The comfort trap is about effort: AI does the work, you watch, the skill atrophies. That one is at least visible — you notice when you stop writing your own drafts.
The calibration trap is quieter. It’s not about what you do. It’s about what you believe.
Here’s the failure mode: an AI gives you a confident, well-reasoned answer to a non-trivial question. You don’t have a strong prior. It sounds right. You adopt it. Later, it turns out to be wrong — not because the AI hedged and you missed the hedge, but because it was wrong with full confidence. No signal. No warning. Just a belief you now hold that you didn’t form yourself.
Do this enough and something subtle degrades. Not your ability to reason — your calibration. Your sense of how much confidence to assign your own views versus an AI’s. The more often you defer to confident-sounding answers, the more your baseline shifts toward “if Claude thinks so, probably.” And that shift is invisible, because each individual deferral feels reasonable.
The asymmetry is what makes this hard. When AI is uncertain, you can tell — the hedging is obvious. When AI is confidently wrong, you can’t — it presents exactly like confident-and-right. So the failure mode is precisely correlated with your inability to detect it. You’re most at risk when you feel least at risk.
The fix isn’t distrust. It’s a habit: notice when you didn’t form a view before the AI did. Not “is this answer correct?” but “did I have a view here, and did I let it get displaced?” If you can’t articulate why the answer is right — not just that it sounds right — the adoption happened too fast.
Effort you can rebuild. A miscalibrated model of who to trust is harder to unwind, because you don’t know which beliefs it’s already touched.