Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2025.
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Citing papers per year: 2025: 2; 2026: 8.
10 papers.
- 2025Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025cited by 17
- 2026LLMs Corrupt Your Documents When You Delegatecited by 9
- 2025User Misconceptions of LLM-Based Conversational Programming Assistantscited by 1
- 2026Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflowscited by 0
- 2026Where Did the Variability Go? From Vibe Coding to Product Lines by Regenerationcited by 0
- 2026Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agentscited by 0
- 2026SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvementscited by 0
- 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agentscited by 0
- 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflowscited by 0
- 2026Vibe Analysis: Exploring LLM Adoption by Data Visualization Practitionerscited by 0
- 2026Vibe Analysis: Exploring LLM Adoption by Data Visualization Practitioners25 Sep 2026
- 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows22 Sep 2026
- 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agents21 Sep 2026
- 2026SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements29 Jul 2026
- 2026Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents14 Jul 2026
- 2026Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration17 Jun 2026
- 2026LLMs Corrupt Your Documents When You Delegate17 Apr 2026
- 2026Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows18 Mar 2026
- 2025Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 202516 Dec 2025
- 2025User Misconceptions of LLM-Based Conversational Programming Assistants29 Oct 2025