Anthropic's S-1 prospectus, filed this week ahead of what may be the largest stock market listing in history, is being read in two ways. Those already persuaded that AI is the next great industrial build-out see a company growing faster than any business before it, generating more than $16 billion in revenue in the first half of this year from a little over $1 billion in the same period of 2025, and turning an operating profit in its most recent quarter. Those who were sceptical before the filing have found reasons to worry: close to half a trillion dollars of compute commitments that cannot be cancelled, customers who can leave within weeks, and a share structure that leaves public investors with no meaningful say. Both readings are fair, but neither explores the more fundamental insight the filing offers.
Source: Anthropic Form S-1, quarterly statements of operations. 2026 figures are unaudited.
In an essay published this week, Google DeepMind co-founder Shane Legg and two economists separate AI capability into two things. Competence is how well a model does a task. Coordination is how well many agents work together without the handoff losses that limit the size of human organisations. Their argument is that competence improvements make firms more productive without making them larger, and that the structural change everyone anticipates, with a few large AI-run firms dominating markets such as software, logistics and finance, only arrives when coordination crosses a threshold they call "supercooperative". Their reading of the evidence is that there is still some way to go. Multi-agent systems produce less than proportionate output as they grow, errors compound across agents, and models that beat human experts on two-hour tasks do half as well over two days.
Read against that frame, Anthropic's revenue looks exactly as the essay would predict for a world of high competence and weak coordination. Spending is concentrated in software, the deepest and most scalable of markets, and within the enterprise it is heavily skewed towards coding. Enterprise users who do not touch coding agents spend a modest amount each month; the heaviest users spend around twenty times as much. The customer base is widening rather than concentrating, with the share taken by the five largest buyers falling from over a third at the start of the year to around a fifth in the latest quarter, as thousands of smaller organisations join. The company's own disclosures show that paid usage drops by a fifth over the Christmas fortnight. Demand follows the human working week, because a person still decides how much work to delegate and must check what comes back.
This matches what we see with our own clients. Over the past six months, most have moved from not considering Claude to treating it as the default. Very few of them have ever indulged in the token-maxing that some sceptics believe inflated the first half of the year, and their spending is bounded not by price but by how far their people can push delegation before verification becomes the bottleneck. Multi-agent systems work when coordination is designed into the harness, with explicit handoffs, tests and review. That costs tokens as well as engineering, and the prospectus acknowledges it, warning that agent workloads use more compute per unit of output and that competition may stop the company charging what they cost.
The essay closes with a request: if coordination is what counts, economic statistics should track payroll against compute spending to see how people and machines are teaming up inside firms. The prospectus is the first such disclosure at scale. In its latest quarter Anthropic spent $5.6 billion on compute and $1.9 billion on its people, shares included: three times its total pay bill and nine times the cash part. Compute has been the larger bill, by a multiple, in every period the filing covers. Headcount has grown about sixfold since the end of 2024, to around 5,300; quarterly revenue has grown nearly sixtyfold, to $11.6 billion, and trailing revenue per employee is above $6.6 million. Claude writes a large and growing share of the company's code and increasingly performs its research, a cycle the filing calls closing the loop. This is the essay's mosaic intelligence, the way an organisation combines jagged specialists into a collective capability none of them has alone, with the pieces rearranged. A few thousand people set direction and check the work, and the specialist capacity is a model, available in whatever quantity the compute allows. No company has shown so clearly what an organisation looks like when its output scales with compute rather than with people. The obvious qualification is that Anthropic is a special case. It builds the technology that makes this possible, and its compute bill pays for training the models and serving customers as well as for running the company, so no bank, law firm or manufacturer will show that ratio. The shape is still worth thinking about, because a firm that buys the capability instead of making it faces a milder version of the same arithmetic, and this is the first clear picture of where it leads.
That is also why the commitments are so large. If the organisation scales with compute, compute is the constraint, and the filing says so: demand for compute has historically exceeded the supply available to Anthropic, compute will remain its largest expense for the foreseeable future, and its place at the frontier depends on securing enough of it to train the next generations of models. Hence around $417 billion of new and amended compute deals since January, on top of what was already committed, and a further $125 billion to $150 billion expected from deals delivering between 2028 and 2031.
The second reason is the one the essay supplies. The training still to come is meant to produce models that are capable together as well as alone, "a country of geniuses in a data center" in the filing's phrase, able to divide a project among copies of itself and recombine the results: mosaic intelligence built from one model rather than from human specialists. The essay's authors say today's systems coordinate poorly in practice but that their potential is immense, since copies of an agent share the same weights and can hand each other a complete working state. The filing already describes Claude coordinating thousands of agents on projects that run for hours or days, work that consumes far more compute per task than a chat exchange. Whether that is coordination in the essay's sense, agents working well together without a human in the chain, is the open question. The commitments are a bet that the next rounds of training answer it, with the compute to run the result already in hand, and the filing concedes that more compute may not yield proportionately better outcomes.
“Even when no team of human specialists can surpass the model on individual tasks, coordination still caps how big an organization can get without falling apart.”
The essay's finding is that competence reshapes an economy gradually and coordination transforms it abruptly, so a firm that crosses the threshold inside the window these commitments cover would change shape fast, and the economy around it with it. If the threshold is not crossed in time, what remains is a capable supplier of individual intelligence carrying very large fixed compute bills while its customers pay as they go.
Takeaways: Anthropic's prospectus is the first detailed look inside a company built to scale with compute rather than people. It is the extreme case, since it makes the technology, but the shape is as significant as the investment case. Its demand is real, broadening but still bounded by human coordinators and by agents that do not yet work well together, which is where the economics of intelligence framework says we are. Its cost structure, with machines outweighing payroll several times over and models writing much of the code, is what that framework describes for a firm in the "supercompetent" regime: fewer people, in judgement and orchestration roles, with specialist work done at the cost of compute. Whether it also becomes the first supercooperative organisation is the question the filing cannot answer, and the compute commitments beneath it are a bet that further training crosses that threshold before the end of the decade. Two indicators will tell: compute spend against payroll, which the essay's authors ask economists to track, and the first sign that usage stops following the human working week. That is the moment the economics of intelligence shifts from productivity to structure, and the moment the question of who controls these firms becomes even more critical.