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

Sebastian Baltes, Marc Cheong, Christoph Treude

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.

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

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.

Paper Structure

This paper contains 17 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Research Method
  4. Data Collection
  5. Coding Procedure
  6. Final Codebook
  7. Results
  8. Annotation Overview
  9. Review Friction
  10. Quality Degradation
  11. Forces and Consequences
  12. Rhetorical
  13. Discussion
  14. AI Slop as a Tragedy of the Commons
  15. Implications for Practice
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Code relationship network with Louvain community clusters. Edges represent conceptual relationships between codes, capturing causal links, scope distinctions, and thematic overlaps. Node colors indicate cluster membership. An interactive version is available online.
  • Figure 2: Code frequency distribution across 978 coded posts. Bar colors indicate cluster membership. An interactive version with co-occurrence data is available online.