The ghost of a 19th century English economist may be haunting yet another part of the AI boom.

In 1865, William Stanley Jevons observed that when the Watt steam engine made coal use more efficient—decreasing the amount required to a task—coal consumption actually skyrocketed. More than 150 years later, one economist is citing this phenomenon, dubbed Jevons paradox, to explain why the cost of AI will continue to creep up.

Despite the price of a single token dropping more than 90% since 2023, spending on large language models has doubled since late last year, according to the Silicon Data Token Expenditure Index. Essentially, token price—or the cost to process the most basic unit of AI—has gone down, but companies are spending more than ever on AI. Apollo chief economist Torsten Slok said it’s yet another example of “Jevons paradox in action.”

“As tokens get cheaper, companies don’t spend less but instead run more AI agents, automate more workflows and generate more code, pushing aggregate expenditure higher even as the unit cost of intelligence collapses,” Slok wrote in a recent blog post.

The cost of tokens has become a major concern for companies racing to leverage AI. The trend of “tokenmaxxing,” in which employees blitz to increase their AI use, has emerged as companies like Meta and Amazon incentivize the technology’s use. However, the deployment of AI just for the sake of it is proving unsustainable. Uber president and chief operating officer Andrew Macdonald recently said the rideshare company burned through its entire AI budget in the first four months of the year amid the company’s increasing use of Claude Clode. Bloomberg reported the company has now capped monthly AI spending to $1,500 per employee.

Others are reckoning with AI—which tech leaders promised would boost productivity—still costing more than human labor: “For my team, the cost of compute is far beyond the costs of the employees,” Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently said in an interview with Axios.

Jevons paradox and the AI boom

With token prices dropping as new AI models become more efficient, the era of tokenmaxxing may be over, but that won’t necessarily solve companies’ AI budget crises.

A group of Bain and Co. analysts confirmed Slok’s point in a brief last week, finding that while token costs were halved from December 2024 to December 2025, the tokens consumed grew by 450% in the same period.

The analysts attribute this paradox to companies feeling compelled to upgrade their AI models to take advantage of the upgraded technology, rather than stick with their current models and pocket the savings. Moreover, tokens per query have increased as agents become more capable of complex tasks. And to Slok’s point, once a team believes AI can complete these more significant tasks, they will ask more of the technology and subsequently use more tokens.

“The models get cheaper. The usage gets heavier. The bill stays stubbornly high,” the brief said.

Token costs are just one area of the AI boom where economists have seen paradoxical economic data emerge. Slok similarly found that despite AI being able to automate 86% of tasks for customer service workers, employment for call center workers in the Philippines has actually nearly doubled over the last decade. A similar trend can be seen in radiologists, another profession deemed endangered because of AI’s ability to automate it. The number of radiologists in the U.S. has actually increased by 10% in the last 10 years.

“Lower cost per interaction does not mean fewer interactions,” Slok wrote last month. “It means more customers served, more channels opened and more markets worth reaching. The technology that was supposed to shrink the industry is fueling its expansion.”

Bain and Co. sees an AI future where a company’s operating expenses come 70% from human headcount, and 30% from tokens. In order to make this shift sustainable, analysts warned companies will have to navigate uncertainty regarding costs of AI by not just creating a budget for AI spend, but determining the true financial returns from employing certain AI tools to assess if the tokens are worth it.

“The opex shift from headcount to tokens isn’t a budget problem,” analysts said. “It’s a structural transformation.”