What is Vibe Coding - and why it's all the tech-LinkedIn talk

You don't need to be an engineer to start building a digital product. Vibe coding is an approach to programming where human enquiry comes first, not syntax. Instead of remembering how to write a specific function, you describe the goal in simple language, and AI helps turn that into code.

However, unlike classic low-code/no-code, flexibility is preserved here: the developer can modify the architecture, fix bugs, and enhance the result. This is not a ‘designer’ with limitations, but a full-fledged collaboration between humans and AI.

Where did the term come from?

The term started gaining popularity in 2024, when developers began using AI tools not as auxiliary tools, but as partners. Articles on HackerNews, DataCamp blogs, projects on Replit, and in the Rewind open-source community showed that it is possible to interact with code as a live interlocutor. Explain an idea, clarify nuances, ask a question, and get the result.

This is what they called vibe coding: when you feel the direction more than rigidly follow the syntax.

Why it's important

Vibe coding has revolutionized the very logic of entering the IT field. Previously, without 2 years of training and mentorship, a person could not write even a simple application. Now, an MVP can be built in a day, without a single line of code being written by hand.

It has given a voice to those who were previously silent. Entrepreneurs, designers, prodcts. And it made developers look at their profession from a new angle: not just ‘writing code,’ but also managing logic, architecture, and the process of interaction with AI.

How does Vibe Coding differ from traditional programming?

Traditional programming is, first of all, about precision. A developer must clearly understand how the language works, which libraries to use, how the architecture is built, and which design pattern is appropriate. A mistake in syntax, and nothing works. Any new task requires a deep dive, sometimes from scratch. That's not a bad thing. It's the foundation of engineering. However, this approach comes with a price: it requires a significant amount of time and effort, especially at the outset, and involves a high technical threshold.

Vibe Coding offers a different role: the developer no longer sits at the wheel; he stands at the controls. His task is not to manually write each module, but to instruct the AI on precisely what needs to be built. The Vibe approach does not require a specialist to have complete control over every line - it requires an understanding of the essence of the task, the ability to make the right queries, test hypotheses, and adapt quickly. It is not a ‘classic coding show’, but rather a stage direction: you set the tone, introduce the context, watch the scene unfold, and intervene if the actors (in this case, LLM models) go off course.

It's not about replacing knowledge, it's about changing focus. Instead of spending time on the infrastructure, the programmer spends more time on the problem itself. For example, in a market analytics problem, he doesn't need to build a data collection pipeline manually; he just needs to explain to the LLM where to collect data from and what to collect, and then focus on analyzing the results.

Vibe Coding reduces the barrier to entry while enabling experienced professionals to work faster. It takes the load off routine steps, freeing up space for product and research thinking. It's an approach where it's not the perfect code as an artefact that matters, but the speed of getting a solution.

Pros of Vibe Coding

Quick Start Ideas

You describe a task in simple words - AI translates that into code. The DataCamp blog presents a scenario: ‘Create an RSVP page...’ - and AI immediately generates HTML and JavaScript, then fixes bugs via feedback. This allows you to have a working prototype in a couple of hours, making it especially useful for project startups and MVP launches.

Simplifying routine tasks

You say to AI ‘generate a signup form’ - and the answer comes practically out-of-the-box. You're explicitly stating that routine tasks are the AI's job, while you focus on architecture, UX, and product logic. This saves time and offloads developers.

Accessibility to a wide range

An entrepreneur with no coding experience or a designer can prototype and test ideas independently. This expands the audience of IT tools to include people from business and marketing, who previously only observed.

Innovation Space

Freely experimenting is akin to a fast, cyclical creative process. Even DataCamp emphasises: ‘You just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works.’ Lots of ideas can be tested in just a day.

Cons of Vibe Coding

Quality and Scalability

AI will generate working code, but this is not always reliable. DataCamp warns that code can grow into ‘rubbish’ over time, harder to understand and maintain. It is experienced developers who can stabilise it.

Security risks

AI often lacks input validation and error handling, making it easy for vulnerabilities to creep into the system. Trusting AI entirely without manual reviews is fraught with problems in production.

AI dependency

Newcomers who are accustomed to vibe coding sometimes struggle to understand what AI creates and why the code works. DataCamp notes that if a tool produces ‘hallucinations’ or fake data, a person can accept it if there are no control skills.

Not suitable for complex tasks

AI is good at creating simple components, such as pages and functions. However, large systems, complex business logic, and multi-module projects with a large amount of data require insight, architectural solutions, and system thinking, which AI is still far from achieving.

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Influence on industry

Changing the role of developers

Today, IT specialists are not just writing code, but also serving as AI designers and curators, responsible for the quality and safety of the generated material. Prompt Engineering skills, checks, and integrations become as crucial as classical programming.

Shift in a startup environment

Startups working with Vibe Coding can do everything on their own, without large teams and budgets: from idea to MVP in days, not months. This accelerates market entry and reduces the cost of the initial cycle.

New approach to training

Now you need to teach not only the syntax, but also competent work with AI, focused on “understanding VS copying”. DataCamp calls for using AI as a tutor: to explain every line, clarify logic, and not just copy and paste.

The emergence of new professions

The roles of “Specialist of the Prompt-Engineer”, “AI-Integrator”, and “Code-Revoy Voice” specialists, who help others work with AI and keep it under control in the codebase, appear.

Partial implementation in large companies

Although a large business, such as Visa, Reddit, or Doordash, is tested by Vibe Coding, it does not yet claim to be completely restructuring its processes. The speed of implementation accounts for 40% of the increase in productivity so far, primarily on “fences” rather than fundamental tasks.

Final thoughts

Vibe Coding is not magic; this is a new model of work: a person says, AI writes, but a person analyzes, checks, and builds architecture. Where you need quick prototyping, it works fine. Where reliability, understanding, and control are essential, you must return control to the developer.

The future is closer, the tools can be trusted - but only until a person remains nearby who knows what and why. And that is why experts who can use Vibe Coding effectively become key in a modern IT environment.

If you are CTO, CEO, or Team Lead, now you should ask yourself two questions:

• How ready are you to use Vibe Coding for a quick prototype or minimum viable product (MVP)?
• What needs to be introduced to ensure quality, safety, and a smooth production process?

The answer to them will help introduce AI in a way that makes it truly work for business goals, without creating risks.