Disclosure: This article was created or edited with AI assistance. It is educational guidance, not a report of measured results. No measured productivity, hiring, sales or income outcome is claimed. Examples are illustrative unless a source is explicitly given.
The phrase "vibe coding" is used for a style of working with AI-generated code. You type a prompt, AI writes the code, and you ship it without fully understanding what happened.
Sounds amazing, right? Here's why it's actually dangerous.
What Is Vibe Coding?
In this article, it means using AI to write code based on vibes — you describe what you want, accept the output, and move on. No deep understanding required.
Why It's Popular
- Drafting — Generate a starting point to inspect
- Accessibility — Non-coders can build apps
- Dopamine — Instant results feel amazing
The Hidden Dangers
1. You Can't Debug What You Don't Understand
When your AI-generated code breaks at 3 AM in production, and you have no idea how it works — what do you do? Call ChatGPT?
2. Security Vulnerabilities
AI models are trained on public code, including insecure code. SQL injection, XSS, improper auth — AI doesn't always catch these.
3. Technical Debt Bomb
Accepting successive changes without reviewing their design can leave unclear dependencies and inconsistent behavior.
4. Career Ceiling
Keep learning the system behind the generated code so you can investigate its failures and evaluate changes.
The Right Way to Use AI
- Use AI as a co-pilot, not autopilot
- Read every line AI generates
- Understand the architecture first, then use AI for implementation
- Design tests around expected behavior and inspect any generated tests
- Learn fundamentals. They never go out of style.
Record the assumptions, inspect the changes and verify behavior in the actual project. A generated explanation is not proof of correctness.
Top comments (3)
Test Driven Development is more important in vibe coding, also the fundamental strong concept. Nor do not need to check every code lines which you deliver, because you don't need to know a target programming language, in my example I just have a partial knowledge of rust and finnaly the binary is wasm + js/html. You need to know how your code is work, and where is the problematic parts.
Time to time ask AI analyze your code.
Last advice: Do not use any dependency.
I think that everyone should vibe code something into a dumpster fire: it is the fastest way to understand the downfalls. Also, TDD is not enough unless you have adversarial AI writing independent tests for another AI to pass. When I tried TDD the AI happily rewrote the tests to match the new code that it wrote.
There are ways to do it to avoid most of the problems you mention. Using two models, one to check up / critique the other works really well for starters. I've recently created applications in languages I understand very well and the outcome was pretty great. AI is also constantly improving.