Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook – a Grey Literature Review

Vibe coding is the practice where coders rely on AI code generation tools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, there has been no systematic investigation into why vibe coders engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of AI-generated code. To this end, we conduct a systematic grey literature review of 101 practitioner sources, extracting 518 firsthand behavioral accounts about vibe coding practices, challenges, and limitations. Our analysis reveals a speed–quality trade-off paradox, where vibe coders report being motivated by speed and accessibility, often experiencing rapid “instant success and flow”, yet most perceive the resulting code as fast but flawed. Our analysis suggests that QA practices are frequently overlooked, with many reporting skipping testing, relying on the models’ or tools’ outputs without modification, or delegating checks back to the AI code generation tools. While vibe coding lowers barriers and accelerates prototyping, it can introduce reliability and maintainability concerns. These insights carry implications for tool designers and software development teams. Understanding how vibe coding is practiced today is crucial for guiding its responsible use and preventing a broader QA crisis in AI-assisted development.