Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world’s needs across inference and training as Scott Guthrie shared with Alex Kantrowitz.
And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.
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Transcript
Transcript
Transcript
I think it’s, it’s we, one of the things that we do when we add new data center capacity or AI infrastructure is, you know, making sure that that we can use this infrastructure for a variety of different AI use cases. I think one of the things that’s really going to differentiate AI infrastructure companies in the future is that ability to kind of maximize yield on the infrastructure. Like how are you driving down the cost of, you know, tokens per Watt per dollar? And you know, part of what makes the Microsoft portfolio so unique is the fact that we have a lot of our own AI products, Microsoft 365 Copilot, GitHub Copilot, the work that we’re doing with Nuance and Dragon and healthcare. We’ve got the world’s largest consumer application with Chachi PT that runs on top of Azure. And we have thousands, hundreds of thousands and millions of businesses. That are also building their own AI applications on top of us. And so as we think about like what market are we going to build a new data center? Is it for training? Is it for inferencing? And you know, how do we make sure that that infrastructure is going to be maximally used? You know, we we feed in kind of each of these different customer scenarios into our calculus. And there are certain tranches of capacity that we’re happy to build out because we can see very clear line of sight in terms of how we’re going to maximize the usage and the revenue from it. And there’s others that were maybe less likely to see the immediate or that the ROI that we’d like.
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