The Compounding Divide.
Exploring why the AI you can buy is converging toward a commodity and why the only thing that compounds is the human-fed learning loop you cannot purchase,
There is a financial services firm in Singapore — call it a composite, because the pattern is now common enough to be a type rather than a case — that spent 2025 acquiring the most advanced AI capability money could buy. Frontier models on enterprise contracts. A dedicated compute allocation that would have been a national-scale resource a decade ago. A new internal platform team. A board deck with a slide titled, without irony, “AI Moat.”
By any procurement standard, they had won. They owned more raw AI capability than any competitor in their market. And for a while, the dashboards agreed: faster reporting, quicker turnaround, lower cost per task.
Eighteen months later, a mid-sized competitor — running older, cheaper, demonstrably less powerful models — was consistently making better calls in exactly the situations that mattered most: the ambiguous ones, the novel ones, the ones with no precedent in the training data. The Singapore firm had more AI. The competitor was getting more from it. And the gap between them was not closing. It was widening, quarter on quarter, at an accelerating rate.
This is the Compounding Divide. It is the most important strategic distinction of the current AI moment, and most organisations are on the wrong side of it without knowing it. The divide is not between firms that have AI and firms that don’t; that race is essentially over, and almost everyone has it. The divide is between firms whose AI gets smarter every time it’s used and firms whose AI simply runs faster while the people around it slowly stop thinking.
The accounting distinction nobody expected from a CEO
In June 2026, Satya Nadella published a long essay — “A frontier without an ecosystem is not stable” — that was viewed more than 28 million times. What made it land was not a product announcement or a benchmark. It was a piece of accounting.
Nadella proposed that two kinds of capital now define a firm in the AI era. The first he called token capital: the AI capability a company builds and owns — its proprietary systems, its data, its evaluations, its tuned and orchestrated workflows sitting on top of foundation models. (The “token” is the unit of text a model reads and generates. It has nothing to do with cryptocurrency.) The second is human capital: the knowledge, judgement, relationships, ingenuity, and pattern recognition of a company’s people.
Then came the line that most readers treated as reassurance and that I read as a warning:
“Human capital does not become less valuable as token capital grows. It only becomes more valuable… Without human direction, you have compute running in circles.”
— Satya Nadella, June 2026
The reassuring reading is: don’t worry, humans still matter. The harder reading — the one Nadella’s own framing actually supports — is that the two kinds of capital behave completely differently as assets. One you can buy. One you cannot. And only one of them compounds. Understanding why is the whole game.
Why token capital converges
Token capital has a seductive property: it is purchasable. You can sign a contract, provision the compute, license the model, stand up the platform, and own meaningful AI capability within a quarter. It is legible to a board, it appears on a balance sheet, and it can be acquired with a decision rather than earned through years of practice.
That same property is its weakness. Anything you can buy, your competitor can also buy — from the same providers, on roughly the same terms, at roughly the same time.
Frontier capability is steadily becoming a utility: metered, broadly available, and increasingly undifferentiated at the point of use. The history of general-purpose technologies is unambiguous on this point. Electrification, in its early decades, conferred enormous advantage on the firms that had it. Then everyone had it, and having it stopped being an advantage: it became the precondition for being in business at all. The same arc ran through enterprise software, through cloud computing, through broadband. The general-purpose layer commoditises. The advantage migrates to the complement, the thing the technology cannot supply and the market cannot sell you.
The economist’s version of this is straightforward: when a valuable input becomes abundant and cheap, the returns flow to whatever scarce input it depends on.
Make compute and frontier models abundant, and the scarce complement is the human judgement that directs them and the proprietary loop that improves them. PwC’s 2026 Global AI Jobs Barometer puts numbers on the migration: the wage premium for AI skills has climbed to 62%, and “professionalised” roles that combine domain judgement with AI are growing roughly twice as fast as “democratised” ones, with 42% faster wage growth since 2021. The market is already repricing the complement. Most corporate AI strategies are still buying the commodity.
The depreciation default
To see why the loop is so unusual, it helps to remember how almost every asset a firm owns actually behaves: it depreciates. A machine wears. A patent expires. A software platform ages into legacy. A trained workforce forgets, leaves, and has to be retrained. The natural tendency of value, in the physical and organisational world, is to leak away. Most of management is, in effect, the work of fighting depreciation — maintenance, retraining, reinvestment, replacement. The default direction of an asset is down.
Token capital obeys this default with unusual speed. A frontier model that is state of the art today is mid-tier within a year and a commodity within two, as the next generation arrives and the price of the previous one collapses. The compute you provisioned is a depreciating capital expense. The platform you built ages the moment it ships. Bought capability does not merely fail to compound — it actively decays, and it decays on the same schedule for you as for every competitor who bought the same thing.
A genuine learning loop is the rare asset that runs the other way. Because it is fed by accumulated judgement and proprietary feedback rather than by hardware or licences, each cycle leaves it slightly more capable than the last. It appreciates through use rather than depreciating despite it. This is what Nadella means when he calls it “unlike most assets.” The strategic error of the Token Buyer is not that they bought a bad asset. It is that they bought a depreciating one and assumed it would behave like an appreciating one — that owning the engine was the same as owning the compounding.
The mechanism: why the loop compounds
Nadella’s real argument is not about either kind of capital in isolation. It is about the engine that links them — what he calls a learning loop. The idea is deceptively simple. Every time your people use your AI systems, both sides should get smarter. People learn from what the AI surfaces. The AI capability improves from the data, corrections, evaluations, and judgement your people feed back into it. Run that loop repeatedly and it accumulates value the way compound interest does — rather than depreciating the way most assets do. Nadella describes the result as “a hill-climbing machine” that becomes “the new IP of the firm.”
This is the part you cannot buy. There is no contract for it. You can only build it, slowly, through use — through thousands of cycles in which a human applies judgement to an AI output, the output improves, the human’s understanding sharpens, and the improved system raises the ceiling on what the next cycle can attempt.
And here is the structural fact that makes the divide so unforgiving: compounding is exponential. A firm that improves its loop by a small percentage each cycle, sustained over hundreds of cycles, does not end up slightly ahead of a firm that simply bought capability and left it static. It ends up exponentially ahead. The distance between them does not grow at a constant rate. It grows at an accelerating one. This is why the Singapore firm’s competitor kept pulling further away rather than being caught: the leader had purchased a level; the competitor had built a slope.
PwC’s data shows what this looks like in aggregate. The top 20% of the most AI-exposed companies achieved average labour-productivity growth of 163% relative to 2018: nearly 5 times higher than the most AI-exposed companies overall. Same access to frontier AI. Radically divergent outcomes. The variable that separates them is not how much AI they bought. It is whether the AI was wired into a loop that compounds human judgement, or simply deployed to run existing tasks faster.
What gets starved
If the loop is the source of all the compounding, then the human judgement that feeds it is the single most important input in the system. Which makes the central irony of the current moment almost unbearable: that input is precisely what most organisations are quietly degrading.
The Microsoft and Carnegie Mellon University 2025 study of 319 knowledge workers found that higher confidence in generative AI inversely correlated with critical thinking. The more a worker trusted the AI’s output, the less they scrutinised it. Participants reported applying no critical thinking at all to a substantial share of their AI-assisted tasks. Read through Nadella’s framework, this is not a productivity footnote. It is the fuel line to the engine being cut. The corrections, the caught errors, the applied judgement — the very inputs that make the loop compound — stop being supplied at exactly the moment the organisation believes it is becoming more efficient.
The training and mentorship picture compounds the problem. PwC found that even as AI skills command a 62% wage premium, more than half the global workforce reported no recent training and 57% lacked access to mentorship. Organisations are accumulating token capital on the asset side while letting human capital — the only thing that makes token capital compound — go uncultivated. They are buying more engine and draining more fuel, and reading the short-term speed as success.
There is a generational edge to this that deserves naming. The corrective judgement that feeds the loop is itself built through years of doing the work — forming positions, being wrong, being challenged, and recalibrating. A junior cohort that arrives into an environment where the AI produces the position and the human merely approves it never builds that judgement in the first place. The independent research is directionally consistent here: studies of cognitive offloading have repeatedly found the effect strongest in younger users, precisely the group entering the workforce now and forming the professional instincts they will rely on for decades. An organisation can therefore be starving its loop on two horizons at once — degrading the judgement of the people it has, and failing to grow the judgement of the people it is hiring. The fuel line is being cut at both the present and the future end.
The Work AI Index 2026 offers the inverse, hopeful signal. Its “high AI achievers” do not simply prompt and accept. They spend more time reviewing and correcting AI output than low achievers (40% of their AI time versus 33%), and they are markedly more likely to deliberately decline to use AI on certain tasks where their own judgement is the better instrument. The differentiator at the individual level is not usage volume. It is the deliberate application of judgement — the behaviour that feeds the loop. The people getting the most out of AI are the ones doing the most thinking around it, not the least.
Three archetypes
It helps to name the postures organisations are adopting, because the labels make the trajectory visible before the financials do.
The Token Buyers have defined AI as a procurement problem. They measure success by capability acquired and cost removed. Their dashboards are green. Their AI runs their existing processes faster, and they have mistaken that speed for a moat. They are, in Nadella’s phrase, running compute in circles — accumulating a depreciating asset on equal terms with every competitor who signed the same contracts, while the human judgement that might have made it compound goes unexercised. The bill has not arrived, so they believe there is no bill.
The Loop Tourists understand, in principle, that the loop matters. They have read the essay. They talk about feedback and human-in-the-loop and continuous improvement. But they have not changed a single incentive, workflow, or metric to make it real. Their people are still rewarded for output speed, not for the judgement that improves the system. The loop is in the strategy deck and absent from the working day. This is, currently, the largest category — and the most dangerous, because the language of compounding provides cover for an organisation that is not actually doing it.
The Loop Builders — the rarest — have rebuilt their operating model around the loop. They instrument the points where human judgement meets AI output and treat those points as the firm’s most valuable real estate. They reward the analyst who caught the subtle error, not just the one who shipped fast. They protect the cognitive work that feeds the system even when it looks, on a quarterly dashboard, like friction. They are often slower than the Token Buyers in any given quarter. They are building the slope while everyone else buys the level.
What this means in practice
For organisations, the strategic question has to change. “How much AI capability have we acquired?” is now close to meaningless — the honest answer for most firms is “roughly the same as our competitors, and increasingly so.” The question that actually predicts the future is: is our organisation getting measurably smarter every time it uses its AI, or just faster? If you cannot point to the specific mechanism by which judgement is captured, fed back, and accumulated, you do not have a learning loop. You have a faster way of doing what you already did, available on equal terms to everyone you compete with.
The practical moves follow from this. Instrument the human-AI interface and treat the corrections, overrides, and judgements that happen there as a first-class asset, not as exhaust. Reward the application of judgement, not just the velocity of output — because the moment your incentives favour speed over scrutiny, you are training your people to stop feeding the loop. Protect the cognitive work that builds expertise even when it reads as inefficiency, because expertise is the input the loop runs on, and an organisation that has automated away its own judgement has quietly switched off its compounding.
For individuals, the implication is sharper and, I think, clarifying. The era in which “I can use AI” was a differentiator is ending. Everyone can use AI. Fluency with the tools is becoming what literacy with a spreadsheet became — a baseline, not an edge.
The durable position is to be the human judgement that makes the loop compound: the person who notices what the model missed, who distrusts the plausible answer, who can correct the system rather than merely operate it, and who feeds that correction back so the next cycle starts higher. Being a fluent prompter makes you interchangeable with everyone else who prompts fluently. Being the judgement in the loop makes you load-bearing — the scarce complement to an abundant, commoditising input.
This is the actionable core of Nadella’s “only becomes more valuable.” Human capital becomes more valuable not automatically, and not for everyone, but specifically for the people and firms positioned at the point where judgement compounds AI capability. For everyone else, the abundance of cheap, capable AI is not an opportunity. It is a rising tide that lifts competitors who built loops and slowly submerges those who only bought tokens.
The closing uncomfortable truth
The deepest discomfort in the Compounding Divide is its timing. The divide becomes most dangerous at the exact moment AI feels most democratised. When everyone has access to frontier capability on similar terms, the natural conclusion is that the playing field has been levelled. The opposite is true. Equal access to a commoditising input is precisely the condition under which the only remaining differentiator — the compounding loop you cannot buy — matters most.
So the firms that feel safest are often the most exposed: they have the AI, the dashboards are green, the speed is real, and none of it is compounding because the human judgement that would make it compound has been optimised into silence. And the firms that feel behind — the ones whose people still argue with the model, still correct it, still insist on understanding before they ship — are quietly building the one asset that accumulates while everyone else’s depreciates.
Token capital is the part you can buy. That was always going to make it the part that converges. The advantage was never going to live in the asset everyone can acquire on equal terms. It lives in the loop — and the loop only turns if a human keeps feeding it judgement.
You can buy the tokens. You have to earn the loop. And the gap between the two is already widening faster than anyone buying their way in can see.