Enterprise Adoption Is Slower and Stickier Than the Demos Suggest

The headline number everyone fixates on is training compute, but the margin story is increasingly about inference. As model providers push cheaper, faster variants, the cost of serving a query has collapsed by roughly an order of magnitude in eighteen months — and that changes which products are viable to build on top of them.

Watch the depreciation schedules, not just the capex line. Hyperscalers are quietly extending the useful life they assign to servers, which flatters near-term margins even as the underlying hardware ages faster in AI workloads than the accounting assumes. When those schedules snap back to reality, the earnings hit arrives all at once.

1. There is a persistent gap between what a model can do in a demo and what an enterprise will actually deploy.
2. Procurement cycles are long, security reviews are longer, and the switching costs — once a workflow is embedded — cut both ways.
3. The lesson is that adoption is slow to arrive and slow to leave.