AI Slop and the Software Commons
Sebastian Baltes, Marc Cheong, Christoph Treude
TL;DR
The paper discusses how AI-generated low-quality code, termed 'AI slop,' threatens the sustainability of shared software resources by externalizing review costs and risking a tragedy of the commons.
Contribution
It highlights the problem of AI slop in software development and proposes concrete steps for stakeholders to address this commons dilemma.
Findings
AI slop externalizes review and maintenance costs.
Review layers are thin and under strain.
Addressing AI slop requires institutional solutions grounded in Ostrom's principles.
Abstract
In this article, we argue that AI slop in software is creating a tragedy of the commons. Individual productivity gains from AI-generated content externalize costs onto reviewer capacity, codebase integrity, public knowledge resources, collaborative trust, and the talent pipeline. AI slop is cheap to generate and expensive to review, and the review layer is already thin. Commons problems are not solved by individual restraint. We outline concrete next steps for tool developers, team leads, and educators, grounded in Ostrom's design principles for enduring commons institutions.
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