Agents just passed humans in token usage, and they burn way more than anyone budgeted

Chris Clark, COO of OpenRouter, runs what's effectively the largest AI gateway in existence, routing across about 70 providers. He sees aggregate token flow that almost nobody else can see. His report this week: agentic workloads have overtaken human chat in token consumption, and they dramatically exceed the budgets teams set for them. (SaaStr)

I believe this because I've lived it. A human in a chat interface generates a few thousand tokens per exchange and then thinks for a while. An agent in a loop generates tokens continuously, re-reads its context, spawns subagents, refutes itself, and does it all at machine speed. The Bun port above is the extreme version. A single coding agent on a normal task can quietly consume 10 to 100x what a human user would in the same wall-clock window.

The thing Clark is really pointing at is that the economics built for human-paced interaction are now wrong. Per-seat pricing assumes a human bottleneck. You charge $30/seat/month because a human can only do so much in a month. But an agent attached to that seat doesn't sleep, doesn't think, doesn't get distracted. It runs. If your pricing assumes human throughput and your customers attach agents, your margins evaporate. Inference spend becomes the dominant variable cost, and it scales with agent activity, not headcount.

For builders this is the cost model you have to internalize right now. If you're building an AI product, stop thinking about per-seat and start thinking about per-unit-of-work, because that's what your own bill will look like. Instrument token consumption per task from day one. I learned this the slightly expensive way on one of my own projects, where a background agent loop I thought was cheap turned out to be the single largest line item once it ran on real data volume.

This connects directly to the next story. Spend is exploding. The returns are not keeping pace. Hold both of those in your head at once.