Vibe coding: how AI assistants erode engineering thinking

A programmer who doesn't write code sounds like an oxymoron. Yet that's exactly what a growing approach called "vibe coding" looks like today. Mark, an ML-engineer with more than four years of machine learning work behind him, decided to test this method on himself — and the results proved both impressive and alarming.

The term "vibe coding" itself emerged in early 2025 and quickly took hold in professional slang. The concept is simple: a developer formulates 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: he describes the desired behavior, checks the result, and adjusts direction, without diving into syntax and architectural decisions.

Mark went even further — he took on a project in an unfamiliar programming language, with an unfamiliar technology stack, and in a matter of days got a working application. To an outside observer, this looks like magic. To the industry, it looks like a signal that cannot be ignored.

However, behind the facade of quick results lurks a serious problem. Mark is honest about it: along with the application, he "vibe coded" a substantial portion of technical debt. The code generated by the AI assistant works — but it works like hastily assembled furniture from a hardware store. It performs its function until the first serious test. The architectural decisions made by the neural network often turn out to be fragile, redundant, or simply strange. Debugging such code is harder than writing it from scratch, because the developer who didn't participate in its creation is forced to figure out someone else's logic, which has no author to explain their decisions.

But Mark's most alarming discovery lies not in the technical, but in the psychological realm. He calls it the "curse of easy wins": after prolonged work with AI assistants, he noticed a marked decline in his willingness to independently tackle complex problems. If previously encountering an incomprehensible error triggered a research process — reading documentation, experimenting, deep diving into the problem — now the first impulse is different: copy the stack trace and send it to the AI chat. This is not laziness in the conventional sense. This is a restructuring of cognitive habits, in which the brain stops training the skill of independent problem-solving because it gets "quick dopamine" from an instant answer.

This effect echoes a broader discussion that has unfolded in the tech community. Researchers from Harvard and Microsoft documented last year that developers who actively use AI assistants demonstrate declining critical thinking over time when reviewing code. They more often accept generated solutions without verification and less frequently ask themselves "but why exactly this way?" Vibe coding takes this tendency to its logical limit: if classical AI assistants help write code, then vibe coding completely delegates the process, leaving humans only the function of acceptance.

To be fair, Mark doesn't demonize the approach. He identifies situations where vibe coding is genuinely appropriate: rapid prototyping, creating disposable scripts, testing hypotheses at early product stages. 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 support are required. Production systems working with user data, financial services, infrastructure code — here delegating thought to the machine transforms from an accelerator into a time-bomb waiting to explode.

In broader context, Mark's story reflects a fundamental question anyone working with intelligent tools will face: where is the boundary between augmenting human abilities and replacing them? A calculator didn't teach us not to think — it freed up thinking for higher-order tasks. But a calculator never claimed to think for us. AI assistants do. And if an engineer stops understanding the code he runs in production, he stops being an engineer — regardless of how impressive the result looks.

Vibe coding won't go away — it's too convenient and too effective for a certain class of tasks. But the industry will need 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 complex things independently remains the chief asset of a professional.