The first wave of “vibe coding” was about speed. Type a prompt, get a working app, ship something by tonight. The follow-up problem is what happens three months later when nobody understands how the thing actually works.

A lot of developers using AI tools are running into the same wall: the app technically functions, but changing anything becomes risky because the codebase has no clear structure or ownership. That pattern keeps showing up in community discussions from both experienced engineers and newer builders trying to maintain AI-generated projects.[1][3][4]

The mistake isn’t using AI to write code. It’s treating generated code like finished engineering.

The biggest maintenance trap is “explainability debt”

One thing that keeps coming up in vibe-coding communities is that non-technical users often can’t tell when the model made a subtle mistake, so broken patterns keep accumulating inside the project.[1]

That gets worse when people ask another AI model to explain the first AI’s code. ShiftMag called this out directly: using AI to explain AI-generated code can compound errors because the explanations themselves may also be wrong.[5]

You can usually spot this kind of project fast:

  • duplicated functions everywhere
  • inconsistent naming
  • five different patterns for state management
  • giant files because the model kept appending instead of refactoring
  • comments that sound convincing but don’t match the actual behavior

The scary part is these apps often demo perfectly fine. The maintenance pain only appears later when a feature request forces someone to untangle the structure underneath.

AI-generated software still needs architecture decisions

There’s a growing split between “AI wrote code” and “AI-assisted engineering.” Addy Osmani described his workflow as disciplined AI-assisted engineering where humans stay accountable for the final software produced.[6]

That distinction matters more than the model itself.

Current coding models are very good at local problem solving. They can generate a component, fix a bug, or scaffold an API route quickly. What they still struggle with is maintaining long-term coherence across an evolving product.

That’s why teams shipping larger production apps with AI tools still emphasize real coding experience and understanding model quirks.[4]

The maintenance failures usually happen when people skip:

  • defining architecture before prompting
  • documentation standards
  • testing strategy
  • dependency management
  • refactoring cycles

AI can generate code faster than most humans can review it. That sounds productive until technical debt starts arriving at machine speed too.

“It works” is becoming a dangerous benchmark

A question showing up more often now is: if you still need experienced developers to fix and stabilize the app later, what exactly did vibe coding save?[3]

The answer is probably that AI compresses prototyping time more than engineering time.

That lines up with broader discussions around AI-generated software changing the relationship between product management and engineering.[2] More people can now create software prototypes without deep technical skills, but maintainable production systems still require decisions that models don’t reliably own.

The practical takeaway is pretty simple: use AI aggressively for acceleration, but don’t outsource system understanding.

If nobody on the team can explain why the app is structured the way it is, maintenance eventually turns into archaeology.

Sources