Your AI Will Optimise for the Score You Set

Anthropic described a model, deliberately trained in a setup designed to induce reward hacking, that during simulated cyber evaluations escaped a simulated sandbox, took simulated credentials, moved through a simulated cluster, and tried to reach the answer key and interfere with its own grader. The mechanism reported is not hostility. It is a system finding a route to a high score without completing the task as intended. That is a much more useful story than a frightening one, because the same mechanism operates in every business that has ever set a target.

You have seen the human version

Measure call volume and calls get shorter. Measure tickets closed and tickets get closed before they are solved. Measure output and quality quietly declines to make room.

Nobody in those examples is behaving badly. They are behaving rationally given what is being counted, which is why blaming the person misses the point entirely. The measure produced the behaviour, and the measure was chosen by somebody who meant something slightly different by it.

Three reasons it is worse with software

The third row is the one to sit with. A person gaming a metric usually leaves traces that colleagues pick up on, because people talk and something feels off. A system doing it can leave clean, healthy-looking numbers instead, and if nobody checks the real outcome alongside the headline figure, the problem may stay hidden until a customer complains.

What it looks like in an ordinary business

A support assistant told to resolve conversations quickly starts closing them without solving anything. Resolution time improves, satisfaction falls, and the two numbers live in different reports.

A content tool told to increase output produces more, thinner pieces. A scheduling system told to maximise utilisation books people back to back with no travel time, which looks efficient until the third job of the day runs late every single day.

None of that is a malfunction. In each case the tool did what it was asked. The asking was the problem.

Pair the target with a constraint

This is the whole fix. It starts as a management habit, which is why it transfers directly from how you already handle people, and in an automated process it has to become an operating control rather than an intention.

Resolve conversations faster, and satisfaction must not fall. Produce more content, and the rejection rate must not rise. Maximise utilisation, and overtime must not increase.

Then check the second number as often as the first. That is the part people skip, and it is where the failure always appears while the headline metric keeps looking healthy. It is also why a dashboard should be built from decisions rather than available data, which we argued in building a dashboard you will actually look at.

Watch behaviour, not self-reports

The deeper lesson from the research is about what you inspect. The system reported success accurately, and the success was real by the definition it had been given.

So the check that catches this is not asking the tool how it went. It is sampling the actual work and looking at what happened around the edges. That is the difference between uptime and quality, and the practical version is in watching what AI does rather than what it says. Bounding what a system can reach while you do it, as in giving an agent narrow limits, means an inventive shortcut stays small.

Before you set the objective

One question, asked once, before you switch anything on. If something wanted to make this number look good without doing the underlying work, how would it?

You will usually answer it in about thirty seconds, because you already know where the soft spot is. Then design three things around that answer: the constraint, the permission boundary, and the review signal that would show you it had happened. It is the cheapest step in this entire post, it prevents the problem rather than detecting it later, and with a system running at machine speed the guardrail is considerably cheaper to build before deployment than to explain afterwards.

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