"Vibe coding" — describing what you want and letting an AI tool write the code, without reading most of it closely — is genuinely useful for prototypes, internal tools, and throwaway scripts. It's also how a surprising number of production codebases quietly accumulate a kind of debt that doesn't show up until someone tries to change something.
Why it feels free at first
The first version of almost anything built this way looks great. It runs, the demo works, the feature shipped in a day instead of a week. The cost isn't in the code that exists — it's in the decisions that got made implicitly, by the model, that nobody actually reviewed: which edge cases got handled, which got silently ignored, whether two similar-looking functions are actually doing the same thing or subtly different ones.
Where the debt actually shows up
Inconsistent patterns across the same codebase
Ask an AI tool to build three similar features on three different days and you'll often get three different approaches to the same problem — different error handling, different naming conventions, sometimes different libraries for the same job. Each piece works in isolation. Together, they make the codebase harder for a human to hold in their head, which is exactly the thing that makes future changes slow and risky.
Edge cases that were never actually decided
What happens when the input is empty? When two requests arrive at the same time? When a network call times out? A generated function often has an answer to these — but it's an answer nobody chose, and it's rarely the same answer across every function that should behave consistently. This is invisible until the exact edge case happens in production, usually at a bad time.
Tests that confirm the code runs, not that it's correct
It's easy to get a generated test suite that passes — because the tests were often generated to match what the code already does, not against an independent definition of what it should do. A green test suite built this way tells you the code is consistent with itself, not that it's right.
The actual fix isn't "stop using AI tools"
These tools are a genuine productivity gain, and avoiding them isn't realistic or necessary. The fix is being deliberate about which parts of a build deserve a human reading every line versus which parts don't:
- Anything touching money, auth, or user data gets read line by line by a person who understands the business logic, full stop — no exceptions for speed.
- Shared utilities and anything more than one feature will depend on get reviewed for consistency before they spread across the codebase, not after three more features copy the pattern.
- Prototypes and internal tools can stay fast and loose — the whole point is speed, and the blast radius if something's wrong is small.
A useful habit: ask the tool to explain its own edge-case decisions
Before merging anything non-trivial, ask directly: "what happens here if X is null / empty / arrives twice?" A generated answer you can't get a clear response to is a sign nobody — human or model — actually decided, which means it's a decision still waiting to surprise you later.