This is why the ARC Prize had to report cost per task next to accuracy, and why cost-aware evaluation has been gaining ground.¹ It is also why, privately, most AI teams now spend their time on a question that leaderboards ignore: given our latency limit and our cost per query, where on the curve should each request sit?

V · Intelligence as allocation: an old idea, suddenly operational

None of this would have surprised Herbert Simon. In 1955 he argued that real decision-makers are boundedly rational: they do not optimise perfectly, they satisfice within limits of time, information and computation.² In 1991 Stuart Russell and Eric Wefald made the idea precise for AI. A rational agent with limited computation must treat its own thinking as an action, with costs and expected benefits, and choose computations by their value of computation: how much a further piece of thinking is expected to improve the final decision, minus what it costs.³

For thirty years that framework mostly lived in planning and game-playing research. It was elegant and seldom urgent, because mainstream systems had no real way to "think more" on demand. Reasoning models changed that. The theory of bounded optimality now has an obvious engineering home.

François Chollet's 2019 definition of intelligence as skill-acquisition efficiency, how much a system gets out of the priors and experience it is given, points the same way.⁴ Put the three ideas together and you get the working definition this series uses:

VI · What changes when thought is metered

If intelligence is a frontier plus an allocation policy, several things change at once.

Evaluation changes. A single accuracy number becomes uninformative and sometimes misleading. Serious comparisons will report curves, cost per solved task, or quality at a fixed budget. Teams that adopt this early will make better model choices than teams still reading leaderboards.

Product design changes. Every AI product now has a budget policy, explicit or not. Does the user choose ("think harder")? Does the developer hard-code a level? Does the model decide? Each choice moves cost, latency, and the user's perception of intelligence. The 2025 move by several providers towards automatic routing between fast and deliberate modes is an early, crude version of letting the system decide.⁵

Pricing changes. Per-token pricing made sense when tokens were roughly proportional to value. With thinking tokens, a customer can pay ten times more for an answer that is no better, or a tenth as much for one that is exactly right. Pressure will build towards pricing on outcomes rather than effort.

Engineering changes. Latency budgets, cost ceilings and quality targets become one optimisation problem. "Prompt engineering" gives way to budget engineering: designing when a system escalates, when it stops, and when it hands off.

Strategy changes. If allocation is half of intelligence, a smaller lab with a superior allocation policy can sometimes beat a larger lab with a superior model, at the budgets customers actually pay for.

VII · The objections

"This is just inference cost. Prices fall every year; the meter will stop mattering." Per-token prices for a fixed level of capability have fallen fast. But William Stanley Jevons noted in 1865 that making a resource cheaper to use tends to increase total consumption of it.⁶ Cheaper thought gets spent on harder problems, longer agent runs and more parallel attempts. The frontier task, the one that matters most, has always been the expensive one. The meter moves; it does not disappear.

"Models will learn to budget implicitly; this is a temporary engineering detail." Possibly, and article 08 takes this seriously. But "the model learns to allocate" does not make allocation irrelevant. It makes allocation a capability that can be done well or badly, measured, and improved. That is the thesis, not a refutation of it.

"Economics is a metaphor; minds are not markets." Fair warning. The claim here is narrower than it sounds. Wherever a resource is scarce and choices about it affect outcomes, the mathematics of allocation applies, whatever we call it. Thinking has become such a resource.

VIII · The question that runs through this series

If intelligence is now metered, everything downstream is rewritten: how we build systems, how we price them, how we power them and how we trust them. This series follows that rewriting over the next decade, one mechanism at a time. Its central question is simple to state and hard to answer:

What happens to intelligence, and to everything built on it, when thinking itself becomes a metered, allocatable resource?

The first part of the answer is uncomfortable. Today's most capable systems are extraordinary at thinking and surprisingly poor at knowing whether to. They write paragraphs of deliberation about trivial arithmetic and then answer an impossible question confidently after a few lines. They have a meter but no sense of value. That gap between the ability to think and the judgement about thinking is the subject of the next article.

We used to ask whether machines could think. The sharper question now is whether they know what a thought is worth.