Cost-Per-Correctness, Not Cost-Per-Token: The Unit Metric Your Bill Won't Tell You
A team I know cut their inference bill 40% last quarter by migrating their support-email triage flow from a frontier model to a mid-tier one. The CFO sent a thank-you note. Six months later, customer support headcount was up two FTEs and average resolution time had risen 35%. Nobody connected the dots, because the dots lived in different dashboards: the inference bill on the platform team's, the support load on the operations team's. The migration looked like a win on the only metric anyone was tracking. The metric was wrong.
This is the cost-per-token trap. Your invoice tells you what you spent on tokens. It cannot tell you what you spent per correct task, because the inference vendor has no idea what "correct" means in your domain. They sold you raw compute. You bought outcomes — or thought you did. The gap between those two units is where AI unit economics quietly comes apart, and the team that doesn't measure the right denominator is running half the equation and shipping the other half blind.
