"An Endless Stream of AI Slop": The Growing Burden of AI-Assisted Software Development

TL;DR

This paper investigates the increasing problem of low-quality AI-generated content in software development, analyzing developer perceptions and responses through qualitative analysis of online discussions.

Contribution

It provides the first empirical study on developer perceptions of AI slop, identifying key themes and proposing mitigation strategies.

Findings

AI slop burdens reviewers and erodes trust

Damage to codebases and developer knowledge resources

Developers propose mitigation strategies and highlight systemic incentives

Abstract

"AI slop", that is, low-quality AI-generated content, is increasingly affecting software development, from generated code and pull requests to documentation and bug reports. However, there is limited empirical research on how developers perceive and respond to this phenomenon. We conducted a qualitative analysis of 1,154 posts across 15 discussion threads from Reddit and Hacker News, developing a codebook of 15 codes organized into three thematic clusters: Review Friction (how AI slop burdens reviewers, erodes trust, and prompts countermeasures), Quality Degradation (damage to codebases, knowledge resources, and developer competence), and Forces and Consequences (systemic incentives, mandated adoption, craft erosion, and workforce disruption). Our findings frame AI slop as a tragedy of the commons, where individual productivity gains externalize costs onto reviewers, maintainers, and the broader community. We report the concerns developers raise and the mitigation strategies they propose, offering actionable insights for tool developers, team leads, and educators.