We’ve informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire! We covered the MAI model technical takeaways on yesterday’s AINews, so I will focus our recap of Satya’s main messages around three elements: * Satya’s adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP * AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft’s position on this as the ur-SaaS titan, and Satya had great answers * Making the Impossible Possible: Kevin Scott’s inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.

Enjoy! Full Video Transcript Voiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya Nadella Sarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, 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 b- both the podcasts all the time. It’s great to be on it. Thank you so much. [00:01:00] So you’re just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what’s the most important reflection or takeaway you have? AI as an Ecosystem Platform Sarah Guo: I, I’d say there are, uh, perhaps the, the biggest one for me is let’s sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right? Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, 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, 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 a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] 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 a stack look like? What does the tooling look like? What is valuable? How do you do that? That’s it. That’s sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right? Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah. MAI Models & Training Strategy Sarah Guo: 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? Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it’s becoming even harder to build a clean lineage model just because there’s so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model. In fact, that’s one of the challenges of a lot of the open weight 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 the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb? Uh, 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. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right? So it’s not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you’ll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they’re not really that critical- They’re work, yeah Swyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there’s a new frontier. Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let’s say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher. Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I’m wondering, you know, you have all this AI strategy that you’re rolling out. Lessons from Two Years of AI Development Swyx: I’m wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we’re gonna really throw a lot of computer transformers.” Satya Nadella: Uh, and they’ve helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they’re climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right? So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it’s very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry. Sarah Guo: Because right now I think when people say, “Wow, I don’t want a token max,” it’s an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that’s kind of what I wish we had gotten there, but I’m glad we are here. Real-World Value & Use Cases Sarah Guo: What are some of the use cases that you’ve seen that have created the most value for your customers? Because I know that people talk a lot about code, and I think it’s pretty clear that that’s something that’s having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it’s interesting, by the way, Elijah, to even talk about the coding, right? Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it’s kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that’s why we need a canvas. So it’s kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right? If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I’m positive that six months from now we’ll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right? I can... Sort of given even my identity, did a bunch of work, then of course I’ll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that’s where compressing of workflows, uh, completing of tasks, uh, that’s where I think