Satya Nadella: commitment

Transcript context

…Say we roll forward seven years, you no longer have access to OpenAI models. What does Microsoft do to make sure they are leading, or have a leading AI lab? Today, OpenAI has developed many of the breakthroughs, whether it be scaling or reasoning. Or Google’s developed all the breakthroughs like transformers. But it is also a big talent game. You’ve seen Meta spend north of $20 billion on talent. You’ve seen Anthropic poach the entire Blueshift reasoning team from Google last year. You’ve seen Meta poach a large reasoning and post-training team from Google more recently. These sorts of talent wars are very capital intensive. Arguably, if you’re spending $100 billion on infrastructure, you should also spend X amount of money on the people using the infrastructure so that they’re more efficiently making these new breakthroughs. What confidence can one get that Microsoft will have a team that’s world-class that can make these breakthroughs? Once you decide to turn on the money faucet—you’re being a bit capital efficient right now, which is smart it seems, to not waste money doing duplicative work—but once you decide you need to, how can one say, “Oh yeah, now you can shoot up to the top five model?” 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.…

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