Uber Executive Questions Return on AI Spending Uber’s aggressive push into artificial intelligence is colliding with a harder question from inside the company: if AI costs are soaring, why aren’t the benefits easier to see?

In early 2026, Uber ramped up its use of large language models such as Anthropic’s Claude Code, part of a broader Silicon Valley trend of so‑called “tokenmaxxing” — running as many AI tokens as possible to boost productivity and experimentation. By April, Uber had already blown through its annual Claude Code budget, a moment COO and president Andrew Macdonald later described internally as “head‑exploding.”

As spending spiked, Macdonald began pressing engineering leaders on whether rising token consumption was really translating into better products. He concluded that higher usage “did not translate into a proportional increase in useful consumer features,” saying, “That link is not there yet… it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25% more useful consumer features.’” In a separate interview, he warned that without that link, comparing AI costs to human headcount “becomes harder to justify.”

The remarks, published in late May, quickly resonated beyond Uber. Business Insider framed them as emblematic of a wider shift, reporting that CIOs across industries feel their AI budgets are “starting to max out with nothing to show for the costs incurred.” Another report described a brewing backlash against tokenmaxxing as engineers and managers complain that “tokens got burned for millions of dollars without any real significant ROI to show for it.”

Yet many in tech still see this as a transitional phase rather than a failure. Commentators quoted by Business Insider argue that AI is “still very new” and that clearer productivity metrics will emerge as companies refine how they deploy the tools. Macdonald himself has suggested that over “coming quarters and years” the payoff from AI investments may become more obvious, even if today “it’s hard” to measure.

For now, Uber and its peers are caught between two timelines: the immediate reality of billion‑dollar AI bills, and a longer‑term bet that those costs will eventually translate into measurable gains.

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