The Rented Substrate
Almost every AI application in 2026 runs on rented inference from three companies. The dependency compounds with utility, and migration to portable inference is not optional.
An AI product that depends on a hosted model API is renting its substrate. The rental terms are unilateral: prices change, availability changes, behaviour changes between minor version bumps, and the renter has no recourse beyond switching providers — which is itself constrained because every other provider is offering a similar deal under similar terms.
The dependency is not symmetrical. The provider has many tenants and can absorb the loss of any one. Each tenant has at most a few providers and would lose its product if the rental ended. The party with optionality has the leverage. In most rental markets this would be regulated. In inference, it is not.
The situation compounds with utility. Each year a product runs on hosted inference, more of its codebase, more of its operator playbook, more of its eval suite gets written assuming the specific behaviour of the specific model. The cost of switching grows with use. The renter pays in proportion to lock-in, and the provider's leverage grows in proportion to the renter's success.
The alternative is portable inference: the same model behaviour reproducible on substrate the operator controls or can substitute. This is not the same as running open-weights models — open weights help, but substitution requires more than weights. It requires the surrounding harness to be substrate-agnostic: prompts that work across models, evals that don't bake in one model's quirks, data formats that survive a vendor change.
Migration is not optional because the providers know what they have. The price they charge today is the price at which the renter still has a product. The price they will charge once the renter has shipped a feature their customers depend on is a different price. The only escape from this dynamic is to be ready to leave.