Quick Overview

  • Uber Technologies' engineering team has surpassed its finance team as the primary department for AI applications within the company. A senior finance executive said Thursday, following reports that the ride-hailing company is under pressure due to the high costs of AI tools used for automating software coding.
  • Uber's Chief Financial Officer Tiho Nedkov noted that over 90% of Uber's finance professionals now regularly use AI tools, highlighting the team's rapid integration of technology into daily workflows, even though the engineering team has now become the largest deployer.
  • "Finance actually led within Uber for a few years... at least until agentic coding came along," Nedkov said at Gartner's annual CFO conference in National Harbor, Maryland.

Deep Insights

These remarks come amid reports of internal unease at Uber over AI spending, driven mainly by concerns about coding tools and token-based usage patterns that are pushing internal costs beyond expectations. This development highlights a broader trend known as "token-maxxing," an industry term used to describe the intensive use of AI tokens.

Uber's internal concerns about AI spending were reported last month by The Information, which said Chief Technology Officer Praveen Neppalli Naga revealed that the company had exhausted its full-year AI coding budget within the first four months of 2026, mainly due to the rapid adoption of tools like Claude Code by engineering teams.

Chief Operating Officer Andrew Macdonald echoed the issue in a subsequent Rapid Response podcast interview, saying it was difficult to justify the return on investment from rising AI token usage. "That correlation doesn't exist yet," he said, adding that although usage statistics have risen sharply, it is hard to establish a direct link between these metrics and improvements in consumer services.

According to Forbes, Uber's deployment of Claude Code accelerated rapidly among engineering teams, with adoption rising from 32% in February to 84% in March. By spring, about 95% of Uber engineers used AI tools monthly, and about 70% of committed code originated from these systems, the report said. This resulted in an average cost of about $150 to $250 per engineer per month, with heavy users reaching up to $2,000.

Nedkov said that although the engineering team leads overall, AI adoption in finance remains strong. Uber's finance organization has driven AI-powered automation across multiple workflows. According to slides used in Nedkov's interview with Gartner's Mallory Barg Bulman, over 96% of invoices are now processed by the technology with accuracy exceeding 95%, reducing manual review time. Contract reviews for revenue recognition have been fully automated, while turnaround time for handling multilingual regulatory notices has been reduced by about 70%.

Nedkov also highlighted a "data agent" aimed at enabling "conversational data analysis" and ensuring company systems keep pace with rapid advances in agentic AI technology. In an interview following his remarks at the Gartner conference, Nedkov said Uber has "basically democratized" the use of AI tools, allowing employees to build and deploy applications without major barriers. He said this approach has brought "a lot of innovation and productivity," while also driving a significant surge in output, which he acknowledged could pose "source of truth" challenges.

"But if you have guardrails, you can control that," he said, arguing that governance and experimentation can coexist. "You have a playground-like environment where people can develop these things." Although most Uber finance professionals are using AI, he said the team is still in the "early stages" of AI adoption. "There's no such thing as 'token-maxxing' in finance," Nedkov said, "but you have to be vigilant about it and ensure limits are set."

Nedkov declined to comment on reports about Uber's 2026 AI budget. The company did not immediately respond to a request for comment.