We Need An Ecosystem in AI, And Every Company Can Win A Place In It

00:01:48 · swyx, Sarah Guo, Satya NadellaPlay from here

[00:01:48] S2: Thank you so much. [00:01:48] S1: 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? [00:01:59] S0: I'd say there are Perhaps 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? I mean, at least for me, having grown up at Microsoft, having seen whatever four major platform shifts, I sort of 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 a traditional enterprise company, participate as a first class participant where they can point to AI they 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 sort of our job to do. [00:03:11] S1: 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. Like, tell us a little bit about the training strategy for Microsoft. Yeah.

00:03:27 · Satya NadellaPlay from here

[00:03:27] S0: 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 pre training, with very good data quality, doing all the abolations, making sure. Because in some sense, it's becoming even harder to build a clean lineage model just 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 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 our FDEs are pretty gone really excited about these MAI models because how the heck can a small 5B 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 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, but they're not really that critical 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 what I think is interesting. And then the one other thing, Sara, since you brought that up, is I do feel there's a new frontier. Like, people talk about the frontier and are you operating at the frontier? Interestingly enough, if you add a little temporality to it, you can use, let's say, in fact, the Land O'Lakes demo we showed was pretty cool. We used whatever, GPT-five point five, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher. So that is another aspect of what it means to appear I mean, operate at the frontier.

00:05:48 · swyxPlay from here

[00:05:48] S2: Yeah. I I think I first of all, have to congratulate you on basically building a frontier neural lab inside of Microsoft in two years. I'm wondering, you know, you have all this AI strategy that you're rolling out. I'm wondering, what do you know now that you wish you would tell yourself two years ago? Wait. Two or three years ago. Three years for the Jensen partnership, two years for MAI.

00:06:07 · Satya NadellaPlay from here

[00:06:07] S0: 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 skating loss 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, and they've helped. The thing that I always go back and say, wow, these things do have capability that they're climbing up. I mean, 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 interesting, 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 measurable. Right. That I wish we had sort of even had more in our consciousness, right, which is as an industry. 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.

00:07:33 · Elad GilPlay from here

[00:07:33] S3: 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?

00:07:46 · Satya NadellaPlay from here

[00:07:46] S0: Yeah. I think yeah. To your point, obviously, coding is now got But it's interesting, by the way, Ladish, to even talk about the coding, right? 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 launched. It's like, oh my god, I have these 100 agent sessions. Cognitive load, it transfers back to me as a human is so excessive that now I need a new UI. Oh, by the way, the chat as the only artifact is 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. 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 start seeing that co work, but even some of the work we showed with 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 tokensagents that are long running, durable, right, then your ability to scale, even what is still judgment and glue work, gets amplified like coding does. So you can I'm positive that six months from now, we'll all be saying, oh, wow. Like all through the night, there was a bunch of stuff that 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, what did you do? Like, did I do this work? And so on. So I think that that's where compressing of workflows, completing of tasks, that's where I think a lot of the value gets created.