Not so long ago, programming was considered a discipline of precision: syntax, logic, testing, commit. Today, a term is emerging that challenges these fundamentals—vibe coding. A new generation of developers is abandoning the classic IDE (Integrated Development Environment, such as Visual Studio Code) and instead typing sentences like “Build me a dashboard that visualizes sales per region, but please give it a modern look” into an AI console. The rest happens automatically.
What initially sounds like a buzzword actually describes a profound change in software development: the transition from explicit coding to implicit, AI-supported generation. Instead of dealing with frameworks, libraries, and API specifics, people are increasingly working at the meta level—they define intent, tone, and direction. AI does the rest.
However, this change is not so extreme that it will render the profession of developer obsolete and allow anyone to develop comprehensive software solutions without much difficulty. We at Vialutions have tested various use cases and formed a fairly clear opinion.
But first, let's take a closer look at the term vibe coding.
Term, origin, and concept of vibe coding
Vibe coding is not an official standard, but rather a cultural phenomenon that has emerged from the growing convergence of generative AI and software engineering. The name alludes to the idea that you no longer write code, but rather describe the feel of an application – the “vibe.”
At the heart of vibe coding is the separation of problem understanding and technical implementation. Whereas in the past the two were inseparable—a developer had to know the logic in order to implement it—vibe coding delegates the second part entirely to AI systems.
Vibe Coding aims to radically change the way we work: developers write prompts instead of functions, teams discuss concepts and user experiences instead of technical implementations, iterations are created not by code changes but by modified inputs in natural language, and the focus shifts from a purely technical perspective to a creative one.
Opportunities and strengths in theory
1. Speed: Small applications and MVPs can be generated in an afternoon. This is revolutionary for startups and agencies under high pressure to innovate.
2. Democratization of software development: Suddenly, anyone with a clear vision can “develop” software. Access to the implementation of complex ideas is no longer limited by programming skills.
3. Creative focus: Since technical details are no longer necessary, the focus shifts to concept, design, and user experience.
At first glance, these advantages sound spectacular. But before we discuss what we think of Vialutions, let's take a look at the disadvantages:
Risks and weaknesses
1. Loss of control: If you don't know what is being generated in the background, it can be difficult to identify errors.
2. Dependence on models: Quality depends entirely on the AI infrastructure. Bias, misinterpretations, or simply incorrect training data can flow directly into productive systems.
3. Loss of technical expertise: When generations of developers no longer see the need to understand code, a dangerous loss of knowledge occurs.
4. Copyright and compliance: Vibe Coding is based on models that have learned from external code. The legal responsibility for generated code—especially in a commercial environment—remains unclear.
5. Lack of traceability: In traditional systems, every decision in the code can be traced. In vibe coding, implementation is a product of emergent processes. This makes debugging and certification difficult.
6. Maintainability issues: Maintainability is one of the most problematic aspects of the vibe coding approach. The generated code is often formally correct but semantically inconsistent, and there are no clear commit logs or traceable merge paths. Since code versions are often completely regenerated from a new prompt, the connection between the change and its cause is lost. This makes auditing, regression testing, and impact analysis considerably more difficult. In short, vibe coding can accelerate development, but it shifts the maintenance effort from the code level to the AI governance level.
Vibe Coding bei Vialutions: An experiment
Vialutions specializes in customized software and applications with a team of over 40 architects, developers, designers, and testers. What could sound better than being able to develop applications in no time with the help of AI?
That's why we tested various use cases. One of them concerns our web application vPortal, which we use for time tracking and absence management, among other things. One team developed the optimized version vPortal 2.0 in the traditional sense, while another team had vPortal 2.0 developed with the help of AI, i.e., Vibe Coding.
A classic problem with working with AI quickly became apparent: it tends to overengineer, adding layers, frameworks, and dependencies that are not absolutely necessary because they were “common in the training data.” Such patterns increase complexity and thus long-term maintenance costs. In addition, the expected time savings may be negated by overengineering.
In our case, this concerned a calendar function, for example. There are existing open source solutions that could be used for this, but the AI “decided” to develop the function from scratch. Contrary to theory, the “AI team” was not busy iteratively optimizing the application, which was created in no time at all, with its prompts, but spent most of its time testing and bug fixing.
In the end, a functioning web application was produced, but the team that had worked with Vibe Coding had no time advantage over the team that had taken the traditional approach. In short, development with the help of AI took just as long as programming from scratch.
Conclusion and outlook
In the short term, vibe coding will primarily play a role in prototyping, low-code environments, and SMEs. In our experience, the concept is suitable for small applications or MVPs, but not for more extensive application software.
In the long term, however, one thing is clear: as generative systems continue to advance, vibe coding will become a standard skill. Not as a replacement, but as an extension—a tool for turning ideas into tangible software in seconds. The new skill will no longer be “mastering Python,” but “prompt literacy”: the ability to think with the machine, formulate intentions precisely, and drive iterative improvements based on feedback.
Vibe coding is not the end of programming, but its next chapter. It is the moment when code ceases to be pure text – and becomes the language of thought.