How the Padding Happens
The chain of logic is a management-textbook counterexample: the company wants to drive AI adoption, needs a quantifiable metric, and so fixes on the easiest thing to measure—"usage"; once usage enters the performance review, employees' rational response is to pad it—having AI summarize documents no one will read, or breaking code that one sentence could write into pieces for the agent to slowly generate. Token consumption goes up, the dashboard looks pretty, real productivity stays put, and the compute bill is burned for nothing.
Goodhart's Law Wins Again
"When a measure becomes a target, it ceases to be a good measure"—this law proves out fast and typically in AI adoption. Quite a few employees at other big companies in the comments claimed the same phenomenon, showing it isn't unique to Amazon but the common ailment of every organization managing AI transformation by dashboard. A truly effective measure should watch outcomes—defect rate, delivery cycle, rework volume—not actions. But outcome metrics are slow and hard to attribute, and management can't wait, so they keep counting tokens. After all this churn, the most expensive cost is probably the last shred of employee trust in the phrase "AI transformation."
via: Hacker News