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arxiv: 2606.18052 · v1 · pith:3D37U7YMnew · submitted 2026-06-16 · 💻 cs.CR

An Empirical Analysis of AI Slop in Music Streaming

Stanley Wu , Josephine Passananti , Viresh Mittal , Wenxin Ding , Haitao Zheng , Ben Y. Zhao This is my paper

Pith reviewed 2026-06-27 00:06 UTC · model grok-4.3

classification 💻 cs.CR
keywords AI musicmusic slopSpotifystreaming platformsgenerative AIcontent distributionengagement metricsAI detection
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The pith

Over 93 percent of AI music on Spotify gets few plays and is rarely recommended.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper investigates whether mass-produced AI music can develop into a self-sustaining slop industry on streaming platforms. It measures user engagement on Spotify and finds that nearly all such tracks attract almost no listens or algorithmic promotion. The authors also publish their own AI tracks through multiple distributors to map the pipeline from creation to availability. They conclude that current low engagement keeps slop from scaling, but falling generation costs could change that unless platforms and distributors act.

Core claim

The paper shows that the overwhelming majority of AI music on Spotify receives few or no listener plays and is rarely recommended by the platform. Creators follow a spray-and-pray strategy by releasing high volumes across many genres. Publishing AI tracks through indie distributors proves straightforward because policies are inconsistent and weakly enforced. Existing detection tools for AI music lack both accuracy and robustness against new generators.

What carries the argument

Empirical measurement of plays, recommendations, and upload success for AI-identified tracks on Spotify combined with controlled publishing experiments through eleven distributors.

If this is right

  • AI music currently shows little evidence of self-sustaining consumption on major platforms.
  • Inconsistent distributor rules make it simple to upload large numbers of AI tracks.
  • Detection tools cannot yet reliably separate AI music from human music at scale.
  • Lower generation costs could tip the balance toward viable slop unless policies change.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platforms may eventually need origin verification during upload to limit low-effort content.
  • The same low-engagement pattern could appear in other AI-generated media if recommendation systems stay unchanged.
  • Clear labeling requirements for AI content could give listeners a direct way to filter tracks.

Load-bearing premise

The method used to label tracks as AI-generated in the Spotify data is reliable and the low-engagement pattern holds for AI music beyond the sampled set.

What would settle it

A large collection of confirmed AI tracks that achieve high play counts and frequent recommendations, or a detection method that reveals many high-engagement tracks were misclassified as non-AI.

Figures

Figures reproduced from arXiv: 2606.18052 by Ben Y. Zhao, Haitao Zheng, Josephine Passananti, Stanley Wu, Viresh Mittal, Wenxin Ding.

Figure 1
Figure 1. Figure 1: Full pipeline of how music (AI and human [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Stacked volumes of weekly releases of AI-generated and human-made tracks for (a) SMMA (overall music catalog) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Only 7.15% of the most popular AI tracks in our dataset generated enough play counts between Jan. and May 2026 to be monetized. We plot the CDF of estimated royalties among this small group of AI tracks. Even here, the majority of AI tracks earned less than $10. Such a stark contrast between AI-generated and human￾made music is not entirely surprising, given that AI tracks often share similar characteristi… view at source ↗
Figure 10
Figure 10. Figure 10: 5.4. Streaming Royalty and Profitability Next, we study the economic outcomes of AI music (and slop) on streaming platforms, by analyzing streaming royalties earned by AI tracks and whether (and how) their royalty patterns could sustain a shadow industry similar to the spam and fake pharma industries. Today, it is extremely easy (and low cost) to generate and publish large volumes of AI-generated tracks. … view at source ↗
Figure 4
Figure 4. Figure 4: We plot the average number of AI tracks released [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Growth of “hits” produced by AI-generated music, [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of revenue for slop artists, sorted by [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Results of our end-to-end AI music uploading. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performance of AI music detectors shown as ROC [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: In Spotify’s strongly connected recommendation [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims to characterize AI-generated 'slop' music on streaming platforms. It reports that 93% of AI music on Spotify receives few or no plays and is rarely recommended, that creators use a 'spray and pray' strategy of high-volume multi-genre releases, that experiments publishing AI tracks through 11 indie distributors reveal inconsistent and unenforced AI policies, and that existing detection methods lack accuracy or robustness. It concludes that decreasing generation costs risk making AI slop self-sustaining unless platforms and the industry take mitigation steps.

Significance. If the engagement statistics and distributor accessibility findings hold after proper validation, the work supplies useful observational and experimental data on the current scale and pipeline of AI content in music streaming. The direct experiment of generating and distributing original AI tracks is a concrete strength that grounds the claims about distribution ease. Such evidence could usefully inform platform design and policy discussions around generative content.

major comments (2)
  1. [Spotify analysis / results reporting the 93% figure] The central 93% low-engagement claim for AI music (stated in the abstract and used to support the self-sustainability conclusion) rests on an unidentified procedure for labeling tracks as AI-generated within the Spotify dataset. No sample sizes, labeling criteria, validation set, false-positive rate, or controls for confounders such as genre or release date are supplied, despite the paper's own later statement that current detection methods lack accuracy or robustness. This labeling step is load-bearing; without it the observed pattern cannot be attributed specifically to AI slop.
  2. [Distributor experiment] The experimental section on publishing through 11 distributors reports inconsistent policies but supplies no quantitative details on the number of tracks submitted, acceptance rates, or any post-publication engagement metrics that would allow readers to assess whether the published tracks exhibited the same low-engagement pattern claimed for the Spotify cohort.
minor comments (1)
  1. [Abstract] The abstract uses 'we confirm' for the 93% statistic; the manuscript should instead state that the figure is observed under the labeling method described in the methods section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments identify important gaps in methodological transparency and experimental reporting. We will revise the manuscript to address both points directly by adding the requested details and clarifications. No standing objections remain after these revisions.

read point-by-point responses

  1. Referee: The central 93% low-engagement claim for AI music (stated in the abstract and used to support the self-sustainability conclusion) rests on an unidentified procedure for labeling tracks as AI-generated within the Spotify dataset. No sample sizes, labeling criteria, validation set, false-positive rate, or controls for confounders such as genre or release date are supplied, despite the paper's own later statement that current detection methods lack accuracy or robustness. This labeling step is load-bearing; without it the observed pattern cannot be attributed specifically to AI slop.

    Authors: We agree the labeling procedure must be described in full. The current manuscript text does not supply sample sizes, explicit criteria, validation metrics, or confounder controls for the Spotify cohort. In the revision we will insert a new subsection (likely in Section 3 or 4) that details the data source, the exact heuristics or external tools used to label tracks as AI-generated, the total number of tracks examined, any manual validation performed, and an explicit discussion of limitations including false-positive risk and lack of genre/release-date controls. We will also cross-reference this with the later detection-methods section to highlight the inherent uncertainty. revision: yes

  2. Referee: The experimental section on publishing through 11 distributors reports inconsistent policies but supplies no quantitative details on the number of tracks submitted, acceptance rates, or any post-publication engagement metrics that would allow readers to assess whether the published tracks exhibited the same low-engagement pattern claimed for the Spotify cohort.

    Authors: We accept that the distributor experiment currently lacks the quantitative detail needed for reproducibility and comparison. The manuscript reports only qualitative outcomes. In the revised version we will add a table or subsection listing, for each of the 11 distributors: number of tracks submitted, acceptance/rejection outcomes, any stated policy language encountered, and available post-publication metrics (plays, saves, or algorithmic placement) for the accepted tracks. Where engagement data are unavailable we will state this explicitly. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational empirical study

full rationale

The paper performs data collection on Spotify, publishes its own AI tracks via distributors, and reports engagement statistics. No equations, derivations, fitted parameters, or predictions appear in the provided text. The 93% figure is a direct count from labeled data, not a reduction of any model output to its own inputs. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The labeling accuracy concern raised by the skeptic is a methodological limitation, not a circularity pattern under the enumerated definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the accuracy of Spotify-derived AI music labels and the assumption that observed patterns reflect sustainable industry dynamics; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Spotify listener plays and recommendation data accurately capture engagement with AI-generated music
    Underpins the 93% slop characterization and the conclusion about self-sustainability.

pith-pipeline@v0.9.1-grok · 5869 in / 1214 out tokens · 46336 ms · 2026-06-27T00:06:21.118059+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

75 extracted references · 5 canonical work pages · 2 internal anchors

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