The Demise of Vibe Coding: Why AI-Generated Code Needs Structure

The Rise and Fall of Vibe Coding

Andrej Karpathy’s vibe coding movement has ended due to poor code quality. According to a Georgetown CSET study, 45% of AI-generated code contains security vulnerabilities.

Why This Matters

The technical reality of vibe coding is that it optimizes for speed of creation, not quality of output, resulting in a significant number of security vulnerabilities and bugs. This approach has led to a failure scale of nearly half of all AI-generated code being insecure, which can have severe consequences in production environments. The ideal model of specification-first AI development, on the other hand, prioritizes quality and security, generating complete and tested software from structured plans.

Key Insights

• A Georgetown CSET study found that 45% of AI-generated code contains security vulnerabilities, 2025
• CodeRabbit’s 2025 AI Code Quality Report showed that AI-generated code has 1.7x more major issues than human-written code, highlighting the need for better code review and testing
• MIT Technology Review distinguished generative coding from vibe coding, emphasizing the importance of structured specifications in AI-generated code, 2025

Practical Applications

• Use case: GitHub Copilot for automated code suggestions, but with manual review and testing to ensure security and quality. Pitfall: Relying solely on AI-generated code without review can lead to security vulnerabilities and bugs
• Use case: Specification-first AI development tools like Codavyn’s Star Command for building complete and tested software. Pitfall: Failing to define clear specifications and security requirements can result in insecure or faulty code

References:

• https://dev.to/michelle-jones/vibe-coding-is-dead-heres-what-replaced-it-4472