Amazon workers under pressure to up their AI usage are making up tasks

Perverse incentives & Goodhart’s law

• Many see token-usage targets as a textbook case of Goodhart’s law: once “tokens consumed” becomes a goal, it stops reflecting real productivity.
• Comparisons are made to past bad metrics like lines of code, bug bounties, and LOC-based performance reviews that encouraged wasteful behavior.
• Some argue leadership mainly wants a green dashboard and “AI adoption” numbers to justify big AI investments or please investors.

How employees game token metrics

• People describe using AI for low-value work: auto‑docs, unit tests for everything, endless diagrams, or agents that churn nonsense and delete outputs.
• Internal leaderboards and implied links to performance reviews reportedly trigger a race to “tokenmaxx,” even at FAANGs and large enterprises.
• Some joke about tools specifically created to burn tokens, or chaining agents to maximize usage.

Debate on real productivity vs. busywork

• Supporters say forcing everyone to try AI accelerates discovery of genuine use cases; experimentation necessarily includes waste.
• Detractors argue trivial tasks done via AI are slower and far more expensive than known commands, scripts, or linters, especially when results must be reviewed for hallucinations.
• Some report modest or unclear productivity gains (e.g., slight PR/velocity increases despite huge spend); others claim dramatic speedups on their own teams.
• There’s disagreement over whether AI lets people “do things without knowing things” (a positive abstraction) or dangerously erodes core skills.

Management culture, fear, and metrics

• Commenters describe executive pressure, AI trainings/hackathons, and slogans like “AI revolution/era,” often feeling coercive and optics‑driven.
• Several note that anxious engineers, influenced by social media stories of AI-native orgs and firings, burn tokens to avoid being labeled laggards.
• Some compare the whole situation to RTO mandates, DEI fads, or Soviet-style central planning: top‑down quotas, dashboards, and box‑ticking.

Environmental and economic concerns

• Multiple posts criticize burning compute “for nothing” during a climate crisis, linking token quotas to data center expansion and energy use.
• Others highlight circular financial incentives: big firms invested in AI providers are effectively paying themselves by driving internal usage, with little clear ROI.

Overall sentiment

• Strong skepticism dominates, but a minority see structured “overuse” as a necessary, if clumsy, way to learn how AI can genuinely help.