Uber gave corporate technology leaders an expensive warning this year when it used up its full 2026 budget for Anthropic’s Claude Code within four months.
While the amount was not disclosed, and the overspend applied to its Claude Code budget rather than every AI project at Uber, the speed of the burn still exposed a wider problem – Companies can adopt AI tools faster than they can measure whether those tools are producing useful results.
In May, Uber President and Chief Operating Officer Andrew Macdonald said higher token use had not yet produced a clear, matching increase in useful consumer features. It was difficult, he said, to connect rising consumption with a claim that the company was shipping 25% more useful features.
This makes Uber’s experience more than an unusually large software bill. Instead, it shows what can happen when when companies go hard on AI adoption without first deciding how they will measure its value..
High AI Use is Not Proof of Value
Uber had reasons to push AI coding tools across its engineering teams. By the second quarter, nearly all its engineers were using them, and Chief Financial Officer Balaji Krishnamurthy said code output per engineer had doubled, according to the company’s Uber’s Q2 earnings call. He also cautioned that the figure carried more detail than a single productivity number could show.
The difference highlighted matters, because more code can show that developers are producing work faster, but the figure alone does not explain whether the code improved products, reduced errors, or delivered features customers found useful. Macdonald’s concern was that the company could see the consumption and output figures more easily than the final business result.
Why The Bill Grew So Quickly
AI coding tools often charge according to the number of tokens a model processes, and the bill rises as employees send more requests and as AI agents read files, generate code, revise their work, and complete longer tasks.
Reuters reported in June that token prices were falling while the cost of completing some AI tasks was rising, especially as wider use and more complex workloads can cancel out lower prices. This makes an annual AI budget difficult to forecast when a company measures employee adoption but does not closely track what each use case costs and produces.
How Uber Brought Spending Under Control
By August, Uber said its overall AI spending had remained broadly stable during the second quarter even as adoption increased. Krishnamurthy credited cheaper models, better default settings for different tasks, and clearer information that allowed employees to manage their own spending. Taken together, these steps suggest that Uber had shifted from encouraging use to making that use more efficient.
Uber also began pointing to results that were easier to measure. Its AI-powered Cart Builder on Uber Eats produced shopping orders that were often twice the size of carts built without the tool. The company also said its personalised destination suggestions correctly predicted where riders were going about 75% of the time, with the assistance of AI.
The Lesson for Enterprise Tech
Companies need to connect each AI tool to a defined job, a spending limit, and a result that matters. In this case, premium models can be reserved for difficult work, while cheaper models handle routine tasks.
Uber’s experience leaves enterprise leaders with a rule – scale AI only as fast as the company can measure its costs and results.
