Reports from earlier this month say that Uber spent a year’s worth of its AI budget in just four months, according to Bloomberg. This comes after the e-hailing and delivery platform announced that it had been using tools such as Claude Code and Cursor to help run and speed up the company’s code.
Praveen Neppalli, Uber’s CTO, explained in an earlier tweet:
“Agentic software engineering adoption is on fire at Uber. 1,800 code changes per week are now written entirely by Uber’s internal background coding agent, and 95% of our engineers now use AI every month across all the tools we track.
“This is a real reset moment for engineering; it’s one of the most exciting times to lead. This shift requires builders to be curious and hands-on. I’m incredibly lucky to be surrounded by a team that’s doing exactly that.
“The best part is that the strongest adoption isn’t being pushed top down from leadership announcements; it’s coming from engineers who are quietly experimenting, quietly shipping, and quietly pushing things forward.
“I love spending time with those engineers because there’s no substitute for being close to the work.
“Over the last few months, we leaned in hard, and the results have been phenomenal.
“The bigger shift: going agentic.
“84% of AI users are now working with agent-style workflows, not just tab completion. Claude Code usage nearly doubled in 2 months (32% → 63%), while IDE-based tools have largely plateaued.
“Engineers are moving from accepting suggestions to delegating tasks. Even within traditional IDEs, ~70% of committed code is now AI-generated.
“Background agents are writing code autonomously.
“Our internal background coding agent went from <1% of all code changes to 8% in just a few months. There is zero human authoring. Engineers review and approve, but the code is written entirely by AI agents.
“The role of the engineer is shifting – from writing every line to architecting systems and reviewing AI-generated code.
“More to come from the [Uber engineering] team in the coming days.”
Introducing The Employee Cap: What Does This Mean For Uber?
As a result of the budget being blown, the company decided to introduce a $1,500 monthly cap per employee, per platform.
In other words, employees are given that amount on each platform to control how much is spent on AI. This is monitored through a dashboard, and when the cap is hit, there are options to request more funds. While this doesn’t necessarily restrict spending, the company believes it’ll help keep responsible use in check.
The company told Bloomberg, “We think this is all a pretty straightforward way to responsibly encourage agentic AI adoption and experimentation at scale across the company.”
More from Artificial Intelligence
Sam Wilson, an AI expert calculated how much $1,500 per employee really is compared to his own spending habits, for context. He wrote on his weblog:
“If we assume two actively used tools per engineer that’s $3,000 * 12 = $36,000 cap per engineer per year. Levels.fyi lists the median yearly compensation package for Uber software engineers in the USA at $330,000.
“That means each employee’s AI spending cap is ~11% of that median compensation package.
“I noted that my own token usage comes to about $1,000/month against each of Anthropic and OpenAI – which currently costs me just $100 per provider thanks to their generous subsidised plans for individual subscribers. Those plans are no longer available to larger companies like Uber.
“Their new policy means if I were working at Uber I’d still have ~$500/month of tokens to spare for each of those tools, given my current usage patterns.”
Are Companies Spending Too Much On AI?
The argument here isn’t as simple as “companies are overspending on AI”. Jobs have been cut and hiring has been slowing as more companies make room for AI in their budgets (which has cost people their jobs), but the idea that AI helps companies save on labour costs doesn’t quite stand when AI budgets are being burned.
This news makes me wonder whether companies are starting to spend more than they can measure when it comes to AI’s return on investment specifically. We’ve spoken about how companies might be aimlessly giving into the AI hype by rushing to try out many different tools while they admit to seeing no long term gains. Using these tools becomes redundant when no actual results are seen in the long run.
Martin Reynolds, Field CTO at Harness, also sees it this way. He said, “Uber capping AI spend is treating the symptom rather than the cause. The real issue is that many organisations are still measuring AI success through consumption rather than outcomes.
“Tracking AI usage made sense when adoption was the priority and engineering teams were sceptical. But many organisations never evolved beyond that stage. Now, with AI embedded in daily workflows, usage metrics start to distort behaviour.
“Employees are rewarded for generating more prompts, tokens, and model interactions – regardless of whether those activities create meaningful business value. As a result, business leaders can’t confidently say whether AI usage is driving results or simply inflating the bill.
“A $1,500 monthly cap doesn’t do much to tackle the root of the issue. Organisations need to build the type of cost visibility and tagging infrastructure that ties every AI query to a work order, a feature, or a business outcome – highlighting whether it genuinely contributed to customer experience or revenue growth.
“That’s how you make AI economically intelligent. Until organisations can measure value, not just spend, they’ll keep swinging between uncapped excess and blunt rationing – and leaving the real ROI on the table.”
