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

arXiv: https://arxiv.org/abs/2510.00328 · HTML: https://mlsanity.com/paper/2510.00328/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2025.

## Built on by (most cited first, 30 of 30)

- [Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks](https://mlsanity.com/paper/2512.03262/) (2025)
- [Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025](https://mlsanity.com/paper/2512.14012/) (2025)
- [When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents](https://mlsanity.com/paper/2606.20023/) (2026)
- [WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis](https://mlsanity.com/paper/2606.01869/) (2026)
- [Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity](https://mlsanity.com/paper/2601.18345/) (2026)
- [Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding](https://mlsanity.com/paper/2602.00854/) (2026)
- [Why Human Guidance Matters in Collaborative Vibe Coding](https://mlsanity.com/paper/2602.10473/) (2026)
- [RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations](https://mlsanity.com/paper/2605.26177/) (2026)
- [Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective](https://mlsanity.com/paper/2511.06428/) (2025)
- [Architecture Without Architects: How AI Coding Agents Shape Software Architecture](https://mlsanity.com/paper/2604.04990/) (2026)
- [User Misconceptions of LLM-Based Conversational Programming Assistants](https://mlsanity.com/paper/2510.25662/) (2025)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase](https://mlsanity.com/paper/2603.25697/) (2026)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories](https://mlsanity.com/paper/2606.13298/) (2026)
- [Stand-Alone Complex or Vibercrime? Exploring the adoption and innovation of GenAI tools, coding assistants, and agents within cybercrime ecosystems](https://mlsanity.com/paper/2603.29545/) (2026)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)
- [Beyond the Org Chart: AI and the Transformation of Invisible Work](https://mlsanity.com/paper/2605.22707/) (2026)
- [Inferring Code Correctness from Specification](https://mlsanity.com/paper/2605.29822/) (2026)
- [Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming](https://mlsanity.com/paper/2606.18293/) (2026)
- [Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration](https://mlsanity.com/paper/2606.19042/) (2026)
- [Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy](https://mlsanity.com/paper/2606.27936/) (2026)
- [Prompt Coverage Adequacy](https://mlsanity.com/paper/2607.02057/) (2026)
- [Plainbook: Data Science, in Plain Language](https://mlsanity.com/paper/2607.05717/) (2026)
- [Case study: solving P-99 with LPTP and an LLM](https://mlsanity.com/paper/2607.21196/) (2026)
- [Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?](https://mlsanity.com/paper/2607.21760/) (2026)
- [Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools](https://mlsanity.com/paper/2607.22928/) (2026)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)
- [AuraForge: Scaling Security Supervision for Training Coding Agents](https://mlsanity.com/paper/2610.00850/) (2026)
- [Last Secure Code Benchmark: Is the Code You Vibe Secure?](https://mlsanity.com/paper/2610.14766/) (2026)

## Abstract (arXiv)

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.