Satya Nadella: belief

Transcript context

…There’s a question going forward. Right now, we have models that have this distinction between training and inference. One could argue that there’s a smaller and smaller difference between the different models. Going forward, if you’re really expecting something like human-level intelligence, humans learn on the job. If you think about your last 30 years, what makes Satya tokens so valuable? It’s the last 30 years of wisdom and experience you’ve gained in Microsoft. We will eventually have models, if they get to human level, which will have this ability to continuously learn on the job. That will drive so much value to the model company that is ahead, at least in my view, because you have copies of one model broadly deployed through the economy learning how to do every single job. And unlike humans, they can amalgamate their learnings to that model. So there’s this sort of continuous learning exponential feedback loop, which almost looks like a sort of intelligence explosion. If that happens and Microsoft isn’t the leading model company by that time… You’re saying that well, we substitute one model for another, et cetera. Doesn’t that then matter less? Because it’s like this one model knows how to do every single job in the economy, the others in the long tail don’t. Your point, if there’s one model that is the only model that’s most broadly deployed in the world and it sees all the data and it does continuous learning, that’s game set match and you shut shop. The reality that at least I see is that in the world today, for all the dominance of any one model, that is not the case. Take coding, there are multiple models. In fact, everyday it’s less the case. There is not one model that is getting deployed broadly. There are multiple models that are getting deployed. It’s like databases. It’s always the thing, “Can one database be the one that is just used everywhere?” Except it’s not. There are multiple types of databases that are getting deployed for different use cases. I think that there are going to be some network effects of continual learning—I call it data liquidity—that any one model has. Is it going to happen in all domains? I don’t think so. Is it going to happen in all geos? I don’t think so. Is it going to happen in all segments? I don’t think so. It’ll happen in all categories at the same time? I don’t think so. So therefore I feel like the design space is so large that there’s plenty of opportunity. But your fundamental point is having a capability which is at the infrastructure layer, model layer, and at the scaffolding layer, and then being able to compose these things not just as a vertical stack, but to be able to compose each thing for what its purpose is. You can’t build an infrastructure that’s optimized for one model. If you do that, what if you fall behind? In fact, all the infrastructure you built will be a waste. You kind of need to build an infrastructure that’s capable of supporting multiple families and lineages of models. Otherwise the capital you put in, which is optimized for one model architecture, means you’re one tweak away, some MoE-like breakthrough that happens, and your entire network topology goes out of the window. That’s a scary thing. Therefore you kind of want the infrastructure to support whatever may come in your own model family and other model families. You’ve got to be open. If you’re serious about the hyperscale business, you’ve got to be serious about that. If you’re serious about being a model company, you have to basically say, “What are the ways people can do things on top of the model so that I can have an ISV ecosystem?” Unless I’m thinking I’ll own every category, that just can’t be that. Then you won’t have an API business and that, by definition, will mean you’ll never be a platform company that’s successfully deployed everywhere. Therefore the industry structure is such that it will really force people to specialize. In that specialization, a company like Microsoft should compete in each layer by its merits, but not think that this is all about the road to game set match, where I just compose vertically all these layers. That just doesn’t happen. So last year Microsoft was on path to be the largest infrastructure provider by far. You were the earliest in 2023, so you went out there, you acquired all the resources in terms of leasing data centers, starting construction, securing power, everything. You guys were on pace to beat Amazon in 2026 or 2027. Certainly by 2028 you were going to beat them. Since then, let’s call it, in the second half of last year, Microsoft did this big pause, where they let go of a bunch of leasing sites that they were going to take, which then Google, Meta, Amazon in some cases, Oracle, took these sites. We’re sitting in one of the largest data centers in the world, so obviously it’s not everything, you guys are expanding like crazy. But there are sites that you just stopped working on. Why did you do this?…

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