Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding

TL;DR

This qualitative study investigates vibe coding, a nascent natural language programming paradigm centered on AI-assisted co-creation and developer flow. By analyzing over 190,000 words from Reddit, LinkedIn, and 11 interviews, the authors develop a grounded theory linking conversational AI interaction, co-creation, flow, and trust, and identify a breadth of pain points and emergent best practices. They reveal how trust mediates the degree of delegation to AI, the impact on code review and reliability, and the risks that accompany broader adoption, including software, developer, and societal concerns. The work offers implications for education, tooling, and future research, emphasizing flow-preserving design and the need for empirical validation of practices in real-world settings.

Abstract

Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software development. Centered on iterative co-design with an AI assistant, vibe coding emphasizes flow and experimentation over strict upfront specification. While initial studies have begun to explore this paradigm, most focus on analyzing code artifacts or proposing theories with limited empirical backing. There remains a need for a grounded understanding of vibe coding as it is perceived and experienced by developers. We present the first systematic qualitative investigation of vibe coding perceptions and practice. Drawing on over 190,000 words from semi-structured interviews, Reddit threads, and LinkedIn posts, we characterize what vibe coding is, why and how developers use it, where it breaks down, and which emerging practices aim to support it. We propose a qualitatively grounded theory of vibe coding centered on conversational interaction with AI, co-creation, and developer flow and joy. We find that AI trust regulates movement along a continuum from delegation to co-creation and supports the developer experience by sustaining flow. We surface recurring pain points and risks in areas including specification, reliability, debugging, latency, code review burden, and collaboration. We also present best practices that have been discovered and shared to mitigate these challenges. We conclude with implications for the future of AI dev tools and directions for researchers investigating vibe coding.

Figures (2)

• Figure 1: Our proposed theory of the vibe coding experience. We identify four core components: Conversational Interaction with AI (the paradigm), Co-creation with to AI (the central activity), Flow and Joy (the developer experience), and AI Trust (a key enabling and mediating factor). Developers co-create with AI through natural language conversation to achieve flow. Trust in AI mediates co-creation: increased trust can enable deeper co-creation, thereby enhancing flow. However, increased trust can also lead to increased risks at the software, developer, and societal levels. During both interaction and co-creation, developers experience pain points which can detract from flow. Emerging best practices often ameliorate these pain points, and increase flow.
• Figure 2: Mapping between vibe coding pain points, best practices, and characteristics of flow