When developing these days, we naturally hit moments where we think: "This is simple enough to just let AI handle, right?"And in most cases, it actually works out fine. API handlers, data transformation logic, repetitive UI components—AI produces them quite convincingly. Sometimes, it even looks cleaner than code I’d write myself.The problem starts here. As the experience of AI doing a "good job" accumulates, your sense of "where to draw the line" starts to blur. At some point, you almost reflexively type a prompt, paste the generated code, and ship it as long as the tests pass. In a busy schedule, that seems like a rational choice.But as time goes by, strange patterns emerge. Problems keep popping up in the same areas. The same types of code keep holding you back. And when you look back at that code, they all share one commonality: "This is code that a human should have been responsible for from the very beginning."Code you shouldn't entrust to AI isn't just about technical complexity. On the contrary, the most dangerous code is often simple on the surface, but critical in terms of context and judgment. Such code doesn't have a single "correct" answer, and the result of that choice impacts the entire system. AI doesn't understand the weight of these choices.<h3 style="font-size: 22px; font-weight: normal;">1. Authentication and Authorization Logic</h3>AI makes it incredibly easy to generate login, token validation, and permission check code. The problem is that it doesn't explain the gap between "working" and "being safe."<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">For example, some APIs must be accessible only by admins. Some should be read-only, and others require temporary permissions under specific conditions.</li><li class="editor_editorListItem__FKzSL">This is not a grammar problem; it’s a business rule and a liability issue. Most AI-generated permission checks stick to generic patterns. But in real services, exceptions always beat the rules—endpoints opened only to specific partners, temporary handling for legacy users, or conditions added during operation. Code generated without knowing this context will eventually leave a crack.</li></ul><h3 style="font-size: 22px; font-weight: normal;">2. Data Models and Domain Logic</h3>AI is great at structuring database schemas and dividing entities. However, it rarely reflects "why our service is built this way."<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Domain logic is a series of questions: Is this state valid? Should this value be mutable? Can this event be rolled back?</li><li class="editor_editorListItem__FKzSL">The answers to these questions exist outside the code: in meeting minutes, past outages, customer claims, and organizational decisions. AI doesn't know these. So, when you entrust domain logic to AI, it looks neat at first, but over time, unexplainable rules pile up. Eventually, no one understands the whole, and simple refactoring can no longer solve it because those initial choices hold you back.</li></ul><h3 style="font-size: 22px; font-weight: normal;"></h3><img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777010092686.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;"><h3 style="font-size: 22px; font-weight: normal;">3. Error Handling and Exception Design</h3>AI-generated error handling is usually polite—it includes <code>try-catch</code> blocks, error messages, and logs. But what matters in production is how to handle that error.<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Should it be retried? Can it be exposed to the user? Does the operator need to know immediately? Or should it be swallowed silently?</li><li class="editor_editorListItem__FKzSL">This judgment is a matter of service philosophy, not technology. AI always chooses the safe or generic side. But that might not be the right choice for your service. If these small misalignments pile up, incident response becomes impossible.</li></ul><h3 style="font-size: 22px; font-weight: normal;">4. Performance and Cost Code</h3>Surprisingly, many people entrust code involving performance and costs to AI. Cache strategies, query optimization, asynchronous processing. AI generates plausible code, but it doesn't explain what kind of cost structure it creates.<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">For instance, the judgment that "a cache should be enough here." AI adds the cache. But it doesn't consider when that cache is invalidated, how it affects traffic patterns, or what kind of cascading effects it might have during an outage. Performance is always a trade-off. What do you lose by gaining speed? What risks do you accept to lower costs? This judgment comes from numbers and experience.</li></ul><h3 style="font-size: 22px; font-weight: normal;">5. System Boundary and Structural Code</h3>There is a final, most important area: How far should one service go? Where does this responsibility end? How independent should this module be?<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">This is not a "few lines of code" problem. It affects team collaboration, deployment strategies, and organizational structure. When you entrust these decisions to AI, the structure moves in a direction that becomes increasingly hard to explain.</li></ul> <img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777010102691.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;"><h3 style="font-size: 22px; font-weight: normal;">If it’s not for AI, then what is?</h3>You might be thinking, "Then what on earth should I use AI for?" The answer is simple: Where judgment is not required, where responsibility is unclear, and where it’s okay to build again. Boilerplates, repetitive code, implementation references, or rapid idea validation—these are areas where AI is the best tool.Conversely, for code that:<ol class="editorOrderedList"><li class="editor_editorListItem__FKzSL">Remains in production for a long time once written,</li><li class="editor_editorListItem__FKzSL">Defines the nature of your service,</li><li class="editor_editorListItem__FKzSL">Needs to be explained when something goes wrong...</li></ol>You must hold onto this code yourself, even if it slows you down.Development in the AI era is not a battle of "how much can I delegate?" It is a battle of "how much can I afford not to delegate?" You don't need to write every line of code yourself, but you must never pass off a choice without understanding it.The moment you recognize that there is code you should not entrust to AI, it ceases to be a dangerous automation tool and becomes a powerful one.

There Is Code You Must NEVER Entrust to AI?! (And It's More Than You Think)

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