Measuring productivity purely by AI token usage misses the point. There are far better metrics to monitor.
The conversation about AI and enterprise productivity has drifted into a peculiar place. Organizations that spent years refining outcome-based performance frameworks are now, in some cases, reverting to curious metrics as a measure of an employee's "productivity." Not how much code they write or how many customer resolutions they manage - purely AI usage time and token consumption. The logic, presumably, is that more AI use equals more value delivered.
This is simply untrue, and two recent examples illustrate the problem well. Starbucks has structured a quarter of tech workers' bonuses around department-wide AI adoption goals, defined as using an AI assistant multiple times a week. Certainly, the intent is to incentivize adoption - but the effect, if history is any guide, is to incentivize the appearance of adoption.
Separately, Uber's engineering teams burned through their entire 2026 AI budget by April after ranking engineers on internal leaderboards based on...
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