Gergely Orosz

2 items

New York Times · 2026-03-22 2026-03-27-w1

Tokenmaxxing: When AI Productivity Becomes Productivity Theater

Token consumption became the week's central metric, and it measures exactly the wrong thing. One OpenAI engineer burned 210 billion tokens in a week; a Figma user ran up $70K in Claude usage through a $20/month account; Anthropic is offering $1,000 of compute inside $200 plans, subsidizing at roughly 5x. The leaderboards tracking this volume are Goodhart's Law applied to inference: the moment consumption becomes the proxy for productivity, consumption is what you get. The $25 economic theory pipeline and the Karpathy Loop running 700 experiments in two days are the same phenomenon from the other side — generation so cheap it exposes that evaluation is the only part of the stack nobody has built. Every SaaS platform offering AI at flat rate is running a margin time bomb; every enterprise treating token volume as a progress signal is one measurement framework away from discovering they've been optimizing for nothing.

New York Times 2026-03-22-3

Tokenmaxxing: When AI Productivity Becomes Productivity Theater

Roose names "tokenmaxxing" — engineers competing on internal leaderboards for token consumption — but buries the only question that matters: nobody measures output quality. One OpenAI engineer burned 210 billion tokens in a week; a single Anthropic user ran up $150K in a month. The leaderboards track input volume, not output value. This is lines-of-code metrics reborn: Goodhart's Law applied to AI inference. The sharper signal is a Figma user consuming $70K in Claude tokens through a $20/month account, revealing that every SaaS platform offering AI at flat rate is running a margin time bomb. The companies that win this cycle won't consume the most tokens; they'll have the best ratio of useful output to tokens spent. That measurement layer doesn't exist yet.