In many organizations that adopt agents and language models, a clear pattern appears: about 1% of users consume roughly 90% of the resources. A few people run most of the prompts, build most of the automations, and drive most of the costs. Yet these resources are often not used well—they don’t generate new value or new “resources” for the organization.

This is the usage pattern many companies see with agents and language models. A small number of users use them very heavily. It costs a lot. And there is no obvious, measurable benefit. You get bills and usage graphs, but not clear gains in efficiency, revenue, or quality.

So what are these heavy users actually doing? Are they very inefficient? Are they creating large amounts of unnecessary automation? Are they mostly testing and experimenting? Or are they simply wasting resources? In many cases they are building and trying things without clear goals or success criteria. They create workflows that nobody else adopts, or they use agents as personal helpers without turning that into shared improvements for their team.

When heavy usage doesn’t create new value, the organization ends up funding exploration without getting much back. The agents and language models are used a lot, but not in ways that change core processes or free up time. Experiments remain experiments, and the rest of the organization barely notices.

A useful way to test this is to ask: what happens when these heavy users stop using agents? If that 1% stopped tomorrow, would the need for resources fall sharply? Would the organization’s demand for agents almost disappear? If so, it suggests that most usage was driven by a few enthusiasts, not by broad, sustainable use cases. The tools were not embedded deeply into everyday work.

To understand if you have this problem, look at how usage and costs are distributed. Identify who the heavy users are and what they use agents for. Check whether their work leads to concrete outcomes, such as time saved, fewer manual tasks, or improved key metrics. Ask how much of their activity becomes shared, production-ready workflows, and how much stays as personal experiments.

Heavy users can be valuable—they are often the ones who explore possibilities and build first versions. But their energy needs direction. Instead of open-ended usage with no clear benefit, their work should be tied to specific problems and processes. Experiments should either be turned into stable solutions or consciously stopped. Otherwise, the organization risks a situation where 1% of users burn 90% of the budget, without creating new value in return.

The goal is not to shut down these users, but to make sure their efforts generate real, lasting benefits. When agents and language models are used in ways that create new resources—saved time, better decisions, improved workflows—the high consumption can be justified. When they are not, it is a sign that usage needs to be aligned more closely with the organization’s actual needs.