Think about the one thing you know better than almost anyone. Maybe it is something you built a business around. Maybe it is a skill you have spent a decade sharpening. Maybe it is something most people would not even recognize as expertise, but you know the difference between good and great because you have done the reps.
Now imagine asking AI to do that thing and sending the result straight to a client. No review. No edits. Just whatever the tool gives you, shipped.
You would not do it. You know too much. You would read the output and immediately see what is missing. The shortcut it took. The nuance it flattened.
The answer that sounds right to everyone except someone who actually knows. In your domain, you are the quality control. And you would never turn that off.
Here is the problem. In every other area you touch, the ones where you do not have that depth, you are probably not applying the same standard. You are reading AI output that sounds polished, sounds confident, sounds complete, and you are treating it as handled. Not because you evaluated it and found it sufficient. Because you do not have the expertise to see what is missing. And when you cannot see what is missing, everything looks right.
That is not how we normally handle delegation. When you hire an accountant or a lawyer or a contractor, you know you are trusting someone. There is an awareness of the gap. You ask questions. You might get a second opinion. You proceed with caution precisely because you know you are out of your depth. That caution is a feature, not a weakness. It is the appropriate response to operating outside your expertise. AI removes it entirely. It does not feel like trusting someone. It feels like looking something up. It feels like knowing. So you do not bring the skepticism you would bring to a stranger’s advice, because the interface never signals that skepticism is needed.
This pattern is backed by a growing body of research. Automation bias is the documented tendency to accept the output of automated systems without sufficient scrutiny. A 2025 review published in AI & Society examined 35 peer-reviewed studies and found that the single strongest predictor of whether someone catches an AI error is their attitude toward AI in that specific context. People who approached AI with skepticism performed better. People who assumed the system was reliable made more mistakes. Not because they were less intelligent. Because they stopped checking.
A separate study published in Computers in Human Behavior found something even more troubling. When people use AI tools, everyone overestimates their own performance, regardless of skill level. The researchers expected to see the classic Dunning-Kruger pattern, where low-skill users overestimate and high-skill users self-correct. Instead, the pattern vanished. AI made everyone equally overconfident. The fluency of the output, the speed, the clean formatting, it all triggered what psychologists call the processing-fluency heuristic. If it reads smoothly, it must be right. The polished surface short-circuits the instinct to question what is underneath.
This is the mechanism that makes the whole thing dangerous. AI does not fail the way a bad employee fails. A bad first draft announces itself. Sloppy formatting, weak logic, obvious gaps. You see it and you fix it. AI’s failure mode is a polished wrong answer. It reads like 90 percent even when the actual quality is closer to 40. And the less you know about a subject, the wider that gap between what you perceive and what is actually there.
The data shows up in industry surveys too. The Stack Overflow 2025 Developer Survey found that experienced developers show the highest rate of distrust toward AI tools compared to every other experience level. Not because they are technophobic. Because they have the reps to see what the tool gets wrong. Meanwhile, less experienced developers report higher trust, higher satisfaction, and higher confidence in AI-generated code. Same tool. Same output. Completely different evaluation, separated only by how much the person actually knows about what they are looking at.
Think about what that means for a small business owner. You are almost certainly an expert in one or two things. In those areas, you have natural immunity to AI’s most dangerous quality, which is the appearance of competence without the substance behind it. But in the five or ten other areas you touch every day,
The fix is not to stop using AI. It is too useful for that, and the productivity gains are real. The fix is to stop running two different standards without realizing it. When AI generates something in your area of expertise, you review it with precision. You catch the subtle errors. You would never ship it untouched. That instinct is correct. The goal is to bring that same posture, that same healthy distrust, to every other domain where AI is doing work you cannot fully evaluate on your own.
In practice, this means budgeting for expert review the same way you budget for the tools themselves. It means having a CPA glance at the AI-generated financial model before it goes to the bank. It means having an attorney read the AI-drafted contract before you sign it. It means treating AI the way you would treat a sharp but brand-new hire. Capable of getting you 80 percent of the way there. Not capable of telling you which 20 percent is missing.
You are the proof that AI needs oversight. Every time you correct an AI output in your own field, you are demonstrating that the tool truly cannot be trusted to operate alone. The only question is whether you will extend that lesson to the areas where you cannot see the seams, or keep assuming AI is only unreliable where you happen to be watching.
Tyler Kelley is Co-Founder of SLAM Agency. He advises CEOs on strategic positioning and helps organizations understand where AI creates value and where it creates risk.