Measuring AI Token Consumption Is the New Lines of Code
- 10 Mar, 2026
I once worked at a SaaS company where the CEO lamented the lack of blog posts touting new features. I understood his point (sort of) but mostly thought: why are you focused on output rather than outcomes?
I heard that CEO’s voice once again reading a recent HBR article about AI burnout.
Particularly this passage: “Firms are incentivizing employees to build and oversee complex teams of agents — for example, by measuring and rewarding token consumption as a proxy for performance.”
It’s like measuring a writer’s productivity by how many words they typed. Or a developer’s value by lines of code. Or a chef’s skills by the number of butter sticks they use. Meta actually does this — counting lines of AI-generated code (not butter) as a performance metric. Really? We’ve been down this road before, right?
But bad metrics don’t just miss the point. They make things worse. When you reward token consumption, people consume more tokens. They’ll run more agents, more tools leading to more agent oversight. And that’s where the real cost shows up.
The study found that after three AI tools running simultaneously, productivity actually dips. More tokens, more agents, more tools — less clarity, worse decisions, and a 39% higher intent to quit among the people experiencing what the authors call “AI brain fry.”
Here’s the kicker: when AI was used to replace repetitive work, burnout scores dropped 15%. The people who benefited most weren’t the ones consuming the most tokens. They were the ones who offloaded the tedious stuff and spent their freed-up time on work that actually mattered.
The lesson isn’t complicated. Reward the outcome, not the consumption. Otherwise you’re just incentivizing people to burn through cognitive capacity for the appearance of productivity.
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