How can society determine who truly owns music created with artificial intelligence? This question has become increasingly relevant as AI music generators grow more sophisticated, challenging traditional notions of authorship and copyright protection. The legal landscape remains clear on one vital point: AI systems cannot be copyright holders, as U.S. Copyright Office regulations and court decisions consistently require human authorship for copyright eligibility.
When music is generated solely by AI without substantial human creative input, it generally falls into the public domain, free for anyone to use. This aligns with copyright frameworks worldwide, including the UK’s Copyright, Designs and Patents Act 1988 and the Berne Convention, which fundamentally link originality to human creative expression rather than machine output. The legal consensus treats AI as a tool, not an author, placing the emphasis on who wields that tool and how. Since the 1909 Copyright Act, human authorship requirement has been explicitly integrated into U.S. copyright law through three essential criteria: fixation, originality, and human creation.
In response to this evolving terrain, major performing rights organizations like ASCAP, BMI, and SOCAN have adopted positions supporting human creators who use AI in their compositional process. These organizations maintain that humans who make meaningful creative decisions when using AI tools can retain ownership rights to the resulting works, reflecting the established legal principle that copyright protection stems from human creativity. Proper rights management ensures composers can monetize their AI-assisted works through these collecting societies worldwide. A March 2025 U.S. Court of Appeals ruling further solidified this stance by denying copyright protection for works created entirely by AI without human contribution.
The Recording Academy has similarly drawn this distinction, excluding fully AI-generated songs from Grammy consideration while accepting entries where human creative control is evident. Musicians can leverage these AI tools while exploring sync deals for additional income streams by licensing their human-created or human-modified AI works for visual media applications.
Meanwhile, litigation has emerged around AI training practices, with recording companies and publishers challenging tech companies that use copyrighted music without permission to train their models. These cases highlight the complex distinction between potential infringement in the AI training process versus infringement in the generated output.
States like California and Texas have enacted AI governance laws, though extensive federal legislation addressing AI music ownership remains elusive.
As the technology advances, industry stakeholders are exploring collective licensing frameworks to guarantee human creators receive fair compensation when their works interact with AI systems, preserving the fundamental principle that meaningful human creative contribution remains the cornerstone of music ownership rights.
