Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review
Ahmed Fawzy, Amjed Tahir, Kelly Blincoe
TL;DR
This Grey Literature Review investigates vibe coding—the intuition-driven use of AI code-generation tools with minimal code review—across 101 practitioner sources and 518 behavioral units. By applying a rigorous, multi-stage search and Braun & Clarke-style thematic analysis, the study uncovers a pronounced speed–quality trade-off: rapid, accessible prototyping often comes at the cost of reliability and maintainability, with widespread skipped QA and uncritical trust. The findings highlight a new class of vulnerable developers and stress the need for tool-level guardrails, explicit QA indicators, and guided workflows to prevent QA erosion in AI-assisted development. These insights inform tool design and organizational practices, suggesting structured review processes and educational interventions to balance innovation with software quality.
Abstract
AI code generation tools are transforming software development, especially for novice and non-software developers, by enabling them to write code and build applications faster and with little to no human intervention. Vibe coding is the practice where users rely on AI code generation tools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, no research has systematically investigated why users engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of the 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 are motivated by speed and accessibility, often experiencing rapid ``instant success and flow'', yet most perceive the resulting code as fast but flawed. QA practices are frequently overlooked, with many skipping testing, relying on the models' or tools' outputs without modification, or delegating checks back to the AI code generation tools. This creates a new class of vulnerable software developers, particularly those who build a product but are unable to debug it when issues arise. We argue that vibe coding lowers barriers and accelerates prototyping, but at the cost of reliability and maintainability. 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.