The mood around corporate AI spending is turning, and Uber is saying so out loud. The company’s chief technology officer, Praveen Neppalli Naga, says the era of tokenmaxxing is coming to an end.

The term needs a translation. Tokenmaxxing is the practice of spending ever more on AI, measured in the tokens that models consume, on the assumption that more usage automatically means more value.

Uber knows the habit from the inside. The company burned through its entire Claude Code budget for 2026 by April, an eye-catching sign of how quickly AI bills can balloon once teams are set loose.

That experience has bred caution and Uber’s leadership has started questioning whether all that spending is actually producing the productivity gains and new products it was supposed to unlock.

The company’s president put it starkly earlier this year. Andrew Macdonald said the link between higher AI spending and shipping successful features simply is not there yet, even as usage statistics soared.

The headline numbers on AI adoption, he said, make your head explode, and yet nothing had meaningfully gained traction in the products customers actually use.

So Uber is pumping the brakes. Rather than chase maximal usage, it is adopting a more disciplined approach, continuing to work with the big model providers but demanding clearer evidence of value.

Uber is far from alone in this rethink. Atlassian has begun putting its engineers on AI budgets as the cost of tokenmaxxing bites, a sign that the free-for-all is giving way to spreadsheets.

The wider evidence backs the skeptics. Study after study has found that most enterprise AI spending never leaves the pilot stage, producing demos and proofs of concept rather than shipping products.

The uncomfortable question is one vendors would rather avoid. There is a reason it is the question AI providers hope engineering leaders never ask, namely whether the output justifies the invoice.

The economics are catching up with the hype. GitHub recently froze new Copilot sign-ups because agentic usage blew past what its pricing could bear, a concrete example of the strain tokenmaxxing puts on providers too.

None of this means companies are abandoning AI. The shift is from unlimited experimentation toward efficiency, smaller and cheaper models, and a harder look at which use cases genuinely pay for themselves.

The timing matters for the whole industry. Model makers have justified enormous valuations on the assumption that enterprise AI spending only rises, so a heavy customer signalling restraint is a data point the market will notice.

It also reframes what progress looks like. If the next phase rewards efficiency over raw consumption, the advantage may shift from whoever has the biggest model to whoever delivers the most useful work per dollar.

That would suit buyers and unsettle sellers. Cheaper, smaller models and tighter budgets are good news for companies footing the bill, and a harder sell for providers whose revenue grows with every token burned.

Coming from Uber, the message carries weight. This is a company that spends heavily on technology and works closely with the leading labs, so its caution is not a laggard’s excuse but a heavy user’s verdict.

Skeptics will note the caveat, of course. Pumping the brakes is not the same as stopping, and Uber is still spending heavily with the big labs even as it preaches discipline about where the money goes.

The tokenmaxxing era was always going to meet a budget. Neppalli Naga is simply naming the moment when the industry stops asking how much AI it can buy and starts asking what it is getting in return.

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