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Musicians Union AFM Sues Major Record Labels Over Secret AI Licensing

The AFM union is taking Universal, Warner, and Atlantic to court, alleging historical recordings were licensed for AI training without compensating session musicians.

This article was AI-generated and published automatically. Context, labelling and all sources at the end of the article.

(KI-generiertes Symbolbild: Gemini / AI Connect)

The legal confrontation over training generative music systems has entered a decisive new phase. In the US District Court for the Southern District of New York, the American Federation of Musicians (AFM) has detailed its lawsuit against major music corporations Warner Records, Universal Music Group, and Atlantic Recording. The union accuses the industry giants of systematically breaching collective bargaining agreements by monetizing historical music catalogues for generative AI platforms.

According to the legal filing, record labels licensed extensive audio recordings to third-party generative AI developers without involving or compensating the session and studio musicians who performed on those tracks. The lawsuit is grounded in the Sound Recording Labor Agreement (SRLA). This framework includes a longstanding New Use clause, which mandates that performers must receive additional compensation whenever their original recordings are repurposed in a new medium or commercial application.

Beyond withholding royalties, the AFM sharply criticizes the labels for a complete lack of transparency. The union was neither informed nor consulted prior to the execution of these lucrative AI deals. Legal representatives for the musicians argue that the labels are attempting to retain all financial windfalls from AI licensing, leaving the performing artists empty handed while synthetic systems trained on their work threaten their livelihoods.

This legal dispute coincides with rapid shifts across music copyright and licensing practices. In a related development, European music platform Jamendo, owned by Winamp, voluntarily dismissed its copyright infringement lawsuit against AI music generator Suno without prejudice on August 13, 2026. Jamendo had originally accused Suno of misusing a research dataset containing over 55,000 tracks for commercial model training, and industry observers view the withdrawal as a sign of an out-of-court commercial data licensing settlement.

The AFM lawsuit could establish a vital legal precedent for the wider creative industry. If the court upholds the union's reading of the New Use provisions, record companies will be forced to distribute a portion of their AI licensing revenues to hundreds of thousands of instrumentalists and studio performers. This significantly raises the regulatory and operational pressure on tech firms and catalogue holders to build sustainable compensation pipelines for human creators.

What this means for you

For artists and media professionals, this lawsuit represents a crucial test of how secondary revenues from generative AI must be shared with performers. Tech companies licensing training data will need far stricter due diligence to ensure all underlying rights are cleared.

Evidence

Solidly sourced
54/100
  • The American Federation of Musicians (AFM) clarified its lawsuit against Warner Records, Universal Music Group, and Atlantic Recording in the US District Court for the Southern District of New York.

    single source
  • The AFM alleges the labels breached the Sound Recording Labor Agreement (SRLA) by licensing music catalogues to generative AI platforms without honoring the New Use compensation rule.

    single source
  • Music platform Jamendo voluntarily dismissed its copyright lawsuit against Suno without prejudice on August 13, 2026.

    single source

The evidence score is computed, not hand-set: from confidence, the number of sources and the share of verified statements.

Source & transparency

As of: August 26, 2026

AI-generatedAI-generated: produced automatically from vetted sources with technical quality checks (source, quote and figure verification); no human sign-off of each item before publication

Sources
2
Verified statements
0 / 3
Evidence score
54Solidly sourced

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