⚡️Satya Nadella: No Priors x Latent Space Crossover Special at Microsoft Build

Please welcome Swyx Saragawa Alad Gil and chairman and chief executive officer of Microsoft, Satya Nadella. Thank you. Hello, Sat. I'm so excited to be here. Welcome to a crossover episode of no priors in lane space with Saan Adela. Congratulations on an amazing build. No. Thank you so much, and it's great to be with both of you. I listen to both of you or both the podcast all the time. It's great to be on it. Thank you so much. So you're just talking about these amazing announcements from across the Microsoft estate all morning for, I think, three hours. What is the what's the most important reflection or takeaway you have? I I'd say there are perhaps the biggest one for me is let's conceptualize this more as an ecosystem play as opposed to a single model or even a single platform. Right? Whenever I at least for me, having grown up at Microsoft, having seen whatever four major platform shifts, I fall into that camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you view what's happening right now, I think this morning's keynote was, how can any company, whether it's an AI native company or an traditional enterprise company, participate as a first class participant where they can point to AI create. Right? It's not that they don't use other people's AI. Of course, they will. But to me, what's the path? What's the recipe? How do I do it? What does the stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's our job to do. Yeah. Ecosystem strategy is very complicated. Right? Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Tell us a little bit about the training strategy for Microsoft. So so the thing that we wanted to do with the MAI models was to build and as Mustafa talked about, first of all, a great lineage. Right? Starting with pretraining with very good data quality, doing all the aberrations, making sure because in in some sense, it's become even harder to build a clean lineage model. Yes. Because there's so much stuff out there that you truly need to ablate out to be able to have a fantastic pre trained model. In fact, that's one of the challenges of a lot of the open rate models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in our FDEs are are pretty gone really excited about these MAI models because how the heck can a small five b model hill climb? And it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. So to …