If you lean on AI advice every day, do you slowly lose your own professional judgment without noticing?

This explores how routinely following AI recommendations can wear down professional judgment. The corpus has no long-term study tracking a professional's skills fading over years. What it has is a set of mechanisms that, taken together, describe how the erosion would happen. The first mechanism is that people lose track of what they can do without the tool. The second is that AI errors are hard to see. The third is that trust builds up through repetition.

The first mechanism is about self-perception. Research on the 'LLM Fallacy' finds that people misattribute AI-produced work to their own ability, and this happens regardless of whether the output is accurate or how much they relied on it (How does AI-assisted work reshape how people see their own abilities?). A professional can feel sharper while the tool does more of the thinking. Because this is a distortion of self-perception rather than a verification failure, forcing people to double-check doesn't fix it. What helps is making clear which part of the work was the human's and which was the machine's. Fluent AI output also invites three thinking errors that make each other worse: mistaking the model's output for the real situation, mistaking intuition for reasoning, and looking for confirmation of what you already believe (Why do people trust AI outputs they shouldn't?).

The second mechanism is that the failures are hidden exactly where judgment matters. In medical triage, legal interpretation and financial planning, confident wrong answers cluster in rare edge cases, while aggregate accuracy looks excellent (Why do confident wrong answers hide in standard accuracy metrics?). A tool that is right 95% of the time teaches you to stop checking, and the other 5% is where the harm happens. Some errors are also built into the tool. LLM recommenders lean toward items that were popular in their pretraining data, not in the user's own context (Where does LLM recommendation bias actually come from?), so a professional who defers is quietly adopting a bias they never chose. Meanwhile AI can produce knowledge faster than people can evaluate it, and the evaluation tools are often AI too, which leaves human checking further behind each year (Can AI generate knowledge faster than humans can evaluate it?).

The third mechanism is time. In a partner-selection experiment, participants started out biased against AI partners but came to prefer them over repeated rounds, because the AI was consistently reliable and less variable than humans (Do humans learn to prefer AI partners over time?). That study wasn't about professionals, but the learning is the same kind that would make double-checking feel unnecessary. Zoom out and the same drift shows up in institutions. Systems stay aligned partly because workers who care about outcomes are embedded in them. As AI replaces that labor step by step, the implicit check disappears without any single moment when it was decided (Does incremental AI replacement erode human influence over society?).

The corpus also suggests that how the AI is used matters. One approach has the machine supply interpretive guidance, highlighting which aspects of the input deserve attention, and leaves the decision and the responsibility with the human. This removed anchoring bias and improved human judgment on hard cases, while a system that hands over a verdict does not (Can AI guidance reduce anchoring bias better than AI decisions?). Read next to the LLM Fallacy work, the pattern is that an AI that does the perceiving may weaken judgment, while an AI that sharpens the human's perception may strengthen it.