Vibe coding is fast, and it feels great in a demo. But production is unforgiving — it doesn’t care that the code was fun to write. This guide walks through what actually goes wrong when AI-generated code reaches real users, and how to ship it safely. The goal here isn’t to scare you off AI; it’s to make sure you understand what you’re signing up for.
What does vibe coding mean?
Vibe coding is building software by describing what you want in natural language and letting an AI model write the code, then accepting the results with minimal review. The phrase comes from the idea of going with the “vibe” of the model rather than reading every line it produces. At its best it’s a fast way to prototype; at its worst it’s committing code nobody has truly read.
Why is vibe coding risky in production?
Because production rewards understanding, not speed. Code that works in a demo can fail under real traffic, real users, and real attackers — and AI-generated code is no exception. The risk isn’t that AI writes bad code. It’s that the whole approach skips the step where a human actually understands what the system does, and that understanding is the only thing that protects you later.
How does vibe coding create technical debt?
Technical debt is code you don’t fully understand. Vibe coding produces a lot of code you didn’t read, which means it produces a lot of debt in a single sitting. The usual pattern:
- Unvetted dependencies. The model picks libraries by name, sometimes hallucinating packages that don’t exist or pulling versions with known issues.
- Duplicated logic. Asked to solve a similar problem twice, the model writes the same solution twice, and later fixes land in only one place.
- No tests. Generated code rarely comes with meaningful tests, and skipped tests mean every future change is a gamble.
- Patterns from training data. The model mirrors patterns it saw elsewhere — which may be outdated, over-engineered, or cargo-culted from another project’s context.
None of this is fatal on its own. It compounds because debt you can’t see accumulates faster than debt you consciously chose.
What are the security risks of vibe-coded software?
Security is where unexamined code is most dangerous. Common failure modes include:
- Repeated insecure patterns. Models reproduce patterns from their training data, including older, insecure ones — SQL built by string concatenation, weak defaults, missing input validation.
- Hallucinated packages. A model can invent a library name that looks plausible. If that name is later registered by someone else, installing it is a supply-chain attack waiting to happen.
- Overly permissive configuration. Models tend to generate the simplest working config, which often means wide-open CORS, permissive file permissions, or secrets in version control.
- No threat model. Nobody asked “who can reach this, and what can they do?” because nobody reviewed it at all.
The uncomfortable truth: a vulnerability in code you wrote yourself is a mistake. A vulnerability in code you didn’t read is a liability you accepted sight unseen.
What happens to scalability?
A lot of vibe-coded software works perfectly for ten users and collapses at ten thousand. The classic issues are N+1 database queries, synchronous work where async is needed, no caching, missing connection pooling, and memory leaks that only show up under sustained load. These aren’t mysterious — they’re the exact things a thoughtful engineer checks before shipping. When nobody reviews, they ship, and the production incident becomes the code review that should have happened earlier.
What about code quality and maintainability?
Code that nobody read is code that nobody can confidently change. Vibe-coded codebases tend to accumulate inconsistent naming, giant functions, swallowed errors, and no documentation. The first developer who has to extend the system spends their time reverse-engineering what the AI did instead of building. That cost is paid by the next person — and the next person is often you.
Is vibe coding ever a good idea?
Yes, in specific situations. Vibe coding is genuinely useful for:
- Prototypes and proofs of concept you expect to throw away
- Internal tools where a rough edge is acceptable
- Throwaway scripts and one-off automation
- Exploring unfamiliar APIs or languages
- Learning — generating code and reading it is a fast way to learn
The useful rule of thumb: vibe code what you’re willing to throw away, and review carefully what you plan to keep. The moment code is heading to production, it stops being a vibe and becomes engineering.
How do you ship AI-generated code safely?
Treat the AI like a capable but unsupervised junior developer. The practices that make generated code safe are the same practices that make any code safe — applied more strictly, because the volume of code is higher:
- Review everything. No change goes in without a human reading the diff. This is non-negotiable.
- Write tests before you trust it. Generate the code, then generate and review tests for it. If you can’t test it, you can’t ship it.
- Scan dependencies. Run supply-chain and vulnerability scanning on every generated dependency, and pin versions.
- Enforce security checks. Static analysis, secret scanning, and a threat-model pass on anything that touches user data or the network.
- Keep a named owner. A human is accountable for the system end to end, including the AI-written parts.
- Monitor in production. Even reviewed code fails; monitoring, alerting, and a rollback path are what contain the damage.
This is the same discipline we build into our production readiness work and our broader managed services — because the tools change, but the responsibilities around them don’t. If you’re unsure whether the AI-generated code in your stack is production-safe, a good first step is a conversation with a team that looks at these systems every day.