Uber's AI team ran out of budget in April. Their fiscal year started in January.
That sentence appeared on Hacker News and hit the front page in under two hours, accumulating hundreds of comments from engineers who recognized the pattern immediately. Not because Uber is uniquely reckless, but because the same story is playing out at organizations everywhere. The r/LocalLLaMA thread about compute cost frustration — 181 upvotes, hundreds of comments from engineers describing identical spiral — makes the same point from the other direction: whether you're paying for cloud inference or running your own GPUs, agentic AI costs are destroying budgets that looked perfectly reasonable when the procurement approval was signed.
I cut my own agent pipeline costs by 74% over six weeks using a routing architecture I'll show you here. The core insight is simple: you are almost certainly sending every task to the same expensive model regardless of complexity, and that single decision is costing you more than everything else combined.
According to Forrester's 2026 enterprise AI deployment survey, 22% of agent deployments now report negative ROI — not because the agents don't work, but because the infrastructure costs exceeded the productivity gains. The agents work. The bills are just bigger than anyone planned for.
