Amazon employees are running AI agents in the background. Not to get work done. But to inflate their token consumption scores. Meta employees are doing it. Microsoft employees are doing it. The phenomenon even has a name now: tokenmaxxing. Workers gaming an internal leaderboard — burning through AI usage metrics that mean nothing — in order to satisfy managers who have confused the measure with the outcome. This is not an AI problem. It is a management problem as old as management itself. And it has a name: Goodhart’s Law. When a measure becomes a target, it ceases to be a good measure. The moment you attach a reward or a consequence to a metric, you have changed what people optimize for. They no longer optimize for the outcome the metric was meant to represent. They optimize for the metric itself.
The AI version of this failure is more insidious — because it comes dressed in the language of innovation. Companies are not calling these targets sales quotas. They are calling them AI fluency metrics. Adoption benchmarks. Token consumption targets. The framing is progressive. The logic is identical. And the damage goes well beyond wasted tokens. Workers are reporting that AI-generated mistakes are slowing projects, damaging equipment, creating financial waste. Experienced professionals are being relegated to fixing AI outputs rather than applying their expertise to solve real problems. And the most corrosive effect — the one that is hardest to measure — is the signal it sends about what leadership actually values. When employees are rewarded for hitting a usage number rather than producing a result, they learn quickly. Compliance is what gets noticed. Not excellence. Not judgment.
So here is the question I want you to ask yourself. What is your AI metric actually measuring? If the honest answer is how much our employees use the tool — you have a compliance regime, not a strategy. Forget the adoption rate. Ask instead what your business can now do that it could not do six months ago. Ask which leaders in your organization have used AI to make a decision that turned out to be genuinely better than the decision they would have made without it. Those are hard questions. They resist the comfort of a dashboard. But they are the only questions worth asking — because they are about the business, not the measure.
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