Uber believes it’s found a solution to its AI spending problem after it blew through its budget for the technology in just the first few months of the year.
In an interview with The Information earlier this year, Uber Chief Technology Officer Praveen Neppalli Naga admitted he went “back to the drawing board” on allotted spending after the rideshare giant encouraged employees to use its tools, particularly Anthropic’s Claude Code, as much as possible, even devising “leader boards” to rank software engineers on their usage.
The blitz was part of a trend of “tokenmaxxing,” or companies incentivizing workplace AI use, only for many to back off from the practice as they found it wasn’t offering the returns on investment to justify the rapid spending. While Uber was no exception, Naga said the company has now figured out a better way to deploy AI without breaking the bank.
“We’re seeing some very interesting trends on AI costs,” he wrote in an X post on Wednesday. “I think it’s another signal that we’re coming to the end of the so-called ‘tokenmaxxing’ era.”
Uber quadrupled the number of employees who use frontier AI tools, Naga explained, which brought down the cost per token. It was able to do this by improving prompt caching, as well as adjusting its default model setting, evaluating new models for efficiency, and allowing engineers to see their AI usage and costs per hour.
“You might expect costs to rise as adoption accelerates,” Naga continued. “We’ve seen the opposite. Not because we’ve restricted access, but because we’ve treated efficiency as an engineering problem rather than a budget problem.”
AI’s rising ROI stakes
The stakes are increasing for companies to deliver on their massive AI investments. Last month, Jim Reid, Deutsche Bank Research Institute’s global head of macro and thematic research, warned AI productivity gains were still years away.
Profit margins for the Magnificent Seven swelled from 15% to 25% between the first quarters of 2023 to 2026, while the rest of the S&P 500 index saw only 10% margin growth over the same period, indicating little widespread returns on investment in AI outside of the immediate tech sector.
As of May, Uber was still trying to unlock the innovation AI promised.
“That link is not there yet,” Uber President and Chief Operating Officer Andrew Macdonald said in an interview on the Rapid Response podcast at the time. “Maybe implicitly there’s more that is getting shipped, but it’s very hard to draw a line between one of those stats and ‘Okay now we’re actually producing like 25% more useful consumer features.’”
The threat of Jevons paradox
Even as Uber unlocks strategies to lower the cost per token to make its AI use more sustainable, it risks falling into a trap economists have warned about: Jevons paradox, in which spending on a resource, in this case tokens, actually increases even as its cost decreases.
Named for 19th century economist William Stanley Jevons, the phenomenon originally referred to his observation of coal consumption skyrocketing in 1865, despite the Watt steam engine making coal use more efficient.
The same dynamic is playing out today with AI: According to the Silicon Data Token Expenditure Index, the price of a single token dropped more than 90% since 2023, but large language model spending has doubled since late last year.
“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,” Apollo Chief Economist Torsten Slok wrote in a recent blog post.
A Bain and Co. brief published in June punctuated Slok’s claim. It found that token costs halved from December 2024 to 2025, but tokens consumed grew by 450% over the same period as companies upgraded AI tools.
Naga, for his part, noted a shift in company philosophy to put quality over quantity when it comes to token spending, but did not say if Uber was using more or less computing than earlier this year.
“This is the future of applied AI at enterprise scale,” he concluded. “The next phase, whatever we call it, will not be characterized by who spends the most tokens, but about how people use them as efficiently as possible.”
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