Is using AI coding tools eroding my engineering skills? Quite possibly — and the reason you can’t tell is structural. Cognitive drift: sustained dependence on a powerful tool gradually erodes the skills it was meant to augment, and the erosion is invisible because the tool compensates for the very decline it causes — until the tool is unavailable, wrong, or insufficient.
This isn’t a willpower failure. The brain deprioritizes what it no longer practices, regardless of how important that capability once was.
The green-checkmark accelerant
AI-generated tests often verify implementation, not behavior — they produce passing signals that feel like quality and aren’t. The progression: the team sees green and stops writing its own tests → loses the ability to identify what should be tested → eventually can’t distinguish meaningful coverage from theatrical coverage. The feedback loop feels complete even when it’s hollow. (Don’t let the agent write both the code and the tests that “verify” it.)
Measuring your drift stage
The calibration check: do a real task without AI. The gap between what you thought you could do and what you actually could is your drift stage. A large gap means you’re further along the retention curve than you assumed. That gap — between self-assessed and tested capability — is the hallmark of lost metacognitive calibration.
Discovering the gap isn’t failure. It’s the start of maintaining the skill on purpose: periodic unaided reps on the capabilities you most need to keep.
Why this belongs in the infrastructure story
The same observability that makes the agent’s decisions visible can make your own drift visible — if you instrument for it. The system improving and you improving are different axes. Track both. It’s the engineer-side mirror of the Fluency Trap.
Where cognitive drift fits the full discipline: curiochat.ai/software-engineer