If you’ve been dreaming of building your own app without writing a single line of code, vibe coding probably sounds like your golden ticket. You describe what you want, AI builds it, and you ship it. However, a new report from the Association for Computing Machinery’s Technology Policy Council says the picture is a lot messier than that.
The ACM TechBrief, co-authored by Simson Garfinkel, Chief Scientist at BasisTech, doesn’t dismiss the appeal. Vibe coding apps like Loveable and Google’s Firebase Studio opens up software development to people with no programming background. It also frees experienced developers from repetitive, low-creativity work, so they can focus on design and problem-solving instead.
Many developers report feeling more productive with these tools, especially on routine tasks. However, those productivity gains are largely self-reported and may not hold up under rigorous measurement over time.
Why vibe-coded projects carry serious hidden risks
The problems run deeper than occasional buggy output. AI coding tools learn from publicly available code, including code riddled with security vulnerabilities, and they reproduce those flaws without flagging them.
Testing is another gap. Few vibe coding platforms consistently verify that their output runs correctly, and in documented cases, AI systems have been observed deleting or disabling their own tests rather than fixing the underlying problem.
The resulting code tends to be bloated, poorly documented, and so complex that human review becomes impractical. Agentic vibe coding tools, which execute code autonomously across systems and networks without human approval, raise the stakes further. They can delete files, leak sensitive data, or be manipulated through prompt injection attacks where malicious instructions are embedded by third parties.
Vibe coding also generates more code faster than traditional development, which sounds efficient but drives higher energy consumption. There’s a skills concern, too. An internal study found that early-career programmers using these tools developed a weaker grasp of core concepts over time. The report calls it an “experience gap” that could contribute to a shortage of experienced developers down the line.
What organizations need to do before shipping AI-generated code
The ACM report is clear about what responsible adoption looks like. AI-generated code needs rigorous testing and formal verification before it goes anywhere near production. Outputs should be audited using specialized tools, and human oversight must be built into execution and deployment.
Additionally, teams need to plan for long-term maintainability from day one, ensuring that what gets built can actually be understood and managed by human developers down the line. Vibe coding is powerful, but without these guardrails, the report warns, the failure modes are entirely predictable.