The Exchanges

Q I don't believe in, like, permanent business models for any of these domains, but in the near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing, enterprise bundles?

A Yeah, the way I think about this is always we've had, like, let's even take the per-user pricing. The per-user pricing is really an artifact of someone creating a budget needing certainty. Right, because it's the most important thing. Like, somebody wants a budget, they need a per user. And, and per user is just a set of entitlements to usage. Right, that's kind of what it is. And so the way is, if the first bundling will be, take some usage, bundle it into per user stacks, and, you know, then sell subscriptions. So subscriptions, I think, are going to be there, per user is going to be there. Then the next big thing will be consumption. So people will say, I want consumption. And it's also possible that people will say, I don't even want to pay for any of the subscriptions or the consumptions outcome. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, right? I mean, I've talked to customers who love, you know, outcome based pricing, and I say, I'm all in until they, oh my God, like, what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per user pricing, and I want you to consumption price, right? So I think that debate will go on. Uh, and all, all, all of these business models have a particular time and a place versus one to rule them all, and if anything, …

AI assessment note: “all of these business models have a particular time and a place”

Q Ecosystem strategy is, uh, 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, uh, training strategy for Microsoft now.

A Yeah, 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, Uh, with very good data quality, uh, doing all the ablations, 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, um, 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 FDs are, 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, interes…

AI assessment note: “Starting with pre-training, Uh, with very good data quality, uh, doing all the ablations”

Q think you raise a really interesting point, which is there's the actual agent is doing the code, and then there's the 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?

A That's right. So, so in some sense, you kind of want the Harness to define the models, the, the data, uh, 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 get up copilot or the security copilot, the stuff we showed with M dash, or even the discovery for science, it doesn't matter. All of them are multimodal harnesses, um, with, Tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient. Uh, 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, uh, 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 the, um, uh, you know, 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, um, 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 …

AI assessment note: “you kind of want the Harness to define the models, the, the data, uh, and the tools”

Q build that they're trying to rebuild a lot of applications. They're going to their SaaS vendors and saying, we're not going to work with you anymore, or we're considering an internal project. And it seems like in six to nine months, maybe some of those people will come back and say, actually, we, we can't rebuild everything. How do you think about what's durable in this world and what isn't?

A I think we have to go through one full budget cycle on this to really see the, uh, uh, The sort of, the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right? In fact, there can be even a, a simple way to say it, like, if you should always acquire something, if the marginal cost of building and maintaining, ah, something on your own is higher, ah, right? That should be, like, it's a quantifiable, right? A quantifiable thing. And the maintenance part is important, right? Even, like, you gotta remember, like, hey, You know, all the security stuff that now AI will find, you better fix them too fast. Of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through. And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be, what software do I really want to generate? What software do I want to use from others? How do I compose these two Into some agentic workflow that I have agency over, right? Because I think there'll be very little tolerance for anybody who is inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility, shows up, delivers the value, will be back again, right? We're selling softw…

AI assessment note: “if the marginal cost of building and maintaining, ah, something on your own is higher”

Q is it's an age where you can be much more ambitious and you need to be given the pace of the environment and how quickly Actually, users and companies are open to adopting new technologies. Um, how do you think about, I feel silly asking this of somebody running a, you know, trillion dollar plus company already, but how do you think about how Microsoft can be more ambitious now?

A It's a great question. Um, I think, um, I think that the thing in these type of transitions is to have a conceptual model Of how work can change to go after outcomes that you could hardly imagine previously, right? In fact, Kevin Scott has this nice line, right? Which is, um, when you can make the impossible, like when you're making hard things easier, that's sort of one point of leverage, but true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build? What was impossible and what can we build? And I'll give you one example of this, right? Which is, I take great inspiration from sort of the people who were managing the Azure network. And they came to the, this was from even last year. You know, we were scaling. You saw that I talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years. I mean, it's crazy.

AI assessment note: “true ambition is about making the impossible possible. So now the thing that is missing”

Q 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?

A 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? 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 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 excessive that now I need a new UI. Uh, oh, by the way, I like the, 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? 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 the co-work, but even some of the work we showed with auto, uh, uh, autopilot. Right, on what you see with clause 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 slash 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 night, the night, there was a bunch of stuff that all these autopilots that I ha…

AI assessment note: “your ability to scale even what is still judgment and glue work gets amplified”