Tokenmaxxing

Tokenmaxxing is a term the New York Times coined in March 2026 for a competitive culture inside AI-heavy companies where employees race to consume the most AI tokens. The metric isn't output — it's burn. Some companies maintain internal leaderboards. At least two — Shopify (Tobi Lutke) and Meta — have made AI usage a factor in performance reviews.

Concrete benchmarks reported

• An OpenAI engineer processed 210 billion tokens in a single week.
• A Claude Code user at Anthropic runs a $150,000/month bill.
• Shopify and Meta evaluate employees on AI usage.

Why it persists

The pattern depends on a measurement gap. Token consumption is trivially observable; the value produced by that consumption is not. So the metric you can measure substitutes for the metric you care about. Combined with the ai-sycophancy-loop — agents structurally reporting success — the operator gets a continuous signal that more spend means more progress, with no opposing signal to correct it.

The author of ceo-ai-psychosis frames this bluntly: the leaderboard measures consumption, not output. They recommend killing the leaderboard and running a cross-analysis on tokens-per-feature against attributable revenue, on the prediction that the result is unflattering.

Adjacent patterns

• ai-great-leap-forward — the same substitution at the organizational level: AI usage itself becomes a KPI, divorced from whether it creates value.
• subprime-ai-crisis — the macro version: the entire revenue case for AI infrastructure depends on consumption that may not produce value.
• no-silver-bullet-llms — the inverse-correlation data: heavier AI adoption tracks with worse delivery and stability.