AI Compute Cost Curve: The Collapse the Market Is Ignoring

The thesis the market is pricing too late

The AI compute cost curve is pointing downward at a speed that makes three-year plans written twelve months ago look absurd. This is a measurable fact: the trajectory of cost per useful token over recent years.

The consensus treats the cost of compute as a structural barrier. I read it as a commodity in free fall.

This is a regime change. Anyone who books AI spend as a fixed three-year line item is building on a surface sliding out from under their feet.

The consensus has the wrong frame

90% of analysts are right about the present. They are wrong about the pace of change.

The consensus watches the training cost of the largest models and concludes AI will stay expensive. That number is a lagging indicator, good for telling the story of the past.

The metric that predicts reality is different: cost per correct answer. Here the trajectory diverges from training dramatically. Inference optimization cuts spend while quality rises.

The gap between these two numbers is precisely the space where the market gets the price wrong.

The cost curve, with its data points

A trajectory needs at least three points. Here they are.

First: photovoltaics lost roughly 90% of their cost between 2010 and 2020, according to the IEA. It is the template of every technology that follows a learning curve.

Second: the cost per token of frontier models has fallen by orders of magnitude in two years, while performance climbed. Third: algorithmic inference optimization adds a further multiplier to the descent.

An August 2026 preprint quantifies this third vector. The CoBa method reaches 85.13% macro accuracy using 49.1% fewer weighted tokens than a proxy that hits 85.20%, according to the paper on arXiv.

The same work matches best-of-16 majority voting to within 0.01 points of macro accuracy, while consuming 58.9% fewer tokens. The message is sharp: the same quality costs half.

The mechanism: routing instead of brute force

The mechanism matters more than the number. This is why the trajectory accelerates.

The previous generation spent compute indiscriminately: more samples, longer chains of thought, heavier evaluators. CoBa treats reasoning as an allocation problem. Every unit of compute goes where marginal value is highest.

The test covers 3,129 evaluations across MATH-500, AIME 2024/2025 and AMC 2023. Economic verification is applied at scale, and uncertain cases are routed to stronger verifiers. The result is constant quality at half the computational cost.

The cliff event: when adoption jumps

AI adoption will stop growing linearly. It will jump. Cliff event: the cost per GPT-4-quality answer reaches $0.001 per token by the end of 2026.

At that threshold the economics of inference change in nature. Applications marginal today become profitable en masse.

The reason for the date is causal, anything but conjectural. Three vectors converge: cheaper hardware, smaller models at equal quality, compute routing like the one described above. The product of the three multipliers produces the jump.

Three categories that will change shape by 2027

• Providers of generic AI capacity
• Vertical companies with proprietary data moats
• Procurement departments with multi-year compute contracts

The first sell what is becoming a commodity. Their margin migrates toward the application layer, and their valuation will follow.

The second capture the value abandoning the model layer. This is the advantage of the next decade, built on the proprietary data that feeds the application.

The third sign prices the market will halve twice before the contract expires. The trap is classic, and the bill arrives on schedule.

My position, and what would falsify it

My position is clear: foundation models commoditize within 18-24 months, and value concentrates entirely in vertical applications with proprietary data.

Whoever buys generic capacity today is purchasing a future commodity. Whoever builds vertical data moats is building the structural advantage of the next cycle.

What would change my read? A stagnation of inference efficiency gains for four consecutive quarters. That data point would close the thesis. Until then the direction stays unequivocal.

The prediction

Explicit prediction. By the end of 2026 the cost per token of a GPT-4-equivalent model reaches roughly $0.001, and CoBa-style inference optimization becomes standard practice in local reasoning systems.

Confidence: high on the technology, medium on precise market timing. Horizon: twelve months.

Kill signal: four consecutive quarters of flat or rising cost per correct answer. That signal would falsify the trajectory.

What this means for decision-makers

For the CTO: reassess your inference stack now. The generic capacity locked in today becomes ballast tomorrow.

For venture capital: the seemingly impossible bet is the thin vertical application on a commoditized model. The data support it. For the Chief Strategy Officer: any three-year plan that assumes expensive compute assumes a world on its way out.

For procurement: negotiate price-adjustment clauses indexed to the curve. Those who get them protect the budget from the coming collapse. Further analyses from this desk remain available in the blog.