Uber burned through its entire 2026 AI tools budget by April. Microsoft faced a similar crisis, pulling Claude Code licenses because the tool worked too well and people used it too much. Even OpenAI's chief executive officer (CEO), Sam Altman, has called token costs "a huge issue " for the company and its customers. "Tokenmaxxing," the tendency to burn through tokens without a clear link to business outcomes, has gone from an internal joke to a boardroom problem. Companies are responding by capping external AI budgets and pulling licenses.
But when teams try to escape these escalating costs by deploying models on their own infrastructure, they trade metered billing for a new hidden expense: infrastructure waste. And nobody is capping this second cost problem because most organizations don't even know how to measure it. Here's what's easy to miss. Per-token costs have dropped as much as 80% over the past year . That sounds like good news, and it is, until you look at what happened to total spending.
Agentic workflows, where AI loops through planning, tool calling, verifying, and correcting, burn through tokens at rates chat-based interactions never approached. According to Deloitte's 2026 TMT Predictions , inference workloads now account for roughly two thirds of all AI compute, up from about a third in 2023. Cheaper tokens didn't make AI cheaper. They made it easier to spend more. Usually whatever topped a leaderboard last month, or whatever a colleague dropped in Slack. Teams deploy it on graphics processing units (GPUs).
The configuration is a best guess because nobody has time to benchmark multiple different setups. The autoscaling is either too aggressive, too conservative, or non-existent. The quantization settings are whatever the last tutorial or blog post read used. Then the GPUs run, usually not very efficiently. The model could be significantly larger than what the task needs, or the hardware may sit underused because the deployment was sized for peak traffic that never showed up. This is infrastructure waste.