A corporate fad of “tokenmaxxing” on artificial intelligence technology is hitting its limits as workplaces throwing AI at everything are seeing the costs rise without a similar spike in productivity. What started as tech industry-fueled springtime hype over squeezing as much AI-generated work as possible out of products like OpenAI’s ChatGPT and Anthropic’s Claude has shifted to a summertime backlash. “It’s very easy to create something you don’t need with AI,” said Vincent Gusdorf, head of AI analytics at Moody’s Ratings and author of a new report that recommends a more disciplined approach.
“Tokenmaxxing” refers to maximizing usage of tokens — the building blocks of generative AI that correspond to small pieces of text that an AI system reads or writes. Each token is about three quarters of a word. And there’s typically a limit to how many you can use, with pricier versions of AI products offering higher caps.
“As bills started to pile in, people realized that those new tools are quite expensive and you need to use them wisely,” Gusdorf said. Tech executives cast high AI usage as a badge of honor Just a few months ago, Silicon Valley executives were promoting high token consumption as a signal of high-performing employees. The stereotypical tokenmaxxer was staying up late — perhaps ignoring their significant other — while orchestrating an army of 24-hour AI agents performing work on their behalf.
OpenAI CEO Sam Altman said in May he was “excited to see what will happen with tokenmaxxing startups, both for how they work internally and the products they can build.” Nvidia CEO Jensen Huang said “if your $500K engineer isn’t burning $250K in tokens, something is wrong.” Facebook parent Meta had an internal competition rewarding token usage. The trend boosted revenue for leading AI large language model developers like Anthropic and OpenAI, but it fizzled as it became apparent it wasn’t necessarily the best strategy for everyone else. Microsoft CEO Satya Nadella has admitted that tokenmaxxing can be addictive but warned in a recent blog post that customers of those models are paying twice for AI, first in spending on tokens and second by feeding all their proprietary data to them.
While promoting Microsoft’s own approach, Nadella’s comments were unusual in the way he raised doubts about the data protection assurances of leading AI providers.
Summary from source