Companies using AI coding tools are starting to confront the economics of heavy token usage, WIRED reports. The story follows businesses trying to understand whether higher model consumption is producing enough productivity to justify the spend.
That question is becoming central as AI agents and coding assistants move from pilots to daily work. Token bills can rise quickly when tools generate code, inspect repositories, retry tasks, or run in the background.
The practical challenge is not simply cutting usage. Teams need cost visibility, budgets, and outcome metrics so they can tell the difference between productive AI work and expensive automation loops.