Audio · 2026-06-04 · 42m · 10 moments

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

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most importan ✦ AI generated

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GUEST Satya Nadella HOST Sarah Guo HOST Elad Gil

timeline · colored by role

Claim◆

This is an ecosystem play, not a single model or platform play — a platform is defined by its ability to create more value above the platform than what's captured in it.

Satya Nadella frames Microsoft's AI strategy as an ecosystem play, arguing that a true platform enables others to create more value on top of it than the platform captures.

transcript

Satya Nadella: 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. I mean, whatever, at least for me, having grown up at Microsoft, having seen whatever, four major platform shifts, I sort of fall into that a 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 created? 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.

Claim◆

This is an ecosystem play, not a single model or single platform — a platform is defined by its ability to create more value on top of it than is captured within it.

Satya defines platform success by the ecosystem it enables, arguing AI should be an ecosystem play where every company can participate as a first-class AI creator.

transcript

Satya Nadella: 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, whatever, at least for me, having grown up at Microsoft, having seen whatever, four major platform shifts, I sort of fall into that a 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 created?

extends · 1

Prediction◆

Coding has worked so well that we now have to rebuild the IDE — the cognitive load of 100 agent sessions transferring back to the human is excessive and requires a new UI paradigm like Canvas.

Nadella explains that GitHub Copilot's success has created a new problem: developers managing hundreds of agent sessions need a redesigned IDE with a canvas rather than just a chat interface.

transcript

Satya Nadella: Coding has worked so well that we now have to rebuild the IDE. I mean, it's kind of nuts to see what we launched is like, oh my God, I have these 100 agent sessions. The 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, in a fully agentic world.

provides context · 2

Mechanism

The enterprise harness defines the models, data, and tools in a loop — the hard lesson is that prepping the context layer is where the magic is.

Satya describes the enterprise AI harness as a multi-model system with tool access and rich context, where the hardest and most valuable work is preparing the context layer so plans execute efficiently.

transcript

Satya Nadella: In some sense, you kind of want the harness to define the models, the 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, the stuff we showed with MDash, or even the Discovery for Science, it doesn't matter. All of them are multi-model 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.

explains mechanism · 4provides context · 2

Claim

Every company's private evals may be its biggest IP — the acid test is whether you can switch from model A to model B and still hill climb on your private eval without leaking traces.

Nadella argues that private evals are the most valuable IP a company can have in AI, and the true test of platform independence is whether you can swap frontier models and still climb on your private eval.

transcript

Satya Nadella: Every company, having private evals, maybe the biggest IP. I think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces, maybe one of the biggest drivers of IP. So in other words, another acid test is you have an eval that's private. You're using a model A. Can you switch it to model B and climb up? If you can, then you're in control. If you can't, you're not in control. And that's where even the harness decision becomes super important. So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs.

Claim◆

Private evals may be the biggest IP a company has — if you can switch from model A to model B and hill climb using your private eval, you're in control.

Satya argues that private evals are becoming the most valuable IP for companies — the ability to switch models while hill climbing on your own private eval is the acid test of control.

transcript

Satya Nadella: Every company, having private evals, maybe the biggest IP, right? I think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces, maybe one of the biggest drivers of IP. So in other words, another acid test is you have an eval that's private. You're using a model A. Can you switch it to model B and climb up? If you can, then you're in control. If you can't, you're not in control. And that's where even the harness decision becomes super important. So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs.

explains mechanism · 2

Prediction

Every company will have human capital plus token capital — the traces between agents and humans become the context to train the 'company veteran agent,' which can go on the balance sheet.

Satya envisions that companies compound human and token capital, where the traces of agent-human collaboration become a durable asset — a 'company veteran agent' that can be put on the balance sheet, unlike tacit human knowledge.

transcript

Satya Nadella: At the end of the day, every company is going to have both the human capital that is still going to be super valuable because humans and their ability to find the gaps that exist at all times is going to be the way we all will create value, right? I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition, even as token capital goes up, right? So let's say any corporation has lots of tokens and a lot of human capital. The question is, how do you compound the two? So if you have a, if you take in teams, I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value. Then that goes back to train not a generalist model, but to train the company veteran agent. right? That is super valuable again, right? Which is when a company says it should in fact go onto the balance sheet is how I think about it, right? That's what, in fact, there may be like human capital was never possible to go put on a balance sheet because you didn't know how to capture the tacit knowledge. Whereas now I think you can with the agents that have learned through time, through all the traces.

extends · 3gives example · 2supports · 2

Prediction

The human capital and the traces between humans and agents in an enterprise become context that trains a company veteran agent — that tacit knowledge can now go on the balance sheet.

Nadella describes a future where the combination of human capital and agent traces creates a 'company veteran agent' whose learned tacit knowledge becomes a balance-sheet asset, changing how IP and value are accounted for.

transcript

Satya Nadella: Every company is going to have both the human capital that is still going to be super valuable because humans and their ability to find the gaps that exist at all times is going to be the way we all will create value. I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition, even as token capital goes up. So let's say any corporation has lots of tokens and a lot of human capital. The question is, how do you compound the two? So if you take in teams, I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value. Then that goes back to train not a generalist model, but to train the company veteran agent. That is super valuable again. Which is when a company says it should in fact go onto the balance sheet is how I think about it. That's what, in fact, there may be like human capital was never possible to go put on a balance sheet because you didn't know how to capture the tacit knowledge. Whereas now I think you can with the agents that have learned through time, through all the traces. So that's what at least we think will happen. I think the SEC is going to have to have accounting standards for token expertise.

Prediction

The SaaS business model packaged workflow in a particular way — we now have to unbundle and rebundle these things and discover new business models, and the value creation opportunity in the agent world is 10x more.

Nadella argues that the old SaaS model of bundling data model, business logic, and UI into apps is being disrupted. Microsoft 365's Work IQ now exposes the M365 graph as a database for agents, creating vastly more value but requiring re-architected infrastructure and new business models beyond per-user subscriptions.

transcript

Satya Nadella: We had a particular way we captured workflow in apps. Because we built a data model. We schematized some part of some business process. We then built a bunch of business logic, and then we put a bunch of UI on top of it. So that's kind of what every SaaS company did. For like 20 years, that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking. I still think the data model that you build underneath every SaaS application is super good. Like why reinvent it? Like my general ledger better be good. So I think the challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models. If you look at it, what's happening today with Microsoft 365 is a great example. We have this thing called Work IQ. We've exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps. It was all e-mail operated on it, Teams operated on it, Word, Excel, PowerPoint, SharePoint. But now, this is one of the coolest things I get to do with Work IQ. I go to a GitHub repo and I say, hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make? It literally can go look at all those transcripts, come back with a plan to change a code base. Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more. But it does require us to have, for example, there's going to be usage around M365, which is going to be perhaps more than even the end users. And we have to even re-architect. In fact, what I use to serve an inbox or a mailbox cannot be used to serve an agent.

extends · 1

Example

The SaaS business model packaged workflow into data model, business logic, and UI — we now have to unbundle and rebundle those layers in new ways, creating 10x more value in the agent world.

Satya argues that the data models and business logic inside existing SaaS apps remain valuable, but must be unbundled and exposed to agents — creating far more value than the original packaged SaaS model, as Microsoft 365's Work IQ demonstrates.

transcript

Satya Nadella: We had a particular way we captured, I would say, workflow in apps, right? Because we built a data model. We schematized some part of some business process. We then built a bunch of business logic, and then we put a bunch of UI on top of it, right? So that's kind of what every SaaS company. For like 20 years, that was. And that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you build underneath every SaaS application is super good, right? Like why reinvent it? The same thing is now happening with M365, because with work IQ, we've exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps, right? It was all e-mail operated on it, Teams operated on it, Word, Excel, PowerPoint, SharePoint. But now, I go to a GitHub repo and I say, hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make? I mean, think about that. It literally can go look at all those transcripts, come back with a plan to change a code base. Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more.

extends · 1supports · 1

Highlight slides

Platform as Ecosystem✦ from: This is an ecosystem play, not a single model or single platform — a platform is defined by its ability to create more value on top of it than is captured within it.AI as an Ecosystem Play✦ from: This is an ecosystem play, not a single model or platform play — a platform is defined by its ability to create more value above the platform than what's captured in it.First-Class AI Participation for Every Company✦ from: This is an ecosystem play, not a single model or platform play — a platform is defined by its ability to create more value above the platform than what's captured in it.Ecosystem over Monolith✦ from: This is an ecosystem play, not a single model or single platform — a platform is defined by its ability to create more value on top of it than is captured within it.Copilot's success creates a new UX crisis✦ from: Coding has worked so well that we now have to rebuild the IDE — the cognitive load of 100 agent sessions transferring back to the human is excessive and requires a new UI paradigm like Canvas.From chat to canvas: the IDE reborn✦ from: Coding has worked so well that we now have to rebuild the IDE — the cognitive load of 100 agent sessions transferring back to the human is excessive and requires a new UI paradigm like Canvas.Private Evals Are the New Moat✦ from: Private evals may be the biggest IP a company has — if you can switch from model A to model B and hill climb using your private eval, you're in control.The Acid Test of AI Control✦ from: Private evals may be the biggest IP a company has — if you can switch from model A to model B and hill climb using your private eval, you're in control.

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