Vibe coding: an AI prototype is not a business application

The essentials in brief

Vibe coding — generating working code by describing what you want to an AI assistant — excels at exploring an idea, validating a concept or producing a prototype quickly. What it does not do: deliver a reliable, maintainable and secure business application. The distance between a prototype that works in ideal conditions and an application an organisation can operate over time remains considerable — and the developer's expertise remains decisive in bridging it.

What vibe coding is (and what it genuinely gets right)

The term "vibe coding" refers to a practice that emerged with code-generation assistants (GitHub Copilot, Cursor, Claude, GPT-4o, and others): instead of coding line by line, the developer — or a non-developer — describes in natural language what they want to obtain, iterates on the AI's suggestions, and assembles a working program without necessarily writing most of the code themselves.

It is a genuine evolution in development practices, and it would be inaccurate to play it down. Vibe coding does several things very well:

• Prototyping quickly: testing an interface, validating a simple piece of business logic, producing a demonstration in a few hours rather than a few days.
• Exploring solutions: comparing approaches without investing several days of development in each one.
• Kicking off a project: generating a base structure, the first routes of an API, the first components of an interface.
• Speeding up repetitive tasks: generating unit tests on existing code, turning a data schema into a model, converting formats.

In these uses, AI is a real accelerator. The problem is not in what it does well — it is in what it is then asked to do next.

From prototype to production: the invisible wall

A prototype that works in a demo is not an application ready to be operated. Moving from one to the other means solving problems that AI-generated code does not handle naturally.

Maintainability and technical debt

AI-generated code is often functional in the moment. What is rarer: that it is readable, well structured, consistent with the conventions of an existing project, and easy to evolve six months later by someone who was not there at the start. AI optimises for the immediate answer to the request, not for long-term readability or overall architectural consistency.

The frequent result: a build-up of technical debt. Functions that duplicate each other because the AI answered the request without checking whether something equivalent already existed elsewhere. Dependencies introduced without any assessment of their maintainability. Structures that work on the nominal case but collapse at the first edge case.

For an experienced developer, fixing and refactoring generated code takes time — sometimes more than writing it directly would have.

Security and data

Security vulnerabilities are rarely visible during a demonstration. They become visible in production. AI-generated code can introduce classic flaws — SQL injections, exposure of environment variables, insufficient error handling that leaks internal information, misconfigured authentication — not because the AI is "bad", but because it answers what it is asked and is not mandated to anticipate attack vectors.

On applications that handle sensitive business data (customer data, financial data, health data, citizen data), a security review is not optional. It requires human expertise and an understanding of the deployment context that the AI does not possess.

Business rules, edge cases and reliability

A business application is precisely an application that encodes rules specific to an organisation: calculation rules, validation workflows, exceptions, special cases accumulated over years of experience. These rules do not appear in the prompt.

AI generates code that works on the nominal case as described. Edge cases — which often represent a small fraction of usage but a high risk when they go wrong — are rarely covered spontaneously. A misinterpreted business rule that produces an incorrect result on 2% of cases can stay invisible for weeks and prove very costly to fix.

Scalability and architecture

A prototype is not designed to scale. It is designed to work. When real usage begins — ten concurrent users, then a hundred, then a thousand; data volumes tripling in six months; integrations with third-party systems — the architectural choices made in prototype mode show their limits.

Designing a scalable architecture from the outset takes experience: knowing what will not hold, choosing the right patterns, anticipating breaking points. It is a skill acquired on real projects, not by iterating on prompts.

Why developer expertise remains decisive

This is not a self-serving argument. It is an operational observation.

The differentiator of a good developer in 2026 is no longer writing code by hand faster than the AI — the AI wins that race on repetitive tasks. It is knowing what to build, how to architect it, where the risks hide and how to maintain the result over time.

A developer with deep command of their technical environment — its patterns, its tools, the known pitfalls of their language and its ecosystem — uses AI as a copilot that speeds up execution. They validate, correct and structure what the AI produces. They make the architectural decisions the AI does not make. They anticipate the cases the AI did not see.

A non-developer who uses vibe coding to produce a business application is doing something different: they are producing something that resembles an application. The resemblance can be convincing. The differences show in production.

That is why we build our business applications with developers who have deep command of their language — not "a bit of everything" — and who use AI as an accelerator, not as a replacement for technical judgement.

The right place for AI in the software factory

AI has its place in software development — a real and growing one. It speeds up the generation of repetitive code, makes documentation easier, helps explore alternative approaches, generates tests, assists code review. It increases the productivity of an experienced team.

It can also be embedded in the application itself: this is the heart of our approach to AI-augmented custom business applications. Here AI is a controlled component — a document-analysis module, a recommendation engine, a data-entry assistant — integrated into a defined process, on data the organisation controls, with explicit validation rules.

That is very different from delegating the construction of the application to the AI. In the first case, AI augments the application. In the second, AI replaces the developer — and the result carries the limits of that substitution.

Our article Framing AI in your business processes explores how to tell genuinely valuable AI uses from gimmicks — before even talking about a tool or development.

Do you have an AI prototype to make production-ready?

If you have produced a prototype with vibe coding and you are considering putting it into production, or if you have inherited an AI-generated codebase that you now have to maintain, the first step is a technical audit: understanding what is reliable, what is not, and what would need to be rebuilt or refactored.

That is exactly what our consulting & audit engagement covers. And if the scoping concludes that refactoring or a rebuild on solid foundations is needed, our support & maintenance service can take over for the long term.

On the hidden cost of these fragile foundations and how AI, properly framed, can instead reduce debt, see technical debt and AI.

The goal is not to tell you your prototype is worthless — it probably has value, at least as an exploration. It is to help you decide what deserves to be industrialised, and how.