Vibe Coding: Why You Shouldn’t Trust AI-Generated Code in Production

According to GitHub, more than 40% of developers now use Copilot. But are we trusting AI too much, too fast? A new trend called vibe coding is gaining traction. While it can accelerate development, it also introduces silent risks — especially when AI-generated code is deployed in production without thorough review.

As AI-assisted development tools become mainstream, developers are increasingly relying on tools like GitHub Copilot or ChatGPT to write code with minimal understanding of the underlying logic. This trend, commonly referred to as vibe coding, poses hidden risks, especially in production-grade systems. In this article, I explore what vibe coding is, its dangers, and how developers can avoid its pitfalls.

What is vibe coding?

Vibe coding is an emerging development method where users interact with large language models (LLMs) through prompts to generate code. It resembles pair programming — but with AI taking on most coding tasks. The user mainly supplies requirements and ideas for how the software or application should function.

While this speeds up development, it can also lead to fragile or insecure code, especially when users don’t fully understand what the generated code does.

Does AI generate good code?

AI can generate high-quality code. I tested Claude Sonnet 4, GPT-4.1, and GPT-4o — all capable models. GitHub Copilot currently uses a variant of Codex (based on GPT), and some newer tools integrate models like Claude. These LLMs are trained on vast datasets, including public repositories (GitHub, GitLab, Bitbucket), Stack Overflow, and cloud vendor docs (AWS, Microsoft, Google, etc.).

However, AI can make mistakes. It might become biased, confused, or inefficient depending on the task. In one case, it got stuck in a troubleshooting loop, drifting into unrelated parts of the codebase and suggesting irrelevant changes. This is known as hallucination risk — when AI outputs plausible but incorrect results.

Even the most experienced developers struggle to keep up with all available information. AI bridges this gap — but not without trade-offs.

How does AI help you create software?

• Memorising syntax: If you’re new to tools like Terraform, memorising templates to deploy on AWS can take hours or days. AI can help you skip that by generating the required code, allowing you to focus on concepts rather than syntax.

• Reviewing and refactoring code: AI can review code and suggest improvements based on software engineering best practices. It can also help refactor existing code, set up repositories with standard templates, or clarify complex implementations.

Does vibe coding work in enterprise environments?

Enterprise applications are far more complex than personal or hobby projects. These systems involve integrations, compliance requirements, and strict performance expectations.

Even though AI tools promise increased productivity, developers must approach them with caution:

• Always review AI-generated code before deploying.
• Ensure the code aligns with business and technical requirements.
• Consider performance, maintainability, and clarity — not just functionality.

If you’d review code from a freelancer, you should review AI-generated code too. The responsibility still lies with you.

One key metric is cyclomatic complexity — a measure of how complex a program’s logic is. AI code might work, but be unnecessarily convoluted. Developers with domain knowledge can often build simpler, more maintainable alternatives.

The dangers of using AI code in production environments

Several studies have raised red flags about AI-generated code:

• A 2021 study by NYU found that 40% of GitHub Copilot-generated code had security vulnerabilities.
• A 2023 paper by Yin et al. found that AI tools increase productivity, but may encourage skipping essential code reviews.
• Even OpenAI has acknowledged that human review is critical, especially in regulated industries like finance, security, and healthcare.

💡 Tips to Prevent Vibe Coding Mistakes

To stay safe and productive when using AI:

• Understand before you accept: Never copy or use AI-suggested code without understanding it.
• Use linters and static analysis tools: ESLint, SonarQube, Snyk, etc., can catch insecure or non-compliant code.
• Avoid over-optimizing prompts: Don’t rely on prompt tweaking to force a working solution — focus on clean, correct code.
• Run code reviews: Have a second pair of eyes (human) check the code before it goes live.
• Use secure templates: Start with project scaffolds that follow best practices.
• Build a security culture: Train your team to view AI as a tool — not a substitute for engineering judgment.

Conclusion

AI tools are here to stay, and they offer massive potential to boost development workflows. They’re great for collaboration, prototyping, bug fixing, and automation of repetitive tasks.

But every AI-generated snippet carries the risk of error.

Responsible AI use means pairing speed with scrutiny. Combine these tools with human expertise to build software that is not just fast — but reliable, secure, and production-ready.

About the Author

Bruno Omizu is a Solution Architect with over a decade of experience in software engineering, solution architecture and technology leadership. He helps teams design and implement secure, scalable, and maintainable enterprise-grade systems across diverse industries.

Follow Bruno on LinkedIn or Medium.

📚 References

• Pearce, H., Ahmad, S., Acar, Y., & Mazurek, M. L. (2021). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions. arXiv:2108.09293
• Yin, P., Wang, W., Wattenberg, M., Zhang, M., Khandelwal, A., Chen, E., & Polozov, O. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv:2302.06590