The term “vibe coding” gained traction in early 2025, largely thanks to Andrej Karpathy, one of the most recognizable figures in the world of artificial intelligence. It quickly came to describe a new way of thinking about software development: rather than writing code line by line, a person tells an AI model what outcome they want to achieve and then guides it through successive refinements. The promise is compelling because it shortens the path from an idea to the first version of an application. At the same time, it does not remove the need to think about quality, security, accountability, and long-term code maintenance.
What is vibe coding?
Vibe coding is the process of creating or editing software with the help of a large language model or AI agent, in which the user describes their intent in natural language. In a narrower sense, the term refers to a way of working in which the user focuses primarily on what the application should do, rather than on fully understanding every technical decision made by the model. Instead of writing the code themselves, they describe what the application is supposed to do, how it should behave, and what outcome they want to achieve. The model then translates that description into instructions, structure, and pieces of code.
Using its own knowledge, additional tools, plugins, or access to the Internet, generative AI can propose solutions, generate code, test the result, and suggest improvements. As a result, the process increasingly resembles a conversation rather than traditional programming: a person describes the goal, evaluates the output, refines expectations, reports errors, and guides the model through successive iterations.
Why does vibe coding capture the imagination?
That is precisely what makes vibe coding so appealing: it lowers the barrier to entry into the world of software development. Even someone with no knowledge of programming languages can see a working result relatively quickly: a simple prototype, a personal-use application, a form, a report, or an internal support tool. In these kinds of use cases, that can deliver real value, because it allows an idea to become the first version of a solution without a long technical process.
From a company’s perspective, that promise is especially tempting. Organizations are full of small improvements and ideas that are too minor to compete with major IT projects but still important enough to make everyday work easier. Vibe coding makes it possible to quickly test whether a given direction makes sense, show it to users, and start the conversation with something tangible—not a slide deck or a description in a document, but a version that people can see, click through, and evaluate.
Vibe coding is not the same as using an AI programming assistant
Not every use of AI in software development qualifies as vibe coding. It is better to think of it as a spectrum of control: from AI-assisted programming to a situation in which the user mainly describes the desired outcome, while the tool largely decides how to achieve it.
The safest variant is using AI as a programming assistant. The human still understands the system architecture, knows the business requirements, makes technical decisions, reviews the code, tests it, and remains responsible for the quality of the solution. The model helps with execution-level tasks: generating pieces of code, tests, documentation, or refactoring suggestions.
Further along that spectrum are agentic tools that can modify files on their own, run tests, diagnose errors, and propose further changes. They can speed up the work, but they require oversight because they often solve a local technical problem without the full business, legal, or architectural context.
Vibe coding begins when control clearly shifts toward the tool. The user describes an idea or an expected outcome, and AI chooses the way to implement it: it structures the code, makes assumptions, and connects the components of the application. The human then evaluates mainly what is visible on the screen, without always knowing what technical decisions were made underneath. The less they understand the generated code and its consequences, the more important subsequent technical verification becomes.
The biggest illusion: a working screen is not a working system
The biggest trap in vibe coding is how easy it is to mistake a visible result for the quality of the solution. An application may launch, a form may save data, and the interface may look correct, but that does not mean the system has been well designed. The most important parts often sit beneath the surface: architecture, data validation, permissions, error handling, security, performance, and the ability to extend the system later.
AI generates solutions based on patterns learned from data and on the context provided in the task. As a result, it can produce code that looks correct and works in a typical scenario, but fails to account for specific business, architectural, or security constraints. Sometimes the output is right; sometimes it is only almost right. And that “almost” can be the most dangerous part, because the error may not be visible on the first click.
That is why a good prompt is no substitute for good requirements. The more superficial the problem description, the more superficial the result may be. Vibe coding does not remove the need for analysis, design, and quality control. It merely shifts the burden from writing syntax to formulating decisions precisely and checking their consequences.
Where vibe coding makes sense
Vibe coding works best where speed, experimentation, and a quick test of an idea matter more than production-grade quality from the outset. Its natural domain is low-risk work that helps determine whether a given direction makes sense at all.
The most obvious use cases include:
- prototypes of new products or features,
- early MVPs with limited risk, especially in startups and small companies,
- application mockups for discussions with clients or teams,
- simple internal tools,
- automation of repetitive tasks,
- report generators,
- scripts for organizing data,
- personal-use applications,
- support in learning programming.
In these cases, speed is the main source of value. Instead of spending a long time describing an idea in a document, teams can build a version that people can click through, show it to users, and improve it quickly. Even if the application is later rewritten from scratch by a technical team, it can help clarify needs, reveal faulty assumptions, and prevent investment in the wrong direction.
There are conditions, however. Vibe coding makes the most sense when the solution:
- does not process sensitive data,
- does not handle payments or critical processes,
- does not have a significant impact on user safety,
- operates at a limited scale,
- has a short or clearly defined lifespan,
- is treated as an experiment rather than as a ready-made foundation for a production system.
In practice, then, vibe coding can be a very useful tool at the beginning of work on a solution. It helps move quickly from an idea to something concrete. But it should not automatically mean that the first working version is ready for production deployment.
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Where the risks begin
The risks emerge when vibe coding moves beyond experimentation and starts touching systems where errors can have serious consequences. A form prototype or an internal report generator is one thing; an application handling payments, customer data, legal processes, healthcare, finance, or infrastructure components is quite another. In those contexts, it is not enough for a solution to work in a basic scenario.
The hardest cases are the ones in between: applications that are not as critical as a payment system, but are expected to run for longer, serve many users, or process company data. In such cases, the decision to use vibe coding should depend on the scale of the solution, the type of data involved, and its intended lifespan.
Security is one of the biggest concerns. AI can generate code that looks correct and meets the stated requirements while still failing to include many mechanisms that remain invisible to the user: data validation, access control, session protection, secrets management, file access restrictions, or resilience against API abuse. A non-technical person may not notice any of this, because the application behaves properly during normal use. Only an edge case, heavier load, or an attempted abuse may reveal that the foundations are missing.
Another source of risk is the data shared with AI tools. Prompts may include code fragments, logs, customer data, sample records, error messages, tokens, passwords, or descriptions of business processes. In a large organization, such information cannot circulate without control. Clear rules are needed: which tools may be used, what data can be entered into them, who approves that use, and how confidential information should be protected.
That is why, in critical systems, AI can be a valuable form of support, but it should not replace the software development process. Code must go through standard quality controls: review, testing, security analysis, dependency audits, and deployment checks. Vibe coding does not need to be banned, but its role should be clearly defined. The greater the responsibility of the system, the less room there is for uncontrolled experimentation.
The cost of seemingly cheap code
Vibe coding often lowers the cost of the first version. That is one of its major advantages: a prototype can be built in hours or days rather than weeks. The problem usually appears later, when the solution has to be maintained, developed further, or adapted to new requirements. At that point, it may become clear that quickly generated code is not always code that can be easily understood, fixed, or safely extended.
If an application is built without a thoughtful architecture, without quality control, and without someone who truly understands the technical decisions behind it, every subsequent change can become more difficult. A simple fix may break other parts of the system, bugs may be hard to reproduce, and the team may lose time trying to understand why something was written in a particular way. This is classic technical debt — only generated faster than before.
This problem is not unique to AI. Poor hand-written code can also become expensive. The difference lies in scale and speed. Vibe coding makes it possible to create a large amount of code very quickly, which means it can also create a large number of problems just as quickly—problems that may only surface during maintenance. That is why the first working version should not automatically be treated as a ready-made foundation for a product. Sometimes it is an excellent prototype. Sometimes it is only a sketch that needs to be redesigned from the ground up before deployment.
Responsibility, licenses, and vendors
If an application generated with the help of AI fails, it will be hard to argue that the model is responsible. In practice, responsibility lies with the organization that decided to deploy the solution, and with the people who approved its use. That is why code created with AI support should be treated as seriously as any other production code. The fact that it was produced faster does not lower the requirements for quality, security, or compliance with company processes.
Licenses, intellectual property, and dependencies are a separate issue. An AI tool may suggest a library of questionable quality, rely on a popular pattern, or generate a fragment of code that no one can later justify. Companies therefore need to know which tools may be used, what data may be shared with them, how the use of AI should be documented, and how dependencies introduced into a project should be checked.
There is also the risk of vendor lock-in. If the entire approach to software development becomes dependent on a single model, platform, or tool, the organization becomes exposed to its pricing, terms of service, availability, and quality. In a small project, that may be acceptable. In a larger company, it requires a deliberate strategy: control over data, a migration plan, alternative tools, and clear rules for use.
How vibe coding changes the work of developers and teams
Vibe coding does not mean that a developer’s job is reduced to fixing whatever AI generates. On the contrary: the more code these tools can produce, the more important it becomes to judge whether that code actually makes sense. Developers remain responsible for architecture, quality, security, the data model, integrations, and anticipating the consequences of change. Writing syntax may take less time, but understanding the system becomes even more important.
AI is good at supporting the execution side of the work. It can help write a simple component, a test, a piece of documentation, a configuration, or repetitive code. It can also explain errors and suggest refactoring. But it does not take responsibility for the whole. The model does not always know the organization’s context, the history of earlier decisions, legal constraints, product development plans, or hidden dependencies between systems. And even if it is given that context, there is no guarantee it will apply it correctly.
Junior developers present a separate challenge. For beginners, AI can be an excellent learning tool because it allows them to experiment faster, ask questions, and compare different solutions. The risk appears when a junior developer starts merely accepting generated code without trying to understand why it works and when it might fail. In that case, AI does not build competence; it masks its absence. For companies, this is an important signal: future teams will need people who can not only generate code, but above all evaluate systems and take responsibility for their quality.
How to use vibe coding responsibly
Vibe coding is best treated as a tool for rapid experimentation, not as a replacement for a professional software development process. It can deliver significant value, but only when its scope and risks are clearly defined.
In practice, several principles are worth following.
- Use vibe coding where the risk is limited—it works best for prototypes, mockups, simple automation, internal tools, and early-stage MVPs.
- Start with the question: what happens if this solution fails? That assessment helps determine whether a quick experiment is enough, or whether a full engineering process is needed.
- Break the work into small, specific scopes. Instead of asking AI to build an entire application at once, it is better to ask for individual features, tests, components, or pieces of documentation.
- Read and verify the generated code. AI-generated code should be run, tested, and reviewed just like any other code, especially if it is intended for wider use.
- Set clear rules for data. Secrets, passwords, tokens, customer data, logs, and confidential parts of systems should not be entered into AI tools without organizational approval.
- Do not hand responsibility for decisions over to AI. AI can help create and improve solutions quickly, but responsibility for requirements, architecture, security, and quality must remain with people.
- Use a mixed approach. Business teams can use AI for prototyping, developers can use it to accelerate their work, and the technical team should decide whether a solution is suitable for further development.
Used this way, vibe coding delivers its greatest value: it shortens the path from idea to something concrete, without replacing responsibility for the final system.
Conclusion
Vibe coding should be treated as a tool for rapid experimentation, not as a replacement for a professional software development process. Its greatest value lies in helping teams move quickly from an idea to a prototype, while decisions about architecture, security, quality, and deployment remain in human hands. Responsible use of AI is not about handing over control. It is about applying it deliberately where it can accelerate work without increasing risk.
