A 50-year-old economics law explains why AI tokenmaxxing was always going to fail

Every productivity metric eventually gets gamed. Tokenmaxxing is just the latest example of a decades-old organizational trap

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In 1975, a British economist named Charles Goodhart scribbled a footnote during a Reserve Bank of Australia conference that would outlive most monetary policy debates: "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." He was talking about inflation targets. He could have been talking about token leaderboards at Meta $META.

The pattern Goodhart identified has a simple modern translation, popularized by anthropologist Marilyn Strathern in 1997: "When a measure becomes a target, it ceases to be a good measure." The saying is true when an engineer runs agents in circles, generates documentation no one reads, or asks a frontier model what to have for lunch just to get to the top of an internal AI-usage ranking. It also explains why this keeps happening, with different technologies, in different decades, to companies that should know better.

The tokenmaxxing era, in which companies pushed employees to consume as many AI tokens as possible, wasn't the first time a new technology produced a metric that looked like progress and measured something else entirely. The organizational failure mode it represents has been named, studied, and warned about for decades. That makes it worth understanding on its own terms.

The metric always looks reasonable at first

Psychologist Donald T. Campbell articulated a parallel principle around the same time as Goodhart. Campbell's law states that the "more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social........

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