The pattern described here is one we see far too often, and it deserves more than a passing acknowledgment. When an AI system produces outputs that are technically flawless but contextually meaningless, the problem is not the model or the data. It is the invisible scaffolding of assumptions that no longer holds. The user who posted this identified something essential: the system keeps running, governance signs off, and the outputs still look valid. But the world has moved on, and the system has not. This is not a failure of execution. It is a failure of reflection, and it is far more dangerous because it is so easy to miss.
For those of you managing production systems, the practical takeaway is uncomfortable. Tightening controls, reducing overrides, and increasing monitoring will not solve this. In fact, these responses often make it worse. They lock the system further into the same outdated logic, reinforcing behavior that is already disconnected from reality. The user's framing of a "Formalisation Trap" is apt because it captures how meaning gets embedded in structure, then outlives its usefulness. The structure becomes the authority, even when the meaning has drifted. You end up with decisions that are defensible on paper but wrong in practice, and the harder you try to enforce compliance, the more you cement the error.
What this means for your team is that you need to build in a different kind of review. Not just monitoring outputs for anomalies, but questioning the assumptions that make those outputs meaningful in the first place. When was the last time you asked whether the rules your system follows still reflect the reality they were designed to model? Not whether the data is clean, or the model is accurate, but whether the underlying premises hold. This is harder work because it is not a technical task. It is a judgment task. It requires humility to admit that what was true six months ago may not be true today, and it requires the courage to act on that admission before the system does more harm.
The user is right to ask if others have seen this pattern. It is not isolated, and it is not going away. The fix is not more control. It is more scrutiny, applied to the assumptions themselves. Build regular checkpoints where you ask what has changed in the world that your system is supposed to serve. If you cannot answer that question, your system is already drifting, no matter how clean your outputs look. The most productive step you can take is to treat your assumptions as a moving target, not a foundation. Because the moment you stop questioning them, you have already fallen into the trap.