Anyone who’s used AI to generate code has seen it make mistakes. But the real danger isn’t the occasional wrong answer; it’s in what happens when those errors pile up across a codebase. Issues that seem small at first can compound quickly, making code harder to understand, maintain, and evolve. To really see that danger, you have to look at how AI is used in practice—which for many developers starts with vibe coding.
Vibe coding is an exploratory, prompt-first approach to software development where developers rapidly prompt, get code, and iterate. When the code seems close but not quite right, the developer describes what’s wrong and lets the AI try again. When it doesn’t compile or tests fail, they copy the error messages back to the AI. The cycle continues—prompt, run, error, paste, prompt again—often without reading or understanding the generated code. It feels productive because you’re making visible progress: errors disappear, tests start passing, features seem to work. You’re treating the AI like a coding partner who handles the implementation details while you steer at a high level.
Developers use vibe coding to explore and refine ideas and can generate large amounts of code quickly. It’s often the natural first step for most developers using AI tools, because it feels so intuitive and productive. Vibe coding offloads detail to the AI, making exploration and ideation fast and effective—which is exactly why it’s so popular.
The AI generates a lot of code, and it’s not practical to review every line every time it regenerates. Trying to read it all can lead to cognitive overload—mental exhaustion from wading through too much code—and makes it harder to throw away code that isn’t working just because you already invested time in reading it.
Vibe coding is a normal and useful way to explore with AI, but on its own it presents a significant risk. The models used by LLMs can hallucinate and produce made-up answers—for example, generating code that calls APIs or methods that don’t even exist. Preventing those AI-generated mistakes from compromising your codebase starts with understanding the capabilities and limitations of these tools, and taking an approach to AI-assisted development that takes those limitations into account.
Here’s a simple example of how these issues compound. When I ask AI to generate a class that handles user interaction, it often creates methods that directly read from and write to the console. When I then ask it to make the code more testable, if I don’t very specifically prompt for a simple fix like having methods take input as parameters and return output as values, the AI frequently suggests wrapping the entire I/O mechanism in an abstraction layer. Now I have an interface, an implementation, mock objects for testing, and dependency injection throughout. What started as a straightforward class has become a miniature framework. The AI isn’t wrong, exactly—the abstraction approach is a valid pattern—but it’s overengineered for the problem at hand. Each iteration adds more complexity, and if you’re not paying attention, you’ll end up with layers upon layers of unnecessary code. This is a good example of how vibe coding can balloon into unnecessary complexity if you don’t stop to verify what’s happening.
Novice Developers Face a New Kind of Technical Debt Challenge with AI
Three months after writing their first line of code, a Reddit user going by SpacetimeSorcerer posted a frustrated update: Their AI-assisted project had reached the point where making any change meant editing dozens of files. The design had hardened around early mistakes, and every change brought a wave of debugging. They’d hit the wall known in software design as “shotgun surgery,” where a single change ripples through so much code that it’s risky and slow to work on—a classic sign of technical debt, the hidden cost of early shortcuts that make future changes harder and more expensive.
AI didn’t cause the problem directly; the code worked (until it didn’t). But the speed of AI-assisted development let this new developer skip the design thinking that prevents these patterns from forming. The same thing happens to experienced developers when deadlines push delivery over maintainability. The difference is, an experienced developer often knows they’re taking on debt. They can spot antipatterns early because they’ve seen them repeatedly, and take steps to “pay off” the debt before it gets much more expensive to fix. Someone new to coding may not even realize it’s happening until it’s too late—and they haven’t yet built the tools or habits to prevent it.
Part of the reason new developers are especially vulnerable to this problem goes back to the Cognitive Shortcut Paradox.1 Without enough hands-on experience debugging, refactoring, and working through ambiguous requirements, they don’t have the instincts built up through experience to spot structural problems in AI-generated code. The AI can hand them a clean, working solution. But if they can’t see the design flaws hiding inside it, those flaws grow unchecked until they’re locked into the project, built into the foundations of the code so changing them requires extensive, frustrating work.
The signals of AI-accelerated technical debt show up quickly: highly coupled code where modules depend on each other’s internal details; “God objects” with too many responsibilities; overly structured solutions where a simple problem gets buried under extra layers. These are the same problems that typically reflect technical debt in human-built code; the reason they emerge so quickly in AI-generated code is because it can be generated much more quickly and without oversight or intentional design or architectural decisions being made. AI can generate these patterns convincingly, making them look deliberate even when they emerged by accident. Because the output compiles, passes tests, and works as expected, it’s easy to accept as “done” without thinking about how it will hold up when requirements change.
When adding or updating a unit test feels unreasonably difficult, that’s often the first sign the design is too rigid. The test is telling you something about the structure—maybe the code is too intertwined, maybe the boundaries are unclear. This feedback loop works whether the code was AI-generated or handwritten, but with AI the friction often shows up later, after the code has already been merged.
That’s where the “trust but verify” habit comes in. Trust the AI to give you a starting point, but verify that the design supports change, testability, and clarity. Ask yourself whether the code will still make sense to you—or anyone else—months from now. In practice, this can mean quick design reviews even for AI-generated code, refactoring when coupling or duplication starts to creep in, and taking a deliberate pass at naming so variables and functions read clearly. These aren’t optional touches; they’re what keep a codebase from locking in its worst early decisions.
AI can help with this too: It can suggest refactorings, point out duplicated logic, or help extract messy code into cleaner abstractions. But it’s up to you to direct it to make those changes, which means you have to spot them first—which is much easier for experienced developers who have seen these problems over the course of many projects.
Left to its defaults, AI-assisted development is biased toward adding new code, not revisiting old decisions. The discipline to avoid technical debt comes from building design checks into your workflow so AI’s speed works in service of maintainability instead of against it.
Footnote
- I’ll discuss this in more detail in a forthcoming Radar article on October 8.
