When I first started using AI coding tools, I’ll be honest—I was excited. The experience of seeing code auto-complete, repetitive boilerplate disappear in an instant, and problems that used to take hours of digging through documentation get solved in seconds was exhilarating. I thought, "Finally, development is becoming truly easy."But at some point, a strange feeling began to set in. I was writing code faster than before. My commit count was up, and features were rolling out quickly. Yet, strangely, I was more exhausted at the end of the day than I used to be. Code reviews were taking longer, bugs weren't decreasing, and the number of moments where I wondered, "What exactly am I building?" was increasing.Is this just my imagination? Or has AI coding disrupted a delicate balance in development?<h3 style="font-size: 22px; font-weight: normal;">AI Doesn't "Understand" Code</h3><img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777009861867.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;">It’s no longer news that AI writes code for us. But what we’re missing is the fact that AI does not "understand" the code it produces. To be more precise, it doesn't need to. Its goal is simply to generate the most plausible output between the input and the output.The problem is that humans have to live on top of that code.In the past, the flow of code became etched into our muscles. We typed it ourselves, bumped into errors, fixed them, got broken again, and accumulated an intuitive sense of how things worked. Now, that process has been shortened. Too shortened. Sometimes, it’s omitted entirely.If "It works, so it's fine" and "AI recommended this code" have become common phrases in your team's conversations, the transformation has already begun.<h3 style="font-size: 22px; font-weight: normal;">The Problem of "Black Boxes"</h3>The biggest feature of AI-generated code is that it lacks an explanation. I don't mean it lacks comments; I mean the structure itself doesn't tell you why it was designed that way. It works. The tests pass. But when you ask questions, you hit a wall:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">"Why is this branch here?"</li><li class="editor_editorListItem__FKzSL">"What scenarios does this cache assume?"</li><li class="editor_editorListItem__FKzSL">"Was this asynchronous process really necessary at this exact timing?"</li></ul>If you can't answer these, that code is not an asset—it’s a "black box" that happens to be running for now. The issue is that these black boxes are multiplying rapidly via AI. At first, productivity seems to rise, but over time, the entire codebase shifts into a state where "no one fully understands it." That is when development slows down—frighteningly so.<h3 style="font-size: 22px; font-weight: normal;">The Disappearance of Excellent Code</h3>There is another strange change: while the average quality of code hasn't visibly plummeted, excellent code is slowly disappearing. AI-recommended code is generally "safe." It’s widely used, syntactically secure, and doesn't cause major accidents.The problem is that it is not "code perfectly suited for our team." AI doesn't know the history of our service’s past outages. It doesn't know why we’ve been conservative in certain areas or which choices were made after years of trade-offs. Stripped of that context, the suggested code converges toward a mediocre mean that isn't sharp anywhere. It’s convenient for now, but in the long run, the team’s technical identity erodes. The codebase turns into a "collection of plausible-looking examples," and the system becomes slowly but surely more complex.<h3 style="font-size: 22px; font-weight: normal;">The Burden of Inferring Intent</h3>Many expected code reviews to get easier thanks to AI. In reality, many teams are experiencing the opposite. Reviewers are no longer just checking code quality; they are forced to spend more time inferring intent.AI-generated code is often unnecessarily long, over-abstracted, and fails to expose problems directly. During a review, I find myself thinking:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">"Was there a reason to wrap it in this many layers?"</li><li class="editor_editorListItem__FKzSL">"Did I miss any edge cases here?"</li></ul>Review is no longer "verification"—it has become "exploration." To determine if the code is safe, I have to understand it from scratch. As a result, while writing time has decreased, understanding time has increased. This is a clear reversal. <img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777009882272.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;"><h3 style="font-size: 22px; font-weight: normal;">Test Code: A False Sense of Security</h3>AI makes test code incredibly well. It looks clean and the patterns are familiar. Yet, accidents still happen in services with those tests. Why? Because AI-generated tests usually verify the "normal flow." But real failures always explode in the weirdest places—edge cases, unexpected inputs, timing issues, or behaviors we assumed humans would never do.This isn't AI's fault. AI has never experienced an outage. Tests ultimately come from past experiences of failure. If you skip that experience, the number of tests might increase, but actual stability does not.<h3 style="font-size: 22px; font-weight: normal;">Who is Most at Risk?</h3>Surprisingly, it's junior developers. AI makes juniors "capable of working" quickly, but it steals something vital in the process: the experience of being stuck, the time spent wandering, and the instinct for finding root causes. If that isn't accumulated, problem-solving ability doesn't grow."The state of being unable to do anything without AI." This isn't an exaggeration. There is a growing number of developers who freeze when looking at logs during a service outage because they’ve relied on AI to think for them.<h3 style="font-size: 22px; font-weight: normal;">Conclusion: AI Doesn't Take Responsibility</h3>All these problems converge on one point: AI does not take responsibility. When the code breaks, the server dies, or you get a call at 3 AM, AI doesn't show up. The person is the one left standing there. And if that person doesn't know "why it was made that way," the problem doubles.The point of this article isn't to say "don't use AI." Quite the opposite. AI is now too powerful to ignore. But we need standards:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Can I merge this code without understanding it?</li><li class="editor_editorListItem__FKzSL">Will I accept the structure AI suggested as-is, or will I challenge it?</li><li class="editor_editorListItem__FKzSL">Do I realize what I'm losing in exchange for speed?</li></ul>The developer of the future is likely not the person who types the most code. Instead, it will be the person who can judge the options created by AI and take responsibility for them.If you feel exhausted, it’s not because you’re falling behind. It’s because the center of gravity in development is shifting. And we are standing right in the middle of that change.

Why Are We So Exhausted After Starting to Use Vibe Coding Tools?!

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