Vibe Coding: How AI Assistants Erode Engineering Thinking

A programmer who doesn't write code sounds like an oxymoron. Yet that is what a growing approach known as "vibe coding" looks like today. Mark, an ML engineer with more than four years of experience in machine learning, decided to test the method on himself, and the results turned out to be both impressive and frightening.

The term "vibe coding" appeared in early 2025 and quickly took hold in professional slang. The idea is simple: the developer states a task in natural language, and an AI agent, whether Claude, Cursor or another tool, generates the code. The programmer acts more like a product manager than an engineer: describing the desired behavior, checking the result and correcting course without diving into syntax or architectural decisions. Mark went even further. He took on a project in an unfamiliar programming language with an unfamiliar technology stack, and within days had a working application. To an outside observer it looks like magic. To the industry it is a signal that cannot be ignored.

Behind the facade of quick results, however, lies a serious problem. Mark openly admits that along with the app he "vibe-coded" a sizable portion of technical debt. The code generated by the AI assistant works, but it works the way flat-pack furniture from a hardware store works: it does its job until the first serious test. Architectural decisions made by the neural network are often brittle, redundant or simply strange. Debugging such code is harder than rewriting it, because a developer who did not take part in creating it has to untangle someone else's logic, and that logic has no author who can explain its choices.

But Mark's most troubling discovery is psychological rather than technical. He calls it the "curse of easy wins": after extended work with AI assistants, his willingness to work through hard problems on his own dropped noticeably. Where an unfamiliar error once kicked off a process of investigation, with documentation reading, experiments and a deep dive into the problem, his first impulse is now different: copy the stack trace and paste it into an AI chat. This is not laziness in the everyday sense. It is a rewiring of cognitive habits, in which the brain stops training the skill of solving problems independently because it gets a "quick dopamine hit" from an instant answer.

The effect echoes a broader debate in the tech community. Researchers from Harvard and Microsoft recorded last year that developers who actively use AI assistants show, over time, reduced critical thinking during code review. They accept generated solutions without checking more often and ask "why exactly this way" less often. Vibe coding pushes this trend to its logical limit: classic AI assistants help write code, while vibe coding delegates the process entirely, leaving the human only the role of acceptance.

In fairness, Mark does not demonize the approach. He identifies situations where vibe coding is genuinely appropriate: rapid prototyping, throwaway scripts, testing hypotheses in the early stages of a product. When the cost of error is low and speed is critical, AI code generation becomes a powerful accelerator. Problems begin when vibe coding is applied where reliability, security and long-term maintenance are required. Production systems handling user data, financial services, infrastructure code: here, delegating thinking to the machine turns an accelerator into a time bomb.

In a broader context, Mark's story reflects a fundamental question that everyone working with intelligent tools will face: where is the line between amplifying human abilities and replacing them? The calculator did not make us forget how to think; it freed our minds for higher-order tasks. But the calculator never claimed to think for us. AI assistants do. And if an engineer stops understanding the code they push to production, they stop being an engineer, no matter how impressive the result looks.

Vibe coding is not going away: it is too convenient and too effective for a certain class of tasks. But the industry will have to learn to handle it the way it once learned to handle outsourcing: understanding what can be delegated, what cannot, and why the ability to do hard things yourself remains a professional's main asset.