Here's a problem most companies haven't hit yet, but will: Uber just burned through its entire 2026 AI coding budget in four months because engineers actually used the tools.
The company deployed Claude Code to its engineering teams in December 2025. By April 2026, the annual budget was gone. Not because the tools failed - because adoption took off faster than anyone forecasted, and the usage patterns were dramatically more expensive than the spreadsheet assumed.
The numbers tell the story. 95% of Uber engineers now use AI tools monthly. 70% of committed code originates from AI. Monthly costs per engineer are running $500 to $2,000, depending on how heavily they lean on the tools. For context, that's 5-20x what most companies budget for a standard SaaS seat license.
Uber's CTO told the r/artificial community they're "back to the drawing board" on AI budgeting for next year. Which is a polite way of saying: we thought this would cost X, it cost 4X, and we have no idea if next year will be 4X again or 16X.
What makes this interesting isn't that Uber can't afford it - the company's R&D spend is $3.4 billion annually, so even at the high end this is manageable. It's what the overrun reveals about how AI coding tools actually get used in production environments.
Most enterprises are still treating AI coding tools like seat licenses: fixed cost, predictable renewal, maybe a small overage if usage spikes. Uber's experience suggests that's the wrong model. The actual cost driver isn't seat count - it's adoption intensity. A team using Claude Code for multi-step agentic workflows generates orders of magnitude more API spend than one using Copilot for autocomplete.
The companies that haven't hit this wall yet probably will. For Uber, an unforecast 4x budget overrun is annoying but survivable. For a smaller engineering org, the same overrun could force choices between AI tooling and hiring.
The interesting question isn't whether this is worth the cost - Uber clearly thinks it is, or they'd restrict access. It's whether the productivity gains have been measured in a way that's comparable to the spend.
Because right now, most of the justification for AI coding tools is vibes. "Engineers ship faster." "Velocity is up." "The team loves it." Those things might all be true. But when your AI tooling budget is approaching the cost of several senior engineers, you need better answers than vibes.
The technology is real. The productivity boost is real. The question is whether anyone's actually done the math on whether it pencils out - or whether we're all just flying blind into a future where half the engineering budget goes to API calls.
