What is vibe coding, and why did it take off now?
AI coding tools such as GitHub Copilot, Cursor, Claude Code, Codex, and others are not new. What changed is their capability and accessibility. Today, they do not merely complete snippets; they can write entire features, modules, and sometimes whole applications from a single prompt.
That is what created the vibe coding wave: non-developers who have never written production code are now creating small web applications, automations, and prototypes. The barrier to entry has dropped dramatically, and that is genuinely valuable in itself.
The problem begins where the barrier disappears.
The problem of evaluating the output
An experienced developer using AI knows when the output looks suspicious. They recognise poor patterns, security gaps, and architectural mistakes. They phrase prompts in a way that produces useful answers, then read those answers critically instead of simply accepting them.
A non-developer user cannot do this. Not because they are less intelligent, but because they lack the professional reference points needed for evaluation. If the AI gives a confident answer and the code runs, that does not necessarily mean the code is good.
This creates a few concrete risks:
- Security problems: AI-generated code may contain SQL injection vulnerabilities, flawed authentication logic, or passwords stored in plain text. If you do not know what to look for, you will not find it. If you do not know what to ask, the issue may only surface after damage has already been done.
- Technical debt: A typical vibe coding outcome is a working but unmaintainable codebase. Every "just do it quickly" prompt can add another layer of disconnected logic. Six months later, nobody understands why things are there, including the AI.
- False confidence: The code runs. The tests are green. The user thinks it is done. Then production reveals dozens of edge cases nobody considered.
So what is vibe coding good for?
Not for replacing experienced developers. It is useful for the things it is actually suited to:
| Use case | Fit and rationale |
|---|---|
| Prototype or POC (proof of concept) | Yes. The goal is fast validation, not production-grade code. |
| Internal tool for a small team | Conditionally. It can work if someone supervises the codebase. |
| Production system serving customers | Not on its own. Security and quality risks are too high without oversight. |
| Automation, scripts, data processing | Yes. The scope is limited and the risk is usually lower. |
| Changing a legacy system | No. Missing context makes it unreliable. |
Where it works, and where it does not
Vibe coding works where the consequence is low, the scope is narrow, and someone in the background understands what the AI is doing.
The closer we get to customer data, payment flows, permission handling, or business-critical operations, the less sufficient it is that the code appears to work at first glance. At that point, the real question is not just whether something can be built, but whether it remains secure, auditable, and maintainable.
What does this mean for software developers?
It does not mean the profession is over. But the role is changing.
A senior developer today is no longer primarily someone who types code. They make architectural decisions, audit AI outputs, and design systems that AI can also help maintain. This is different work, but not lesser work.
The companies truly at risk are the ones that believe vibe coding replaces software development competence. It does not replace it; it shifts where that competence is needed. The question is not whether a developer is needed, but what kind of developer is needed, and for what.