The vibe coding dilemma: Why AI code alone is not the solution

The illusion of a simple solution

The idea of 'vibe coding' is based on a fundamental misconception: equating 'works' with 'is good'. Just because a code snippet produces the desired output today does not mean that it is a sustainable solution. There is often a complete lack of understanding of architecture. Vibe coders fail to recognise that their 'working' code:

• opens security gaps.
• becomes exponentially slower as the number of users increases
• has no error handling
• is not documented or testable
• It becomes incompatible with future system updates
   

The Enterprise Java community 'TheServerSide' (note: Java is a programming language) warns: "Future developers tasked with maintaining Vibe-coded projects will inherit a black box with no clear design, minimal comments, and logic built through random regeneration. Fixing or expanding this code will be a nightmare, and eventually the only feasible option will be to rewrite it all from scratch, erasing all the initial 'savings' of Vibe coding.'

The roadmap trap snaps shut when a quick hack suddenly becomes a critical business application. One developer reports: ‘AI starts making mistakes, and worse, it repeats mistakes in an endless cycle of debugging and creating new problems. At some point, debugging AI-generated code becomes more labour-intensive than writing the code manually.”

Companies then have to decide whether to continue operating the fragile code or to develop it completely from scratch. Either way, time and money that could have been saved are lost. This is a well-known phenomenon. Examples include the undocumented extension in SAP, the programming language that only one developer knows – who retired five years ago – and government software with no interface. Germany has many such software issues that will take a lot of time and money to fix.

From the Pretty Prototype Dilemma to the Ugly Code Problem

In our previous article on imperfection, we described the 'Pretty Prototype Dilemma' – the tendency to spend too much time perfecting prototypes instead of testing them quickly. Vibe Coding represents the opposite extreme, with code created so quickly and superficially that fundamental quality standards are neglected.

While the Pretty Prototype Dilemma is characterised by perfection paralysis, it is now speed that blinds us.

The consequences are evident in documented cases. For example, Google Gemini's CLI tool accidentally deleted user data because an AI-generated script executed incorrect delete commands. The code appeared to work, but error handling was inadequate.

An even more dramatic case is that of Replit, where the Vibe Coding service deleted an entire production database because the AI generated faulty SQL commands. Users did not understand the code and blindly trusted the AI, which is programmed for plausibility rather than correctness.

The middle ground

AI as a tool, not a replacement

Vibe coding is not inherently bad; it depends on the context. For real prototypes, one-off data analyses, or learning projects, for example, the 'as long as it works' approach can be perfectly reasonable.

Success stories: When vibe coding works

Despite the risks involved, there are certainly positive examples of successful vibe coding. Companies are using AI-generated scripts for internal automation, covering everything from data preparation to report generation. Marketing teams are using AI to create A/B testing tools that reduce development cycles from months to days.

The key lies in conscious limitation: these tools remain within their original context and are regularly reviewed and documented. Such experiments often yield valuable insights for professional development projects.

Vibe coding is particularly valuable for learning purposes, as developers and non-developers alike can experiment with new technologies, understand APIs, and explore possibilities. This playful approach lowers inhibitions and promotes innovation, provided the boundaries remain clearly defined.

Problems arise when:

• The code is running in production
• Others depend on it
• data is being processed
• The tool needs to scale

Rather than banning AI tools, a conscious distinction should be made between rapid prototyping and sustainable software development.

Companies need clear rules in the form of guidelines and checklists on vibe coding, for example. Which tools can be created by non-developers? When does IT need to be involved? How do prototypes become real products?