Vibe Coding: What It Actually Is, When It Works, and When It Burns You
Vibe coding — describing software in plain language and letting AI build it — is real and useful. Here's where it succeeds, where it collapses, and how to do it responsibly.
"Vibe coding" started as a joke and became a workflow: you describe what you want in plain language, an AI writes the code, you look at the result rather than the diff, and you iterate on behaviour instead of implementation.
Dismissing it is easy and wrong. Taking it seriously as a replacement for engineering is also wrong. The useful position is in between, and it depends almost entirely on what you're building.
Where vibe coding genuinely wins
Prototypes and validation. Getting a clickable version of an idea in front of five users in an afternoon is enormously valuable, and the code's quality is irrelevant because you're going to delete it.
Internal tools. A dashboard used by four people in your own company, behind auth, with no sensitive data flow. The blast radius is small and the alternative was a spreadsheet.
Glue and scripts. One-off data transforms, migrations, scrapers, report generators.
Unfamiliar territory. Writing your first thing in a framework you don't know. The AI gets you to something running, and then you read it to learn.
Front-end iteration. Layout, spacing, states, responsive behaviour. Visual output is instantly verifiable, which is exactly the condition under which AI generation is safest.
Where it reliably burns you
Anything handling money, health data, or credentials. Generated code frequently ships permissive access rules, missing authorisation checks, and secrets in the wrong place.
Systems that must scale. Models optimise for "works now," not for query patterns at ten thousand rows.
Long-lived codebases. Without conventions, each feature is written in a slightly different style. At around fifteen features, the codebase becomes hostile to further change — including by the AI.
Debugging what you didn't read. If you can't explain how it works, you can't fix it at 2am. This is the real cost, and it arrives later than the benefit.
The rules that make it work
1. Specify before you generate. Write a paragraph on data model, user roles, and what must not happen. Two minutes of specification saves an hour of correction.
2. Keep a conventions file. State your stack, patterns, naming, and forbidden approaches. Every serious AI dev tool supports this, and it's the highest-leverage thing you can do.
3. Build in small, verifiable steps. One feature per request. Check it works before moving on. Big-bang generation produces big-bang debugging.
4. Read the security-relevant code, always. Auth checks, database access rules, anything touching user input, and anything calling an external API with a key.
5. Ask for tests and run them. "Write tests for this" costs one prompt and catches most regressions.
6. Version control everything. Commit before each significant change. The ability to revert cleanly is what makes fast iteration safe.
7. Know when to stop and refactor. When the AI starts breaking things it previously fixed, the codebase has outgrown its structure. Pause and reorganise.
The security checklist nobody runs
Before anything vibe-coded goes public:
- Row-level access rules on every database table, tested as an unauthenticated user.
- No API keys in client-side code — check the built bundle, not the source.
- Server-side validation on every input, not just client-side.
- Authorisation checks on every mutation, not just authentication.
- Rate limiting on anything expensive or public.
- Dependency audit for known vulnerabilities.
Most publicised failures of AI-built apps trace to exactly this list.
What this means for developers
The skill that's appreciating isn't typing code — it's specification, review, and architectural judgement. Knowing what to build, what shape it should be, and whether the thing in front of you is correct.
Junior developers who use AI to skip learning fundamentals will struggle when the generated code is subtly wrong. Junior developers who use it to read more code than they could have written, and to ask better questions, will move faster than any previous cohort.
The honest summary
Vibe coding is a legitimate and powerful mode for prototypes, internal tools, and front-end work. It is a liability for production systems handling anything that matters, unless someone who understands the code reviews it.
Use it to find out whether an idea is worth building. Then decide, deliberately, whether the prototype becomes the product — or the specification for one.
Related: Cursor vs GitHub Copilot and our best AI coding assistants review.
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