Four forces that make rented intelligence fragile

Rented intelligence does not fail loudly. It is repriced, deprecated and reshaped underneath you.

Part I argued that intelligence has become an operating input rather than a piece of software. This part examines the four forces that make that input unstable when it is rented rather than owned.

2. Four forces that make rented intelligence fragile

The case does not rest on predicting which provider wins. It rests on four properties of the market that hold regardless.

2.1 Concentration without stability

Three providers hold roughly 88 percent of enterprise LLM API spend, and in the same market the leader has changed twice in three years, with the current leader moving from 12 percent to 40 percent share. High concentration is ordinarily paired with high stability, which is what makes it tolerable. This market has the concentration without the durability.

The response to that instability is the most revealing data point available. Open-weight models closed the capability gap from 174 Elo points in May 2023 to 49 points in March 2026 while getting dramatically cheaper — and their share of enterprise API spend fell from 19 percent to 11 percent. The binding constraint on portability is not capability and it is not price. Our reading, flagged as inference rather than finding, is that enterprises stayed on frontier APIs because they had built nothing that would let them switch.

2.2 Deprecation as a recurring operating cost

Model retirement is now scheduled rather than exceptional. One leading provider publishes minimum notice of six months for generally available models, three months for specialized variants, and as little as two weeks for preview models. Under that policy the model generation that initiated enterprise generative AI procurement in 2023 switches off in October 2026, along with every fine-tuned derivative built on it. Models released in late 2025 were retired inside twelve months. Deprecation does not arrive as a drumbeat; it arrives in clusters — eight notices in seven weeks, three of them on a single day.

Sovereignty does not eliminate deprecation cost, it contains it. If model selection is an abstracted routing decision inside your own layer, a deprecation is a configuration change plus a regression run. If it is implicit in a thousand prompts inside a vendor's tool, it is an unbudgeted migration project.

2.3 Price is not a stable unit of comparison

Almost no procurement process controls for tokenizer efficiency, which means per-token list price is not a reliable basis for comparing cost across model generations or vendors. Both things are true: the cost of a unit of intelligence is collapsing while the enterprise AI bill is rising, because consumption grows faster than unit price falls and the composition of that bill is set by the vendor rather than the buyer. Sovereignty converts an opaque, vendor-determined bill into a legible, buyer-determined one.

2.4 Regulatory divergence is a design constraint