The most careful, frequent AI users may be the ones whose own judgment quietly erodes fastest — why would that happen?

This explores whether heavy, careful AI users lose their own judgment faster than casual users. The corpus doesn't directly show that the most diligent users decline fastest, and no study here measures it. In one study the word "diligence" even means something else: Do people fear judgment when they use AI at work? found that AI users expect *other people* to see them as less diligent, so they hide their AI use. What the corpus does offer is a set of mechanisms that explain how frequent, engaged use could wear down judgment without the user noticing.

The main mechanism is fluency. How do competent systems quietly undermine safety oversight? argues that the riskiest systems are the ones that seem to work well, because polished, confident output slowly lowers the reader's guard. Why do confident wrong answers hide in standard accuracy metrics? shows why this is hard to catch. In medicine, law and finance, confident errors pile up in rare edge cases while overall accuracy still looks high. A user who checks diligently and finds the AI right most of the time learns to check less, and the errors that slip through are the ones that do damage. Why do people trust AI outputs they shouldn't? adds that three traps multiply each other rather than simply adding up: mistaking the AI's output for reality, mistaking fast intuition for careful reasoning, and having your existing beliefs reflected back to you. The more you lean on the tool, the more these traps reinforce one another.

The survey data shows confidence and results drifting apart. In Why do workers feel confident with AI but get poor results?, 90% of workers felt confident with AI. Only 25% said it worked on the first try, and half spent more time than doing the task by hand. The gap was widest among younger workers. Developers show the opposite pattern: in Why do developers keep using AI tools they don't trust?, use kept rising while trust fell from 40% to 29%, mostly because of code that looks right but hides subtle bugs. So heavy use doesn't always produce blind trust. Sometimes it produces tired distrust, where people keep using a tool they no longer believe in. Neither outcome is the same as keeping sharp judgment.

The less obvious finding is about what actually wakes judgment up. What makes people distrust AI agents they delegate to? found that people pulled back their trust when an action couldn't be undone and others would see it, such as sending an email. High stakes alone didn't do it. If that holds more widely, judgment switches on in response to visible consequences, not to real risk. Quiet errors that can be fixed later pass through even when they matter.

The corpus suggests the fix lies in how the work is designed, not in more effort from the user. Can AI guidance reduce anchoring bias better than AI decisions? has the AI point out what to look at instead of handing over an answer, which removes the anchoring effect of seeing a ready-made decision. What makes accountable judgment scarce when AI cognition is cheap? argues that once AI makes first-pass thinking cheap, human judgment becomes the valuable skill. It survives only if organizations keep people accountable for decisions and leave them room to learn from practice.