The Problems with Vibe Coding (That No One Talks About)

Start with the genuine wins

Vibe-coding — building software through natural-language prompts to an AI — has produced real productivity gains. Developers move faster, prototypes emerge in hours instead of days, and non-technical founders can build things that previously required an engineer. These benefits are real and shouldn't be dismissed.

The problems with vibe-coding aren't arguments that it doesn't work. They're specific failure modes that appear consistently when vibe-coded software is deployed to production or handed over to a business that depends on it.

Problem 1: Acceptance without specification

Vibe-coding workflows typically proceed by iteration: describe what you want, see what the AI builds, redirect if it's not right. The acceptance test is "does it look right?" rather than "does it meet a written specification?"

This matters because looking right and being right are different things. A UI that passes a visual check can have incorrect logic in the functions it calls. A form that appears to save data may behave differently under edge cases that weren't tested. Without a written spec that defines exactly what "done" means, there's no way to determine whether the software is correct — only whether it seems correct in the demo.

In business contexts — payroll calculations, pricing logic, compliance rules, financial reporting — the difference between "looks right" and "is right" has real consequences.

Problem 2: Technical debt accumulates invisibly

AI-generated code often gets the observable behavior right while accumulating invisible technical problems: tight coupling between components, missing error handling, hardcoded values that should be configurable, copy-pasted logic that should be abstracted, security patterns that are almost correct but not quite.

None of these problems show up in a demo. They show up when a dependency updates and the tight coupling breaks. When the edge case that wasn't tested appears in production data. When the missing error handler causes an outage. When the security pattern fails under a specific request pattern.

The rate of visible output from vibe-coding often exceeds the rate at which the accumulated invisible problems are assessed. The result is a codebase that delivers features fast and fails unpredictably.

Problem 3: Maintenance requires the original prompter

Vibe-coded software is often not written by anyone who could read and understand it without the AI. The person who built it can iterate on it using the same tool that built it. But if that person leaves, or if the AI model changes, or if the tool is unavailable, the codebase becomes opaque.

Traditional software is maintainable by any developer who knows the language. Vibe-coded software with poor structure, inconsistent naming, and insufficient comments is maintainable only by someone who can reconstruct the original builder's intent from an AI conversation.

Problem 4: AI hallucinations compound in complex logic

AI models occasionally produce code that looks plausible but is incorrect: a function that handles the common case correctly but fails on a specific input, a library usage that worked in a previous API version but no longer does, a security check that appears complete but misses a specific attack vector.

In simple CRUD applications, these errors are usually visible quickly. In complex business logic — tax calculations, compliance rules, financial aggregations — they can persist undetected until the specific input that triggers them appears in production data. The more complex the logic, the more reviewing the output against a detailed spec matters.

Problem 5: The billing model transfers the failure risk to buyers

In most vibe-coding arrangements, a developer uses AI tools and bills by the hour or by token consumption. This means:

• The cost of prompt iterations and dead ends lands on the client's invoice
• The cost of debugging the accumulated technical debt lands on the client's invoice
• The cost of fixing production failures that the AI introduced — but that a proper QA process would have caught — also lands on the client's invoice

None of these costs are obviously unfair — the developer is spending time, and that time is real. But the structure means the client absorbs the downside of vibe-coding's failure modes while the developer captures the upside (faster output, lower overhead).

What actually addresses these problems

The problems with vibe-coding aren't solved by avoiding AI. They're solved by structural choices around the AI:

• Written specs before work starts — so acceptance is binary (meets the spec or doesn't) rather than subjective (looks right or doesn't)
• Behavior-driven testing — so regressions are caught before they reach production
• Code review by someone who can read the output — so invisible technical debt is identified before it compounds
• Fixed-price billing — so the cost of AI's inefficiency stays with the vendor rather than the client

SixHelix uses AI in production throughout our workflow. The answer to vibe-coding's problems isn't less AI — it's better process and a better commercial model around the AI.