Dwarkesh Patel: commitment

Transcript context

…At the end of the day, we’re going to build a world-class team and we already have a world-class team that’s beginning to be assembled. We have Mustafa coming in, we have Karen. We have Amar Subramanya who did a lot of the post-training at Gemini 2.5 who’s at Microsoft. Nando, who did a lot of the multimedia work at DeepMind, is there. We’re going to build a world-class team. In fact, later this week even, Mustafa will publish something with a little more clarity on what our lab is going to go do. The thing that I want the world to know, perhaps, is that we are going to build the infrastructure that will support multiple models. Because from a hyperscale perspective, we want to build the most scaled infrastructure fleet that’s capable of supporting all the models the world needs, whether it’s from open source or obviously from OpenAI and others. That’s one job. Secondly, in our own model capability, we will absolutely use the OpenAI model in our products and we’ll start building our own model. And we may—like in GitHub Copilot where Anthropic is used—even have other frontier models that are going to be wrapped into our products, as well. I think that’s how each time… At the end of the day, the eval of the product as it meets a particular task or a job is what matters. We’ll start back from there into the vertical integration needed, knowing that as long as you’re serving the market well with the product, you can always cost-optimize. 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.…

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