In this interview during theCUBE's coverage of AWS re:Invent, Matt Garman, chief executive officer of AWS, sits down with theCUBE’s John Furrier to outline the company's aggressive strategy for scaling AI infrastructure to meet the demands of the "AI factory" era. Garman describes a shift where the "campus is the new computer," highlighting Project Rainier – a collaboration with Anthropic involving 500,000 Trainium 2 chips in a single location. The conversation dives into the debut of Trainium 3, described as the world’s best inference platform with a 4x compute increase over its predecessor, and teases Trainium 4, which promises another 8x leap in performance. Garman notes that over 50% of tokens served through Amazon Bedrock are already running on Trainium, underscoring the rapid adoption of AWS custom silicon.
Garman also details how AWS is moving beyond generic models to enable true enterprise differentiation through Nova Forge and "frontier agents." He explains that Nova Forge allows companies to inject proprietary data earlier in the training process to create secure, custom frontier models within their own VPCs. The discussion further explores the rise of autonomous agents capable of executing long-running tasks and remembering developer preferences to "force multiply" engineering teams. Garman argues that despite interest in the edge, the power and compute constraints of modern AI are driving workloads back toward the cloud, a trend supported by AWS landing 3.8 gigawatts of new data center power in the last 12 months alone.
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
AWS re:Invent 2025. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Register for AWS re:Invent 2025
Please fill out the information below. You will receive an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for AWS re:Invent 2025.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
AWS re:Invent 2025. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Sign in to gain access to AWS re:Invent 2025
Please sign in with LinkedIn to continue to AWS re:Invent 2025. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Matt Garman, AWS
In this keynote analysis from AWS re:Invent 2025, theCUBE’s John Furrier joins analysts Paul Nashawaty, Zeus Kerravala and Sarbjeet Johal to unpack how Amazon is redefining cloud infrastructure through the lens of agentic AI. The panel breaks down Matt Garman’s declaration that "agents are the new cloud," exploring key announcements surrounding the Nova model family, AgentCore and Amazon Bedrock. The discussion highlights AWS’ strategic pivot from merely abstracting infrastructure complexity to abstracting work itself, effectively bridging the gap between professional coders and "citizen developers" while unifying the experience for builders at every level.
The conversation digs deeper into the practical realities of enterprise AI adoption, emphasizing the critical role of security, governance and compliance in moving from proof-of-concept to production. Kerravala, Johal and Nashawaty analyze AWS’ vertically integrated approach – spanning from custom silicon like Trainium and Inferentia to the application layer – and how this full-stack strategy allows customers to train models on proprietary data with improved price-performance. The group also debates the evolving competitive landscape, noting how AWS is equipping organizations to build autonomous, long-running agents that function as teammates rather than just tools.
In this interview during theCUBE's coverage of AWS re:Invent, Matt Garman, chief executive officer of AWS, sits down with theCUBE’s John Furrier to outline the company's aggressive strategy for scaling AI infrastructure to meet the demands of the "AI factory" era. Garman describes a shift where the "campus is the new computer," highlighting Project Rainier – a collaboration with Anthropic involving 500,000 Trainium 2 chips in a single location. The conversation dives into the debut of Trainium 3, described as the world’s best inference platform with a 4x comp...Read more
exploreKeep Exploring
What is the current vision on how AI infrastructure is evolving in terms of large scale and accelerated computing systems?add
What is the current evolution and future outlook of cloud infrastructure?add
What is the perspective on customer behavior regarding technology use in an AWS cloud, and what investments are being made to enhance performance and customer choice?add
What is Tranium 3 and how is it significant in the context of AI systems?add
What is an AI factory and how does it involve the integration of AI into physical and digital workflows?add
What are the factors influencing the growth and value of AI for customers using AWS?add
>> Hello, I'm John Furrier with theCUBE. We are here in Seattle for the exclusive interview around Reinvent Preview and all the news coming out. So we had Matt Garman, the CEO of AWS. Welcome back to theCUBE. Thanks for inviting me back this great studio here.
Matt Garman
>> Yeah, actually thank you. Thank you for coming to Seattle. Welcome.
John Furrier
>> So on the keynote announcement, there's a lot going on with AI infrastructure. You guys are leading in the CapEx. There's a big sovereign conversation with global ecosystem, a lot of national conversations. But AI factory seems to be dominating the conversation and all the discussions about what's powering it. And I was saying on LinkedIn and X, AI factories is like the cloud. It's got a similar thing to it. So a lot of the trends are pointing to large scale compute, accelerated computing, XPUs. This is the hottest area right now in build out of, I call AI factory areas, not just data centers, they're like full-blown systems. What's your vision on how the infrastructure is changing and evolving?
Matt Garman
>> Yeah, I think, look, it's a fun time to be in the cloud for sure. And I agree there's always kind of a new name for what the cloud or things are, but we are seeing that particularly for the largest customers building some of these AI systems, they are needing massive, massive areas of compute. We've talked about our project Rainier, which is together with Anthropic, 500,000 Tranium two chips all in basically a same data center campus. And we used to say some phrases like the rack is the new computer and the data center is the new computer. Now we kind of say the campus is the new computer because it really is... These are massive compute installations and for the very largest customers. But I think the cool part about that is the innovation that comes back to every customer out there who's interested in going and building models. And not every customer is going to go spend tens of billions of dollars to go build a frontier model, but every customer wants to be able to take advantage of AI. Every customer wants to be able to go and build. And some of the announcements that we have at Reinvent are really targeted at how do we help customers be able to go get that value out of all of this spend that's going in there. And as you mentioned, AWS is spending a large amount of capital to go and build out data centers all around the world to land compute infrastructure, to land network infrastructure, to land storage infrastructure, so that customers can keep seamlessly scaling. And we're landing different new types of compute, we're landing new types of clusters, and we're landing new services to help them take advantage of all this technology that's coming out.
John Furrier
>> One of the things in following you guys since the beginning is just the growth of just how you guys grew, but now as this next level hits, the conversations keep coming back to the chips, the silicon. Is there any economics that have changed? Because your core competency that you guys have built has been building large scale systems. We've been covering the new region expansions, now you've got big data centers, you've done multiple deals where you're going out and building more AWS data centers. That's a core competency. Is that going to be a service? And how does that service look? Because it's almost like reminds... I won't say outpost, but remember the outpost was like, okay, we'll put it at the edge, we'll put AWS out there. These large data centers that you're building have a distributed component to them. And with AI, tie that together because these are essentially you're building out the infrastructure and how do you expand that out when the software stacks are changing? Energy is bounding everything. You've got software stacks around the agentic coming.
Matt Garman
>> Yeah. Look, I think that there's going to be a small number of customers who want to have these dedicated AI factories, as we call them, where they can go and we'll put large amounts of compute next to where they're going to be. These are going to be sovereign nations. These are going to be US governments and secret agencies. These are going to be really, really, really large customers. However, I think that the vast majority of customers are going to continue to take advantage of this technology inside of an AWS cloud. They're going to go to US East or they're going to go to EU West or wherever their region is and take advantage of these technologies there. And for us, that investment in silicon is one of the things that it can be invisible to customers under the covers, but what our job is to deliver better performance at lower costs and at better latencies so that customers can keep getting value in their applications. And I think sometimes that gets lost. And it's why we invest in our own custom silicon because we think customers want a choice. We think customers are going to want to use the best infrastructure for the workloads that they have. And at Reinvent, we announced the GA of Tranium 3, really excited about Tranium 3. It's the most powerful AI system available out there today. And as we've talked about before, we're kind of bad at naming, but it turns out that Tranium 3 is the world's best inference platform. And so these are no longer chips, they're no longer servers. A Tranium 3 Ultra server is actually two racks of computers that are all kind of networked together to give you this really, really large instance that is a super powerful AI system. And that is incredibly exciting and gives great differentiated performance for customers and we're excited about that roadmap. We think Tranium 3 is going to be a blowout success and is today more than 50% of the tokens that are served through Bedrock are already running on Tranium. So Tranium already runs more than half of our tokens all going through Bedrock. And we think Tranium 3 is a real step change function in what's going to be possible there. We also pre-announced that we're already a hard at work at Tranium 4, which you might expect, but we announced that to the audience today and super excited about that as well. I think Tranium 3 is about a 4X increase in the amount of compute compared to Tranium 2. Tranium 4 is like an 8X jump again in the amount of compute that's available. It's a further jump in memory bandwidth so that you can really kind of streamline the throughput of inference through these platforms and training through these platforms. Really excited about that. The team has been just incredible at turning that innovation into value for our customers. And that's what I'm most excited about.
John Furrier
>> You mentioned tokens, pumping out a lot of tokens. One of the things when I ask people on the queue, "Hey, what's an AI factory?" It's quite the buzz where it's become a thing. And it's really more than a data center. It's really a lot more. And some answers are data in, tokens out. Some say data in, intelligence out. So the theme is bringing AI into physical things, whether it's workflows, digitally, or physical AI. So the question is that as you see Amazon bringing AI to things because the way the models are ruling, you need all that horsepower. What's under the hood? Can you explain, because everyone thinks, oh, Nvidia is doing it alone. And they're not. They work with you guys very closely. So talk about what you guys are building that's going to deliver that token value.
Matt Garman
>> Yeah.
John Furrier
>> You're doing a lot internally with Anthropic and you're publicly talking about it. So everyone wants more tokens.
Matt Garman
>> Yeah. Well, and it's not just tokens. And I think the key thing, and actually, if you take a step back, it's fun to talk about like an AI factory. 99.999% of customers will never purchase an AI factory. They'd really just want to use the cloud in their environment and build inference into their applications. And so it's a fun thing to talk about, but most customers are not going to purchase that. What they're going to buy though is what they... And actually when you get down and you talk to an enterprise, what they realize is their differentiated value is data, right? It's the IP that they have inside of their company. It's what makes insurance company A different from insurance company B or bank A different from bank B or retailer A different from retailer B. They have this data and it's about their workflows. It's about their customers. It's about the IP that goes into their business. And when I talk to a lot of customers, they say, "Look, AI is awesome." It's like incredible models that are enabling content summarization. They're enabling workflows. They're enabling, pulling together a bunch of information. And this is an interesting kind of observation is they say, "Look, I wish I had a model that deeply understood my company. It deeply understood all of my data and everything like that." Now, obviously all of the model companies out there in the world don't have access to this private set of data. And this is the tricky part. And so a lot of companies are like, "I'd love to train my own data." Because then those tokens that are outputted would know me. They're not generic tokens. They're tokens that know my company, but I don't want to spend $10 million to go build, and I don't have the skills to build a frontier model. So we actually sat back and said, how do we get the customers get more value out of those tokens? How do they get more value for their enterprise? It's not just tokens, right? So generic tokens are actually useless. They're just bits of data if they're not useful and integrated into your company. And so part of the announcement we had at Reinvent was around our new set of Nova models, which are fantastic. They're really, really great and a leap forward in what's possible, particularly from a price performance, a latency perspective and capabilities, really impressive with regards to Nova 2 Light and Nova 2 Pro. Very excited about those. What we also announced though is this program called Nova Forge. And Nova Forge, what it does is it introduces the first ever idea of open training models. And so there's open weights models today where people can kind of take a completed model and then do a bunch of post training on it and play with the weights and try to customize their model. What we saw is what happens is those models actually forget all of the knowledge they got originally, right? And so you actually, customers, they try and they get somewhat better results, but can't really customize it. What we're doing with Nova and Nova Forge is we're allowing customers to insert their proprietary data earlier in the training process. And then we're having a proprietary set of Amazon curated data that we will mix with that data to kind of finish training the model. So to the end of the day, the customer has effectively a privately built frontier model that deeply knows their enterprise data and is at the frontier of capability. And then they have that for their own use. And then you get back to now the tokens are super valuable to them and they know their company, they deliver results and we're very excited about this.
John Furrier
>> This is a huge deal, I think, because one of the things, you look at the models when they first came out, the frontier standard was defined by large scale, multi-step reasoning, persistence, memory and all those things. Unpack that because what this means is you're essentially... It's almost like an AI development kit if you think about it in a kind of weird way. But talk about the impact of that because the question I would have is, okay, is that secure on my premise? Is that going to be on the cloud and does other people see the data or you're going to give me your Nova. It's kind of half-baked and I integrate my stuff in and then I have a persistent brain basically.
Matt Garman
>> That's right. So the idea that we have is that every single startup and enterprise can have their own custom frontier model and it's only for them. And so once you take your data through a bunch of tools and recipes that we make available, mix it together with the proprietary kind of curated Amazon data set of all the things that we've put together so that the model knows how to do reasoning and knows how to do intelligence, et cetera, but together with your data and then it's secure inside of your VPC. No one has access to it. None of that data goes back to the Nova team. All of that data and your own version of your model stays inside of your VPC. And so you know it's secure, you can run it from there, you can upload it to Bedrock and run it in a serverless way so you can actually get all of the benefits of Bedrock running this model serviceably, but no one else has access to it, including us. And this is why customers for the last 20 years have trusted Amazon to keep their data safe.
John Furrier
>> Talk about what the alternative, I mean, because what you're kind of getting at is that the heavy lift to build the frontier model is a lot of work. Costs a lot of money and you're going to open that up and democratize that and enable that. And on the other hand, I could see a significant performance enhancement because there's a lot of kind of knowledge in the data. Does that fill up on the performance side too?
Matt Garman
>> Hugely. So we have a couple of really great beta customers who've been working with us, take Reddit as an example. They were super interested in this idea where they could infuse their data into Nova so that it really deeply understood what they think about and what values the Reddit community has and how that tone should be. They've seen much, much, much better results than any commercial model or anything that they've seen out on the market
John Furrier
>> So you're basically saying you think AI FX will be these large scale data centers, whether it's in the cloud or servicing kind of cloud architecture and that the enterprises will move to these highly efficient frontier models that'll embed their data estate into it.
Matt Garman
>> That's right. Exactly it.
John Furrier
>> And what does that mean? Give an example of what that would look like because this could change the economics of the enterprise.
Matt Garman
>> Yeah. So I'll use this exact example where somebody like Reddit was trying to struggling with, how do I get these models to deeply understand my own data? Instead, what they did is they took a checkpoint of a... And we basically said, look, we can train one of these large frontier models one time and then let everybody else kind of ride on top of that and benefit from that capability. And so what we do is we take a checkpoint, allow them to mix their data in, our data gets mixed in. Now they have a copy of the model for a very, very small fraction of the cost of training their own model. And they have this custom model that they can use for however they want. They can use that model to distill smaller models. They can use that model to train their own kind of internal workflows. They can use that model to go build interesting experiences for customers. We've talked to banks, we've talked to manufacturing companies, we've talked to pharmaceutical companies. Everybody is quite excited about this and I think this is really what people actually want to do.
John Furrier
>> I think it was two years ago I asked you here in this room if you would work with OpenAI at that time, they were not under contract with you guys, but now you all have worked with them. We also talked about how models will work with each other. So what you're kind of getting at now is if I'm an enterprise, I get my model, the frontier capability, I then embed my data estate in there, all my intellectual property, proprietary data, and now I can then use other models in Bedrock or other places.
Matt Garman
>> Well, you can always use other models, but you can use your own kind of custom model in conjunction with those other models. So of course there's going to be lots of use cases where you're still going to want to use Claude Sonnet model as an example. Or there's other use cases where you're going to want to use open source models, whether they come from OpenAI or they come from Quinn or they come from Llama or from Meta. You're going to want to combine a bunch of these things together for the best. Some of them will be cheaper and more performant on certain use cases, but you have your own custom model for the workflows that really matter for your data and your kind of core line of business applications. And we think it's going to be a really great capability for a large set of customers.
John Furrier
>> Well, give you props, Matt. You got it right on the future two years ago, what we talked about actually happened. I want to ask you on that thought, okay, I love this direction, but now we're talking about distributed computing at the edge. If I walk into a retail outlet, I got wireless systems, they're connected to the internet using cloud services. You've got this idea of a hyper convergence with wireless. You guys have Prometheus, a lot of work going on there. There's a lot of telecom activity happening. What does the AI factory cloud vision look like when you say, how do I bring all those smart models across the network of a company because I might want to have an edge computer vision and have custom models come to the edge? Because it's a hybrid distributed environment. What's your vision on how this progresses? Because you're going to see models come out faster now. I want to train at the edge too.
Matt Garman
>> Yeah. Look, I think there's a lot of interesting kind of unsolved problems there at the edge. I think frankly, I kind of differ a little bit of where I think the world is going right now. What I'll tell you is I think, and the more I see this, I see actually things moving more towards the cloud as opposed to away from the edge. And kind of how I think about it is because the edge is compute constrained, because the edge is power constrained, you don't actually want to use all the models there. You're not going to have access to the data at the edge. You're going to have little bits of inference. You may have your Alexa devices be able to kind of recognize wake words, but there's so many things you're going to want those models to do, you're not going to put them at the edge. And so the edge inference is like a small amount of little inference there where you want that to be smart, you want that to be small. But the vast majority of that hybrid world is actually trending towards the cloud because the models are bigger, the data set is bigger, the knowledge that you want to get out of those is bigger. And so that's actually a trend I see, which is not things moving out to the edge. I think it's more-
John Furrier
>> So it translate into the Amazon speak of cloud early days. It's like an API call in your mind. Is that how you see it? Because it is calling a model.
Matt Garman
>> That's right.
John Furrier
>> And our agents though.
Matt Garman
>> And that's exactly right. And so in the API call world, and I'll just use the Alexa device as an example, the Wake word kind of is a local kind of AI engine that can recognize that you said Alexa. And then when you ask it, "Hey, summarize this or do a conversation." It sends API requests effectively back to the cloud where these smart sets of a bunch of different models can answer questions, respond to you, have a conversation, summarize data, et cetera, make recommendations.
John Furrier
>> Let's translate to the agents because now with now the new AI native infrastructure, when you bring all this AI capabilities, agents will be doing a lot of work.
Matt Garman
>> Absolutely.
John Furrier
>> This has been a big part of Reinvent on your keynote around Gentech and bringing that frontier capabilities to agents who are working. They're workers at this point. So they're not API calls. It's much more complex.
Matt Garman
>> That's right.
John Furrier
>> What's your vision and how is this playing out and why the focus so deeply on this right now?
Matt Garman
>> Yeah. Look, I think that the promise of AI has been enormous over the last couple of years and many companies have gotten a lot of value from it, but I think it's like 10 to 20% of the value that they're going to get. And the rest of that value does come from and is kind of be unlocked by agents. And we have this view that billions of agents are going to be built out there in the world. And we're building a lot of tools and announced many more at Reinvent around Agent Core where we kind of give people a scalable, robust platform to go build their own agents. We also introduced this idea of frontier agents, which are these... They're autonomous agents that can work for many hours, if not days on their own and can scale out to accomplish lots and lots of tasks or a large set of tasks. And so we're super excited. Now as you know, Kero is our development IDE and CLI that has really caught fire. Customers are quite excited about this and largely because it pioneered this idea of agentic development, not just code completion and vibe coding, which it supports as well, but it really is this thought of how do you get agents to go do work on your behalf? And so we launched the Kero agent, the Frontier agent as part of the announcements at Reinvent. And the idea there is that you actually have this agent that can go do long-running tasks for you. You can say, with a set of instructions, kind of think about it like a junior developer. I want you to go work on that. And then you can have multiple of those agents that are going to work. And so now you think about how are you really force multiplying a fantastic engineer to deliver 10X the amount of capability that they or 20X the amount of capability they had before. And so these frontier agents we think are going to be transformative to development teams where the limiter to delivering value to customers has often been do we have enough software development hours? It's always been, we have a huge list of things that we would like to get to the customer's hands and just don't have enough capabilities to do. If we can unblock development teams to no longer have that be the constraint, but they're just brainstorming about what is the next good idea and that's the constraint, it unlocks an enormous amount of possibilities to deliver huge value.
John Furrier
>> What's some of the things that we're going to see with frontiers because on the frontier models, we saw, okay, some cool chatbots and then it became, wow, this is moving very, very fast. How do you see that progression value because memory, remembering things, having the Nova capabilities or integrating with other models, I see an interplay between the agents. What's that longevity play look like?
Matt Garman
>> Yeah. And it's a great question. I think if you look at where the evolution of kind of coding has gone, the first set of kind of coding tools, the cursors and things of the world were like you would type in words and it would project, like it would guess what you were going to write next. And you could do auto-completion and then you could do this vibe coding thing or you could kind of write words more quickly. As we switch to agents, it's a little bit more like you're directing as opposed to you're writing the code, right? So think about it more like you're directing a bunch of agents. I want you to go write this function. I want you to go accomplish this task. With frontier agents, it's even more abstract than that. It's work where I want you to go solve this problem. And so it may think a bunch of different ways. It may reason, it may work for hours and hours trying different things, testing different ideas, exploring different ways to accomplish something, trying it again. And that is the interesting thing where it may then come back the next day and say, "This is what I came up with. " Now the cool thing about it is they learn over time. And so you have this memory where you say, "Ooh, I like what you did there." And you as the developers still have to be an expert. You have to look at the code they came up with really kind of reason with them. But then you say, "You know what? Our team uses snake case when we're writing code. So next time, can you please do that?" Next time it goes, "Great, I know that now." And that's a simple case, but we also say, "Our team also likes to write unit tests in this different way. If you can make sure you do that next time, that'll make this easier."
And all of a sudden, you're three months, six months in. These frontier agents are acting like they are part of your team like they know exactly what you do. They know where your code repositories are. They know how you like to write code and how you like to integrate it and how you like these things factored. And they allow you to kind of really force multiply things. So I think it's a-
John Furrier
>> This is why I like the physical AI, not from a data center standpoint, but in the real world when you bring AI to the human, it's what's your preferences?
Matt Garman
>> That's right.
John Furrier
>> I like this or this way. And how hard was that to do? Because again, I'm trying to scope where that frontier agent means from a game changing, needle moving standpoint, compare it to where it was, how game changing is that?
Matt Garman
>> It's completely different. And it's going to take teams thinking differently about how they develop code and how they operate code. We also launched frontier agents around cloud operations, as well as around security and pen testing. And so we're going to need teams to think differently about how they develop code, how they build products, et cetera. And so completely different there. It is definitely a step change function in what's possible for sure, and we're at the early stages of what it can be. And so I think it's not perfect today. It's not going to be... There are going to be more innovations, there's going to be more advancements, but we're very excited. We think this path on frontier agent, just like models get better with every generation, these frontier agents will continue to get better and better as we evolve, but they really are a step change in what's possible.
John Furrier
>> We talked about silicon infrastructure, AI research agents and models are all changing. How would you compare this to the original value proposition of AWS? Because I remember when we talked about this many times, you eliminated a lot of things from the IT world. If you were a startup or a big company, you'd have an IT department, you have to buy servers, and so you go to the cloud. How does this change that? Because there's a lot of similarities in the sense of some things go away, the heavy lifting or the toil, whatever you want to call it. What's happening there? What does the infrastructure need to be to enable the value that you provide and how do customers see that? How do you share... What's the vision on that? Because I can see this changing the economics on the work, the future of work, the products will change. You'll see AI native coding around the model capabilities. What's your vision on how that ties together for the enablement?
Matt Garman
>> Yeah. Well, you had a couple of questions there. I'll try to unpack them. I think the first is... We continue to think about the AWS business just like we always have, which is how do we think about muck or toil that customers have that is not differentiating, that we can help them take on and make easier for them. I think AgentCore... And then how do we build them really flexible building blocks so that they're not kind of stovepiped into a very bespoke way of doing it. And it's like, you can do anything you want as long as it's my way. That's not how we thought about AWS. We thought about it as we want you to have a canvas and a bunch of tools to be able to accomplish what you want to accomplish. We're taking the AI and agent world of view much the same way. If you think about AgentCore as an example, it's exactly what we've done with AWS for how do you go build agents. It's how do you have... You can have a secure compute environment and you can use that together or not with our capability that allows you to have memory. And you can use all of those together or not with our agent observability or not. Or you can just use agent observability, right? You can use all of these things and you can pull in. If you want to use a Google Gemini model, you can. No problem. If you want it, but we have a bunch of great integrated models into Bedrock that you want to use Anthropic or you want to use Nova. Awesome. If you want to use external frameworks, great. If you want to use the Amazon Strands agent, it works great. We kind of have this view that every customer is going to want to build these things differently. And our job is to make sure that they scale great, that they're secure, that we make it so it's easy for you to go and get your idea out to customers fast, secure, cost effectively, and in a great way.
John Furrier
>> It's almost like AI, higher level services coming in similar pattern.
Matt Garman
>> That's exactly right.
John Furrier
>> Final question, AI investments, you guys are still going full throttle, the demand is there, you got CapEx build out continuing to go. How's the demand on that? Seems to be high.
Matt Garman
>> Well, look, we're building data centers and landing servers as fast as we can. And I would say up and down the stack, I would say from Graviton to Tranium to X86 to Nvidia for storage to databases, frankly to power, right? We've landed 3.8 gigawatts of new data center power in the last 12 months alone and we're looking for ways to go faster. The demand out there is incredible. It's a CapEx intensive business, as you mentioned, but that's kind of the job that we've taken on to help our customers scale.
John Furrier
>> You drove the wave transition, big transition with the cloud. Now we have AI. What's your thoughts on the whole AI bubble that didn't pop last week a couple weeks ago? So you're seeing the earnings continue to do well on all the suppliers. You guys are doing well. Just for the people that are learning about what this new wave is, because it really is a whole nother step function.
Matt Garman
>> Yeah. Look, I think people talk about an AI bubble because it's just growing so fast and valuations, I can't speak to what the stock market decides is valued or not. What I will tell you though is our customers are getting a lot of value and our customers are driving value for their businesses. And when they see ROI, they're not going to stop buying from us. And so all I can speak of is that they're getting real ROI to their businesses. Their businesses are growing because of their use of AI from AWS. And so I have very little concern that that's not going to keep growing because we're still at the early stages. And for us, if we keep making customers happy, building innovative new services and really leaning in to help them lower costs, I feel very confident about the future.
John Furrier
>> And the open Nova models and the foundation, frontier agent, I should say, is really going to enable that.
Matt Garman
>> Absolutely.
John Furrier
>> Matt, thanks so much for taking the time. Really appreciate it.
Matt Garman
>> Yeah. Thanks, John.
John Furrier
>> I'm John Furrier for theCUBE here in Seattle for the scoop and Reinvent preview and breaking down the keynote analysis with Matt Garman, CEO of Amazon. Thanks for watching.
John Furrier