Capital in Tokens: Why Your People Just Got More Valuable, Not Less

I just finished Satya Nadella’s essay on X, “A frontier without an ecosystem is not stable.” It went up on Sunday and has already pulled in tens of millions of views which tells you something on its own, given its not a product launch or a model release. It’s a strategy memo for every company trying to figure out where it stands in this new economy. It put words to something I’ve been circling for a while.

Part of that is because I’ve spent the last few weeks deep in the Cursor ecosystem, watching how fast things move when AI actually gets embedded into a workflow instead of bolted onto one. Reading Nadella’s framing made it concrete: what’s happening right now, in record time, is a full rebuild of the operating system businesses run on. Not an upgrade. A rebuild.

“A frontier without an ecosystem is not stable.” - Satya Nadella

That’s the thesis in eight words. Everything else in the essay is him unpacking it.

The two kinds of capital

The core idea is what Nadella calls “capital in tokens.” His argument is that every company now carries two distinct forms of capital.

The first is human capital what he describes as the knowledge, judgment, relationships, ingenuity, and pattern recognition of a company’s people. Nothing new there. We’ve always known this mattered.

The second is new: token capital. The proprietary AI capability a company builds and owns, as opposed to what it simply rents through an API.

The counterintuitive part: the obvious read is that as token capital scales up, human capital becomes less important. Machines do more, people do less, value shifts accordingly. Nadella’s argument flips that completely. In his own words, human capital does not become less valuable as token capital grows, it becomes more valuable.

Human capital doesn’t get cheaper as AI gets stronger. It gets more expensive.

Why AI alone just spins its wheels

Think about what AI actually does without a human steering it. It optimizes whatever target it’s given, inside whatever boundaries it’s given. But it doesn’t decide which problems matter. It doesn’t connect a customer complaint in support of a roadmap decision in a product to a phrase in a sales deck. It doesn’t notice that the real issue isn’t the one anyone asked about.

That’s still entirely a human job. Without it, AI doesn’t fail loudly, it just runs in circles, confidently. It produces a lot of output that looks like progress and isn’t.

“Without human direction, you have compute running in circles.” - Satya Nadella

Put simply: AI without people is just expensive servers heating up the atmosphere. Impressive electricity bill, no compounding value.

The real competition isn’t about picking the best model

This matters for anyone actually running a company, not just watching one from the sidelines.

Every major tech player is now building on remarkably similar underlying technology. The model landscape is converging fast what was a meaningful gap six months ago is closing. So if everyone has access to roughly comparable horsepower, “which model did you pick” stops being the differentiator.

Nadella’s sharpest point is that the real competitive question is learning system you build on top of it.

Your workflows. Your accumulated edge cases. Your institutional scar tissue from the deals that went sideways and the processes that finally worked. All of that, fed back into a system that gets better every time someone uses it, that’s the actual asset. Not the model. The loop around the model.

Nadella actually gets specific about what that loop is made of, which is rare for a CEO essay like this. He lays out three layers: private evaluations that measure whether the model is improving against outcomes the business actually cares about, not just public benchmarks; private reinforcement-learning environments that let the model get stronger from real traces of work inside the company; and a retrieval layer that makes institutional memory searchable and usable. Stack those three together and you get a system that compounds with every cycle of use - what he calls a hill-climbing machine.

“Unlike most assets, it compounds.” - Satya Nadella, on the hill-climbing machine

Most balance-sheet assets depreciate the moment you buy them. This one is built to do the opposite.

I’d go as far as saying we need a new metric entirely: something like AI-learnability - how fast and how well a company’s internal systems improve from each interaction. I suspect that number will end up correlating with business outcomes more tightly than almost anything else we currently track.

And this only works if knowledge is captured in a form an agent can actually use, not buried in someone’s head, not scattered across six tools, not living exclusively in a quarterly deck. It means fine-tuning on your real cases, your real failures, your real customers. Not generic, abstract data that could belong to anyone.

The sovereignty test

Nadella gives this loop a name worth sitting with: the new IP of the firm. And he offers a concrete way to test whether you actually have one.

Can you swap out a generalist model: switch providers, upgrade versions, whatever without losing the accumulated, company-specific expertise your system has built up? If the answer is no, you don’t have a learning loop. You have a rental agreement with extra steps, and your “institutional knowledge” lives entirely inside someone else’s model weights.

The framing is simple but it’s a genuinely useful filter for decision-making. You can delegate a task. You can hand off entire chunks of execution.

“You can never offload your learning.” - Satya Nadella

That’s the line Nadella keeps coming back to. You protect the learning like it’s the whole company. Because increasingly, it is.

A warning from the 2000s

This is what turns the argument from a nice metaphor into an actual warning and it’s not me reaching for a parallel, it’s the one Nadella draws himself.

We’ve run this experiment before. In the 2000s, GDP numbers looked perfectly healthy on paper while entire industries were quietly hollowed out by outsourcing. Manufacturing left. Jobs disappeared. The aggregate statistics stayed calm while the underlying structure of entire economies shifted somewhere else, slowly enough that nobody panicked until it was already done.

Nadella’s warning is that the same pattern is available again, just with frontier AI models instead of factories: a small number of dominant models absorbing the specialized knowledge of entire industries and selling it back at commodity prices, while the companies that generated that knowledge end up with none of the value. A company or a country that becomes fully dependent on someone else’s model, with no internal loop of its own, is outsourcing its future capability while the dashboard still looks fine.

This matches what I’ve been seeing elsewhere

This connects directly to something I’ve written about before: value accumulates with whoever owns the outcome, not whoever sells the tool. The tool is increasingly commoditized. The outcome, the actual compounding result inside a specific company - isn’t.

It also explains something I’ve been watching closely: the AI-trainer market. Why is it growing right now, specifically? Because the companies pulling ahead understand that human capital inside the building is exactly what makes “capital in tokens” function at all. Someone has to teach the system what good looks like. That’s a human job, and right now it’s an underpriced one.

This is the part of Nadella’s formula that actually makes it work in practice. Capital in tokens, on its own, is inert. It only compounds when it’s paired with the human judgment that knows what to feed it and what to ignore.

So what does this actually mean for you

Everyone will have access to roughly the best model, eventually, on roughly the same terms.

The companies that come out ahead are the ones building their own internal learning system right now - their own quality benchmarks, their own fine-tuning loops, their own corporate knowledge base that an agent can actually query. Two or three years from now, that compounding advantage won’t be something a competitor can buy with a subscription. It’ll be structural.

The real question now is how your company plans to accumulate its own experience through AI, in a way nobody else can copy.

That’s the real moat.