Even Linus Torvalds himself admitted to using AI-powered vibecoding. This news rocked the community: some saw it as the death of the programming profession, others as a magic wand that would do all the work for them. Both are wrong.
Let’s be honest about what vibecoding actually is and what it definitely isn’t.
What is vibecoding?
First, let’s get things straight. Vibecoding is an approach to development where an engineer focuses not on writing syntax (code), but on controlling system behavior through natural language (prompts) and an intuitive understanding of the task.
Previously, programming was like building a house brick by brick. You had to know the composition of the cement, the types of masonry, and the state standards for rebar. If you forgot a semicolon, the wall would fall (the compiler would throw an error). It was a battle with syntax.
And vibecoding can be called jazz improvisation with a supercomputer. The conductor tells the orchestra, “Give me more drama here, and then we’ll go into a major key.” And the result is more important than the process. Who cares what the underlying cycle is if the business problem is solved in three minutes and works flawlessly?
But several dangerous myths have already grown around this new term. We’ll address them below.
Myth 1: “Now you don’t need to learn the hardware. AI will do it all itself.”
Reality: Without fundamental knowledge, you’ll turn into a garbage generator.
Many beginners think, “Why should I know how databases or APIs work if I can just ask ChatGPT?”
The problem is that AI is an executor, not an architect. It can write code that looks functional, but is unoptimized, unsafe, or architecturally flawed.
- Linus Torvalds can afford to vibecode in Python because he has decades of experience in systems programming. They intuitively sense how the algorithm should work and can instantly assess the quality of the AI’s output.
- A beginner, ignorant of the basics, will simply accept the first piece of code from a neural network they come across. And when (not if, but when) this code breaks, they will be unable to find the error or fix the prompt.
Conclusion: Vibe coding is a multiplier of your experience. If your experience is zero, then 0 * AI = 0.
Myth 2: “Vibe coding is easy. Just say ‘make a website’ and you’re done.”
Reality: Prompt engineering is a new, complex discipline.
Recall the Claude Cowork case from Anthropic. An AI agent that writes code itself can “perform destructive actions on files” if the command was unclear.
The cost of a prompt error has increased dramatically. Previously, a coding error resulted in a Syntax Error. Now, a mistake in a prompt can lead to a database deletion or a data leak, because the AI ”understood the problem that way.”
Vibrational coding requires extreme clarity of thought. You must be able to decompose a problem, describe edge cases, and set constraints. This isn’t a magic button; it’s controlling a complex intelligent agent.
Conclusion: yes, you no longer write code by hand, but you think through the logic in your head. And this is often more difficult.
Myth 3: “This is only for MVPs and toy projects.”
Reality: yes, this tool really does significantly speed up enterprise development, but under strict control.
Skeptics say, “Well, sure, you can put together a landing page, but you can’t build a serious fintech project on prompts.” Don’t close your ears. There really is a grain of truth in this. The truth is, you can’t just hand over the task of “build a new app” to an AI and go get coffee.
This tool works great in the right hands. Professionals know that vibe coding is incredibly good for:
- Writing boilerplates and tests: what used to take hours is now done in seconds.
- Refactoring and finding vulnerabilities: AI sees patterns better than humans.
- Creating microservices. We’ve already discussed how AI is ideal for generating small, isolated functions (microservices) that solve a single problem.
Serious projects are built not on magic, but on the orchestration of such AI solutions by experienced architects.
So, who is the vibe coder of the future?
They’re not a lazy junior copying answers from ChatGPT.
They’re an AI Architect who:
- Understands the business problem better than the client
- Knows technical constraints and architectural patterns
- Speaks the language of prompts like a native
- Can verify the result (Code Review for neural networks)
The market is changing. Classic coding is becoming a thing of the past, giving way to the management of meanings. But this doesn’t mean the work will become easier. You’ll have to work with your head, not your fingers.
