By Walter Donway, The Daily Economy
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Organizations often mistake measurable activity for meaningful achievement. AI productivity metrics confuse computation costs with added value.
A recent Wall Street Journal report on a workplace trend called “tokenmaxxing” offers a revealing glimpse into some of the confusion attending America’s AI boom.
Some companies, the Journal reports, are experimenting with measuring an employee’s engagement with AI by tracking “tokens”—the units into which the system converts text typed into prompts. Now, in some workplaces, it seems token consumption has become a badge of an AI user’s engagement, experimentation, or productivity.
This is a striking moment. During what often feels like a national celebration—or national heart attack—over the transformative productive potential of artificial intelligence, we are publicly debating if an employee’s value might be measured by the volume of text sent to and from a chatbot.
The controversy deserves more attention than its odd jargon suggests. It exposes a central uncertainty in the AI revolution: what, exactly, does productive use of AI mean?
Reporting in Built-In, Ellen Glover reports that tokenmaxxing “is taking much of the tech industry by storm… individuals are ranked on leaderboards based on how much they use AI, with generous perks and incentives encouraging them to push these tools to their limits… The assumption is that the more you use AI, the more productive you must be. Those who lean in the hardest will come out on top.”
She adds that some employees take advantage of the fact that now “systems use AI agents to work autonomously for hours on end, reviewing and editing large codebases and writing entire programs while their human users are out living their lives.”
Tokens are real enough. Large language models do not “read” language as humans do. They convert words, punctuation, fragments of words, and other text elements into tokens—standardized units processed mathematically. The more tokens used, generally, the more computing resources consumed. AI providers often charge by token volume. Tokens therefore matter to engineers, accountants, and software managers.
When tokens migrate from a technical unit used in billing into a measure of employee performance, however, we risk confusing the cost of computation with the creation of value.