Every technology market eventually produces its boilerplate. Cloud computing took roughly a decade to settle on familiar contract structures. The market for AI models appears to be doing it in three years.
Procurement executives at large buyers describe a convergence on a handful of non-negotiable terms: clarity on whether customer data trains future models, audit rights over model changes that could affect output quality, benchmark commitments tied to renewal, and defined exit provisions, including data return and transition assistance.
Buyers are writing the standard
Notably, the standardization is being driven from the buy side. Industry groups in financial services, health care and insurance have circulated model clauses, and vendors that once treated every term as bespoke now arrive with pre-approved language.
The motivation is workload as much as risk. Legal departments that negotiated a handful of AI contracts in 2024 are handling dozens now. Novel terms do not scale.
What remains contested
Two areas remain genuinely unsettled. The first is liability for model output, where vendors continue to resist meaningful responsibility for errors. The second is version stability: buyers want notice and testing windows before models change underneath them, while vendors want the freedom to improve continuously.
How those two questions resolve will shape the market's structure. If buyers win version stability, the market favors fewer, slower, more accountable vendors. If vendors win, buyers will respond the way they always have, by building evaluation muscle in-house and treating every model as replaceable.
Earlier Cranberry Journal coverage examined the Training Data Market Grows Up, AI Spending Moves From Experimentation to Infrastructure and Companies Should Disclose AI Use the Way They Disclose Auditors.
