Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda

TL;DR

Vibe coding is an emerging software development paradigm where humans and Generative AI collaborate through natural language dialogue, shifting intent mediation from deterministic instructions to probabilistic inference, with significant implications for democratization and systemic leverage.

Contribution

The paper defines vibe coding as a new paradigm, analyzes its implications, and proposes a comprehensive research agenda for understanding this collaborative AI-human software development process.

Findings

Redistributes epistemic labor between humans and machines

Shifts expertise from technical implementation to orchestration

Identifies opportunities and risks of vibe coding

Abstract

Software development is undergoing a fundamental transformation as vibe coding becomes widespread, with large portions of contemporary codebases now being generated by Artificial Intelligence (AI). The disconnect between rapid adoption and limited conceptual understanding highlights the need for an inquiry into this emerging paradigm. Drawing on an intent perspective and historical analysis, we define vibe coding as a software development paradigm where humans and Generative AI (GenAI) engage in collaborative flow to co-create software artifacts through natural language dialogue, shifting the mediation of developer intent from deterministic instruction to probabilistic inference. By intent mediation, we refer to the fundamental process through which developers translate their conceptual goals into representations that computational systems can execute. Our results show that vibe coding redistributes epistemic labor between humans and machines, shifting expertise from technical implementation toward collaborative orchestration. We identify key opportunities, including democratization, acceleration, and systemic leverage, alongside risks such as black-box codebases, responsibility gaps, and ecosystem bias. We conclude with a research agenda spanning human-, technology-, and organization-centered directions to guide future investigations of this paradigm.