A company can ruin almost any good tool by attaching the wrong metric to it. Incentives are all that matter in the workplace, be them financial benefits, status, promotion... Workers work with incentives. You and me too. Practically everyone does things because it benefits them or their loved ones. Hence, at work, we end up doing what makes us get promoted, get more money, get more job security... We're not the owners of the company, we're an employee. We look out for ourselves. That's ok.
AI usage in tech companies
When management starts celebrating token consumption, prompt volume, agent count, or daily AI usage, people will optimize for machine activity instead of useful results. If your job is at risk because you're flagged as refusing to use AI then... you use AI. A lot, specially when engineers are rewarded for using it more and more. That does not mean they are irrational. It means they are employees. Employees chase what leadership can see, especially when the visible thing carries rewards. Right now AI activity carries a lot of rewards
This is just KPI corruption in a new costume. Organizations know in theory that once a metric becomes a target it stops being a clean measure, then they forget the rule the moment the metric looks technical and future-facing. AI makes the amnesia worse because machine activity is easy to graph and easy to brag about. AI adoption is one of them.
The better scoreboard is harder and less flattering. Imagine a support team that proudly doubles its AI-assisted response volume. That sounds great until you notice escalations also rose because the first-pass responses were shallow and supervisors spent more time fixing them. A better metric is not "how many AI answers did we generate?" It is "did first-response time improve without escalation, rework, or customer frustration getting worse?" The same thing applies in engineering. Burning more tokens is meaningless if review time, defect rate, and rollback risk all get worse. How much impact did the engineering team really have anyway?
There is an objection worth taking seriously. Early in a rollout, usage metrics can matter. If nobody is touching the tool, there is no adoption story at all. Fine. But temporary experimentation metrics have a bad habit of becoming permanent vanity metrics. Once status and evaluation attach to visible AI activity, the organization starts manufacturing activity to feed the scoreboard.
That is how useful tools become bureaucracy. Employees start prompting when they should just decide on their own. Leaders start asking for agent plans because agent plans look modern. Teams optimize for measurable AI surface area instead of actual cost, quality, and delivery. The institution has simply found a new way to waste money while congratulating itself.
This used to be a solved problem. Management used to reward engineers for writing more code. So codebases would end up growing dramatically and becoming brittle and bloated. The simplified metric already showed how you can't put simple metrics for performance in place and expect good results. As soon as you put them, people optimize for them. And that's ok, I do the same.