AI cost discipline is becoming an engineering leadership problem.
In real engineering organizations, cost problems rarely arrive all at once. They start as small exceptions: a premium model for a trivial task, a long context window where retrieval would do, repeated prompts with the same prefix, an agent loop nobody is measuring.
The latest signals make this hard to ignore. AI spend is being questioned at the board level, and infrastructure teams are responding with inference routing, prefix-aware caching, token compression, and workload segmentation. That is the right direction.
AI adoption should not be measured by usage — it should be measured by useful work that survives review, deployment, and operating cost. The mature AI stack optimizes for cost per accepted outcome, not raw consumption.