Tokenmaxxing is Goodharting

Coding agents and reasoning models let individuals consume many more LLM tokens than they could a year ago. It’s now easy for a single engineer to spend thousands of dollars in daily token usage. This is being actively encouraged through the recent memetic spread of “Tokenmaxxing” – the idea that if you consume more tokens, you’re more “AI native” and therefore producing more valuable output. Tokenmaxxing is not The Way. Plainly, it’s a textbook instance of Goodharting. Token leaderboards come from an understandable short-term instinct to shift habits towards more AI usage, but direct optimization in this fashion inevitably overshoots into wasteful spending. Token-usage-as-target means token consumption ceases to be a useful metric. Per-engineer token usage is, admittedly, useful as a diagnostic when engineers are dramatically and systematically underusing AI. However, the leaderboard version of token usage is likely actively harmful. This is analogous to how “lines of code merged” or…