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AWS CEO Matt Garman joins us now on Bloomberg Television and Bloomberg Radio. Matt, it's great to have you back on the show. You know, this is the fastest growth for AWS in nearly 5 years. But to kind of capture a moment in time, could we start by talking about how much of that was driven by the frontier labs, the big ones, OpenAI, Anthropic, or how much of it was something broader across the enterprise, something bigger in AI?
Yeah, it's actually across the board this growth that we're seeing right now, and much of that is some of the AI labs that are building their models on top of AWS, but actually a lot of that growth is spread across all of our startup and enterprise customers where AI is really impacting almost every single industry. And so whether it's financial services companies or healthcare companies or retail companies or media companies, really that growth is coming across the board as companies of all different sizes and all different industries are looking to use AI to grow their business. And so we're seeing a lot of growth from the top, frontier labs, but for AWS, we're not, like some others, maybe as concentrated on just one or two large customers, but it's actually growth from a really broad set of customers, which is nice to see.
Matt, when AWS says the AI business has a revenue run rate of $25 billion, what does that mean? What does the figure encompass?
Yeah, that includes both training from very large companies like Anthropic and OpenAI and other large tech companies as well as many startups, but it also, and a big chunk of that, is really inference that that broad swath of companies are doing. And so as companies think about how they take models in something in Amazon Bedrock and run those models to get value out of their business, some of them are automating processes, many of them are running agentic workloads that they build on AWS to further drive efficiencies in their business or deliver new customer experiences. We consider all of that work, whether it's agent growth, whether it's inference, and some of that is training new models. All of that encompasses the AI business for us.
I think I've asked you this question at various points in time since you became AWS CEO, but is there a percentage split right now, training versus inference, that you can give me?
Yeah, you do, and it actually keeps shifting. I would say more and more it keeps shifting more towards inference. I think we still see large training clusters being used by a number of companies, but as these models get really popular and really powerful, more and more companies are integrating that inference into their workloads. So, you know, I don't know the exact percentage today, but it keeps shifting more and more towards inference, and we expect that to continue as the economics make sense, where you really want most of that cost and spend being where value is being created for end customers.
Amazon's overall capex number for this year is very big, $220 billion. So it's up 20 billion, and Andy Jassy explained that's mostly AI, but it also accounts for higher memory pricing, right? Capex higher because the cost environment is higher. But from AWS's perspective, what's the trajectory for next year? You expect that capex will be bigger still, because I think one of the things Amazon is quite clear about is that even at $220 billion, it might not be enough to meet current demand.
Yeah. One of the things that we're quite excited about is just the potential business for us is just massive, and we see this as a huge opportunity for us to really invest and help customers take advantage of the AI opportunity. And so we will keep investing. We think that there's a big opportunity for us and for customers. And as Andy kind of called out last week, we have really great insight into what that demand is going to be from customers. And so when we invest, and we will keep investing in capex next year as well, we have great insight into when that revenue is going to land. And so it's pretty well known for us, and as Andy mentioned, much of our capacity has already spoken through through the end of '27 and even through much of '28. And so as we're investing, we're getting 5-year commitments from customers. We're getting these long-term commitments. And so we're out there making investments to keep being able to grow the business and try to meet customer demand, but as you say, today demand still significantly outstrips supply and we're trying to build and invest to keep up with what customers are asking for.
And so we expect the capex number will be bigger next year than it is this year.
I expect us to keep investing as we see opportunities, and some of that we'll see how the market continues to grow. But right now we're excited about our investments and we'll continue to invest.
The chip business has also got a lot of momentum. You know, Trainium for some time has been a big part of the strategy and we've discussed that. I've been able to go to Austin, spend a lot of time in Annapurna Labs to look at the server design. Let's start by asking, when you say the chip business has a revenue run rate of $25 billion, what does that mean? Is that the business of selling the chips to third parties or renting out capacity based on the chips?
Yeah, renting out capacity based on those chips. So today we run all of our own chips inside of the AWS cloud, and that includes both Trainium chips as well as Graviton chips, which are general purpose processors. Graviton is incredibly popular. We've been building Graviton for many years now. And in fact, almost all of our large customers use Graviton as some part of their deployments. And it's a key part of how we help customers deliver extra value by lowering their costs. Trainium is wildly popular, and as we've mentioned a couple of times now, we're largely sold out through the end of next year for Trainium capacity, and it's really really popular. We're starting to see both large customers as well as startups really love the cost benefits of running on Trainium, and having that choice in a cloud where you can run on purpose-built processors like Trainium that give you really great AI performance together with Nvidia chips for when you need some of that processing for GPUs as well gives customers the right mix that they're oftentimes looking for. And so we find that combination to be quite powerful, and our Trainium 3 chips are really really popular with customers right now.
We're live on Bloomberg Television and Bloomberg Radio. This is Bloomberg Tech in conversation with Matt Garman, the CEO of AWS. Explain the difference in economics between capacity based on Trainium and capacity based on Blackwell, for example. What is the selling point of going with Amazon silicon?
