An Empirical Analysis of AI Slop in Music Streaming

2026-06-16

AI Summary

This study addresses the proliferation of AI-generated music—often termed “AI slop”—which risks evolving into a self-sustaining gray market that undermines the integrity of the music ecosystem. For the first time, it systematically maps the end-to-end AI music pipeline, spanning generation, distribution, and consumption. Drawing on streaming platform data analysis, a controlled release experiment conducted with 11 independent distributors, and an evaluation of mainstream AI music detection tools, the research uncovers critical vulnerabilities: while 93% of AI-generated tracks receive negligible plays and minimal algorithmic promotion, lax platform moderation enables their mass upload. Moreover, current detection methods exhibit insufficient accuracy and robustness. These findings provide an empirical foundation and policy-relevant insights for understanding and mitigating the harms posed by low-quality, high-volume AI-generated content.

Abstract

Generative AI models lower the bar for content creation, making it easy for any user to create professional-looking images, text and music with minimal effort. This has enabled a new cottage industry around creation of "AI slop" mass quantities of mediocre content produced to generate revenue, often through misrepresentation as human-authored content, or scams involving automated scripts and fake consumption. While there are obvious parallels between the AI-slop industry and "traditional" email spam networks, it might be too early to determine if AI slop generation can grow into a similar self-sustaining industry. In this paper, we look specifically at the music industry, and explore the question: Can we prevent AI music slop from growing into a self-sustaining shadow industry? To answer this question, we characterize the current state of AI slop in music, and its pipeline from generation, distribution, and consumption by users on streaming platforms. By examining growth and engagement on Spotify, we confirm that AI music exhibits AI slop characteristics: the overwhelming majority (93%) of AI music receive few, if any listener plays, and are rarely recommended. AI musicians "spray and pray," releasing large volumes of music across multiple genres in hopes of generating a hit. We also explore the AI slop pipeline by generating and publishing our own AI tracks onto streaming through 11 indie music distributors. We find distributors have inconsistent and largely unenforced policies on AI music, making it surprisingly easy to publish mass produced AI songs. Finally, we consider AI music detection, and find that current methods lack accuracy or robustness. As generation costs decrease, we believe slop generation in music will become self-sustainable, unless concrete steps are taken by the music industry. We consider and discuss potential mitigation methods based on our findings.

Problem

• AI slop
• music streaming
• generative AI
• content moderation
• shadow industry

Innovation

• AI slop
• generative AI
• music streaming
• content moderation
• AI detection