Aravind Jayendran

Uber gave 5,000 engineers access to Claude Code in December. By February, usage had nearly doubled. By April, the CTO told the company they'd burned through the entire annual AI budget. The adoption curve tells you everything about what happened. In December 2024, 32% of Uber's engineers were using Claude Code. By February 2026, that number was 63%. That's not a gradual rollout. That's a product so useful that engineers pulled it into their workflow faster than finance could model the spend. Uber has about 34,000 employees. Engineering is roughly 15% of that headcount, somewhere around 5,100 people. At enterprise API pricing, Claude Code runs $100 to $200 per developer per month on Sonnet alone. But that's the subscription math. The real number is token consumption, and Uber's engineers aren't building hello-world apps. They're building rider-driver matching algorithms, dynamic pricing engines, and real-time logistics across 70+ countries. Every one of those tasks eats context windows for breakfast. The scale of what these engineers are actually doing with AI is wild. 92% of Uber's developers use AI agents monthly. 65 to 72% of code written inside IDEs is now AI-generated. 11% of all pull requests are opened by agents, not humans. The company's AI code review system, uReview, analyzes over 90% of the 65,000 diffs Uber ships per week. AI-related costs at Uber are up 6x since 2024. CTO Praveen Neppalli Naga's quote was "I'm back to the drawing board." That's the CTO of a $144 billion company admitting that the tools work so well his team can't afford to keep using them at this rate. Here's the part nobody is pricing in. Anthropic's Claude Code hit $2.5 billion in annualized revenue by February 2026. That's up from $1 billion in November 2025. The fastest enterprise software ramp in history, and a huge portion of that growth is coming from exactly this pattern: companies deploy Claude Code, engineers love it, usage explodes, budgets evaporate. Uber won't be the last company to have this conversation. The average Claude Code developer burns about $6 per day. Multiply that across thousands of engineers running complex agentic workflows, spawning sub-agents that each maintain their own context windows, and the math compounds fast. One engineering team running Claude Code in automated CI/CD loops can drain a monthly budget in days. The CFO problem is now the bottleneck for AI adoption at the enterprise level. The technology works. The productivity gains are real. Uber's own data says 75% of AI code review comments are marked helpful by engineers. The constraint is that traditional annual budgeting was designed for tools with predictable per-seat costs, and AI coding agents have usage curves that look like cloud compute bills from 2015: exponential until someone notices. Every enterprise CTO is about to have the same meeting Praveen just had. The tools are too good to pull back. The costs are too unpredictable to ignore. And the companies that figure out token cost optimization first will have a structural advantage over every competitor still running annual budget cycles against exponential adoption curves.

I can't believe I still have to say this, but LLMs are OBVIOUSLY capable of reasoning. You can literally watch them reason IN PLAIN ENGLISH in front of your very own eyes. The cope around this is unreal.