Why it matters
Large internal AI adoption numbers do not prove ROI. This is the operational correction many teams eventually face: once agent usage is widespread, leadership still needs evidence that the extra spend produces better shipped outcomes.
Tokenmaxxing read
Tokenmaxxing fails when usage volume becomes the proxy for progress. Replace leaderboard logic with delivery metrics such as merged work, incident reduction, cycle time, and cost per accepted change so that high-burn agent sessions are judged by output, not activity.
Source takeaway
Tech Brew’s May 27, 2026 write-up says Uber leadership is questioning whether heavy Claude Code usage is producing useful features, even after broad internal adoption and an aggressive push toward AI-assisted engineering.
