The Vibe
Vibe-coding lowered the barrier to building software to almost nothing. Describe what you want, and the AI writes the code — modern frameworks, deployment, version control, even the database, all handled for you. Prototyping has never been this fast.
It feels unreal at first. You ship a feature before lunch that used to take a sprint. The product evolves in front of you, and for a moment, you feel unstoppable.
Then it scales. Bugs start showing up, and fixing one seems to create two more. The AI tells you a feature is done — and it isn’t. Not because the AI got worse, but because nothing was there to catch the gap between “the AI said so” and “it actually works.”
Here’s what usually causes that gap — and, more importantly, how to close it without giving up the speed that made vibe-coding worth doing in the first place.
Five Patterns of Tech Debt
1. One folder, everything in it
Specs, code, half-finished notes, old screenshots, one-off reports — in a vibe-coded project, they often live in the very same folder, with nothing marking which one is current.
Ask an AI “how should checkout work” in a folder like that, and it’s just as likely to open a six-week-old draft as the actual current logic. It has no way to tell the difference — the folder doesn’t tell it which file to trust.
Why it matters: the AI isn’t ignoring your instructions on purpose. It’s reading whatever it finds first. If two files disagree, you’re rolling dice on which one wins.
2. Old and new logic, tangled together
Requirements change — that’s normal. What’s not normal is when the old version never actually leaves. You update a rule, but the previous one is still sitting in the code, in a file nobody deleted. Now there are two or three versions of “the rule,” and it’s not obvious which one is live.
A codebase should hold what the product does today — not a diary of everything it used to do. Picture someone who remembers every single day of their life in perfect detail. All that stored history doesn’t make them sharper — it just makes the one memory that matters right now harder to find. Your codebase works the same way.
Why it matters: when two contradicting versions of a rule both still exist, the AI isn’t being careless by picking the wrong one — it’s picking one, because nothing told it the other was retired.
3. The same problem, solved five different ways
A button component already exists. A date-formatting function already exists. But ask an AI to build a new page, and it will often write both from scratch — because nothing pointed it back to what’s already there. It solves problems locally, one prompt at a time, with no memory that a similar problem was already solved three screens ago.
The result: four different date pickers, three different ways to format a price, each behaving just a little differently. None of them are wrong on their own — the fragmentation is the problem.
Why it matters: every duplicate is one more place a bug can hide, and one more place someone has to fix by hand when the rule changes.
4. No automatic guardrails
Most people remember to build. Few remember to verify. Without automated tests, the only way to know a feature still works is to manually click through the entire product every time something changes. That doesn’t scale past a handful of features, so eventually people stop checking and start hoping.
Think of it like a train: vibe-coding is the engine, and it’s fast. Tests are the tracks. An engine without tracks doesn’t go anywhere useful. Good tracks do two things — they tell the AI what “correct” looks like, and they tell it to check its own work every time, and if a test breaks, to keep fixing and re-running until everything passes again on its own.
Why it matters: without that loop, there’s no way to sign off on what the AI built. You’re not reviewing the work — you’re just trusting it.
5. Everything gets slower
Eventually, the first four patterns show up as the same symptom: everything takes longer. Development slows down, because every new feature risks breaking three old ones. Shipping slows down, because nobody’s confident enough to release without re-checking everything by hand. Even responding to customers slows down, because nobody can say for certain whether a bug is fixed without testing it live.
Underneath it is usually one root cause: nobody defined what “done” actually means — not “does it look done,” but tested, documented, and safe to build on top of.
Why it matters: speed is the entire reason to vibe-code. Losing it to the very problems vibe-coding introduces is the outcome worth avoiding.
The Real Issue
None of this means the AI writes bad code. Almost every individual change it makes is reasonable, given what it can see in the moment. What’s missing isn’t a smarter AI — it’s structure: one source of truth instead of scattered notes, logic that’s written once and reused instead of rebuilt, tests that run automatically instead of depending on memory, and a clear definition of “done.”
Vibe-coding was never meant to organize itself. That job didn’t disappear — it just didn’t show up on day one, because day one was too fast and too fun to notice it was missing.
So What Helps
- Keep one source of truth. Separate the current plan from notes, drafts, and old reports. If a document isn’t current, it shouldn’t be sitting next to the ones that are.
- When a rule changes, delete the old one. Don’t leave two versions for someone — or the AI — to guess between.
- Point the AI at what already exists before it builds something new. Most “new” components are old ones in disguise.
- Make tests the guardrail, not an afterthought. Even a handful of automated checks beats relying on memory, and gives the AI something to verify itself against.
- Define “done” before you start building. A one-paragraph spec beats a folder of half-finished notes every time.
Where This Goes
Vibe-coding isn’t the problem, and slowing down to write a fifty-page spec before touching code isn’t the fix either — that just trades one kind of stall for another. The real move is to let the AI keep moving fast on top of a few pieces of structure: one source of truth, a clear definition of done, tests that catch what memory can’t.
Get that right, and vibe-coding stops being something you eventually have to grow out of. It becomes the way you build — permanently, and at any scale.