Someone else's capital cycle is your renewal risk
Enterprise AI is being served from infrastructure funded by an investment cycle that analysts expect to run past a trillion dollars a year. Whatever happens to that cycle happens to your unit costs, and almost no AI business case has been tested against it.
The business case assumes today’s prices. It was built when the price per million tokens for a capable model was falling steadily, it extrapolates that trend, and it treats inference as an input cost with a downward slope.
That slope is not a law of nature. It is the visible surface of an investment cycle, and the investment cycle has a shape that people whose job is forecasting such things have opinions about. Barclays’ analysts have written that annual AI infrastructure spending from Western hyperscalers and AI labs could pass a trillion dollars before peaking later this decade, well above consensus at the time — their global outlook sets out the reasoning. Others disagree, in both directions. The disagreement is the point.
The claim: your AI unit economics are downstream of a capital cycle you have no visibility into, and the correct response is not a forecast but an architecture that survives being wrong about one.
Two ways to be wrong, and they are not symmetrical
| If the build-out continues | If it slows sharply |
|---|---|
| Capacity is plentiful and prices keep falling | Capacity tightens and discounting stops before it reverses |
| Providers compete for enterprise volume | Providers price for margin and the cheapest tiers are trimmed first |
| Capability improves at the pace of the last three years | Improvement continues but the frontier tier becomes a premium product |
| The risk is over-caution — building for constraints that never arrive | The risk is a cost base that cannot be reduced without rebuilding |
The asymmetry in the last row is the reason this is worth thinking about at all. Being wrong in the optimistic direction costs an organisation some engineering discipline it did not strictly need. Being wrong in the other direction means discovering that a system designed around a price cannot be operated at a different one, at the point where the price has already changed.
The three dependencies worth naming
Price. Most business cases are single-point. The useful version states cost per completed unit of work at today’s rates and at rates several times higher, and says at which point the case stops working. That number is the single most informative thing in the document and it is almost never in there.
Concentration. A capability wired to one provider through application code is a business dependency on that provider’s commercial decisions. The mitigation is not multi-provider redundancy for its own sake — that has real costs — but the ability to move, which is a gateway, a portable prompt layer and an evaluation set that can compare candidates.
Capability tier. Systems designed around the most capable model available are the most exposed, because that tier is where pricing power sits. A system that routes the easy majority of its traffic to a smaller model has already reduced its exposure, and has done so while saving money in the meantime.
At what inference cost does this system stop being worth running?
- Record that, and stop worrying about it. Not every system needs this analysis. True more often than the anxiety suggests, particularly for low-volume, high-value work.
- Build the routing and the gateway now, while it is cheap. This is where most high-volume assistant and enrichment workloads sit.
- That is the finding. Instrument first. The most common answer, and the one that makes every other question unanswerable.
What this looks like in practice
Every item on that list is worth doing for reasons that have nothing to do with market conditions. That is deliberate. A resilience measure that only pays off in a scenario you cannot predict is a hard thing to fund, and every one of these pays for itself in ordinary operation — which is why the right time to build them is while the argument is optional.
What this does not tell you
We have no view on whether the investment cycle is rational, sustainable or mispriced, and we are not qualified to have one. The analyst forecasts cited here are forecasts, they disagree with each other, and quoting one as though it settled anything would be exactly the error this practice tells clients to avoid.
Nor is this an argument for delaying AI work until the picture is clearer. It will not become clearer, and the organisations that build capability now will have the measurement, the evaluation sets and the operational knowledge that make any future decision cheaper. The argument is narrower: build so that a change in someone else’s pricing is a routing decision rather than a rebuild.
The reader who should act is whoever owns the AI business case. Add one line: the cost multiple at which this stops working. If nobody can compute it, that is the first piece of work, and it is a week — not a quarter.