different capabilities. We'll have to have different motions to help all of that. But I do think that that move of getting your data into a cloud world, it's kind of a necessary condition to have future really, really successful, deeply integrated AI, I think, into your business processes.

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  49. Is working there, and you're co located with where the model can run and all the controls can run, and the guardrails can run, and you can have a rag index that's nearby too that takes advantage of all of that data. That's when you can really start integrating it into your actually production applications. And that's where you're going to see a lot of the wins that are really meaningful, not just kind of like a cool, hey, that's neat that I can have a chatbot, but really integrated into how your workflows change.

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  50. To that cloud migration, because we see with customers forget proof of concepts, like you can run proof of concept anywhere. And I think we've the world has proven over the last couple of years. You can run lots and lots and lots of proof of concepts. But as soon as you start to think about production integrating into your production data, you actually have to have that data in the cloud too so that the models can interact with that data and you can actually have it as part of your system. And I do think that that is going to be a tailwind over the next couple of years as people really want to have these agentic systems. They really want to have their data in a secure environment but integrated into an AI workflow. And you can't orchestrate an AI workflow pointing it out of mainframe. It's not going to be possible. And if you have the data going back and forth to some model, the security and control of making sure that that IP stays with you is risky too. But if you move the whole data into a secure cloud environment, then you have a modern data lake that has all your data, your application.

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