The Millions of Songs Mashed Into AI-Generated Music

Millions of Copyrighted Songs Were Fed to AI Music Generators – Now There's Proof

Millions of copyrighted songs trained AI music generators, and new searchable databases from The Atlantic now confirm which tracks were used.

AI music is flooding streaming platforms. But listeners like it less and less

Music fans are becoming increasingly uncomfortable with AI songs, according to a recent study.

AI music tools challenge Bay Area musicians

AI music tools challenge Bay Area musicians: As affordable AI platforms spread, artists debate creativity, copyright and survival.

HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark

As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms. These include vocal synthesis, arrangement, and professional mastering. However, current detection research remains largely confined to a binary `AI-or-human' paradigm. It fails to reflect the realities of contemporary music production workflows. In real-world production, AI tools are increasingly used to refine or master human-produced tracks, and human engineers likewise post-process AI-generated material to ensure professional quality. Moreover, users often employ adversarial tactics to bypass AI detectors, such as applying human mastering to AI-generated tracks. This creates a grey area that a simple binary classification fails to capture. In this paper, we define and investigate ``AI Music Tracking'': the challenge of identifying specific AI integration across the multifaceted spectrum of music production. To this end, we introduce HAIM, a dataset with diverse labels for stages of music production. It is designed to isolate stages of AI intervention, including hybrid production and agent-level tracking. Our evaluation of state-of-the-art detectors reveals systemic flaws. By releasing HAIM, we propose a new benchmark that shifts the field beyond binary classification toward a granular, structured evaluation of AI music.

Deezer: AI-generated tracks now represent 44% of all new uploaded music - Deezer Newsroom

As the only streaming platform tagging AI-generated music, Deezer now reveals that nearly 75,000 AI-tracks are uploaded every day

Who owns an AI generated song? What we can learn from the phonograph and the evolution of copyright laws

History suggests copyright adapts to technology. AI may be no exception.

Why We’re Taking a Human‑First Stand on AI‑Generated Music — Qobuz Community

Artificial intelligence is transforming the music industry at an unprecedented pace. Faced with the explosion of AI-generated content and the legitimate questions it raises, Qobuz has chosen transparency. This charter establishes our framework: how we use AI, where we draw our lines, and what commi

Inside the Don’t Ask, Don’t Tell Era of AI in Music

From juicing demos to cloning vocals, AI music tools are creeping into the workflows of top producers, songwriters and artists. It’s mostly happening behind closed doors — for now

SlopTracker — Exposing AI-Generated Artists on Spotify

AI-generated artists are flooding Spotify, racking up millions of streams, and siphoning revenue from real musicians. See the receipts.

The grueling fight over who profits from AI music

AI can generate songs in seconds. But behind every AI track is a complicated question: Who should get paid? And, how? The fights have started.

Soulless: List of AI Artists Hiding on Spotify

Soulless: AI music is stealing from real artists. AI music analyzer (open source).

Soulless: List of AI Artists Hiding on Spotify

Soulless: AI music is stealing from real artists. AI music analyzer (open source).

The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization

This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account of why this matters. We audit two commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal). For each system and genre, we generate 100 tracks and compare them against human corpora of equal size, using 72 music information retrieval (MIR) features and multiple diagnostics of dispersion, redundancy, and separability. We define homogenization as reduced acoustic variation in standard computational audio features including rhythm and timing, timbre/spectral shape, and dynamics, both within genres and across genre boundaries. We also generate tracks using only a genre name as the prompt, with no additional instructions, to reveal each system's default musical tendencies. The results show two structurally distinct homogenizing tendencies. Lyria reduces within-genre acoustic diversity, while Suno collapses the acoustic distinctions between genres without compressing within-genre spread. Neither system follows user prompts faithfully, indicating that the observed patterns reflect learned priors rather than prompt constraints. The two systems do not converge on a common acoustic profile and are more acoustically distant from each other than two random human subsamples would typically be. Nevertheless, a standard classifier distinguishes AI from human tracks near-perfectly on MIR features alone. We argue that these patterns matter not as an aesthetic curiosity but as a justice-relevant condition, shaping which musical styles become legible, valued, and economically rewarded as generated outputs increasingly circulate at scale.

Deezer Launches Free AI Music Detector for Playlists

43% of people joining Deezer from other streaming platforms already have AI music in their playlists

[Interview] Snow J of Cheerful Music dives into the cross cultural intersection of AI and music – EARMILK

In conversation with Earmilk, Snow. J explores how AI has restructured the foundation of music culture, striking a balance between AI and human-made music as well as Cheerful Music's distinct approach to engaging AI technology in music making and marketing.