No Priors: Artificial Intelligence | Technology | Startups
00:05:48 · swyx
[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 Nadella
[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 Gil
[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 Nadella
[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.
00:09:38 · Elad Gil
[00:09:38] S3: I think you raised a really interesting point, which is there's the actual agent that's doing the code, then there's a harness around it. And that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent. What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized?
00:09:56 · Satya Nadella
[00:09:56] S0: That's right. So, in some sense, you kind of want the harness to define the models, data, and the tools, and so that you have a loop across those three. And so, what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copilot, the stuff we showed with Emdash, or even the discovery for science, it doesn't matter. All of them are multimodal harnesses with tools access so that you can do this progressive disclosure of tools even so that they're token efficient. And then you're feeding it with very rich context. Because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products. It's available in Foundry. And we're open, like you can use your Lama harness, whatever, or you can use any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now, a lot of dialogue is, hey, if I train the harness plus tools and the model together, you get evals. And what we are proving out is, and the best example of that, is what we did with Emdash, right? Because when it launched, it found bugs or vulnerabilities that were not found by mythos. And so there is existence proof, I would claim, that you can have a multimodal harness that can, in fact, be more performant in the real world.