RIAA CEO Mitch Glazier: Using Market Substitution to Prove AI Music Training Is Not Fair Use
In an interview on The Verge Decoder podcast, RIAA CEO Mitch Glazier outlined the legal framework for the Recording Industry Association of America's lawsuit against AI music company Suno. The litigation aims to establish that training AI models on music does not constitute fair use, with the core argument relying on the fourth factor of fair use: market substitution. Glazier argues that AI-generated music competes directly with human-made music on the same platforms for the same subscription fees, thereby harming the potential market for original works. He also described a layered governance strategy: first securing creators' rights through legal precedent, then allowing market forces—such as labeling, fan preference, and platform competition—to drive economic outcomes.
In a November 2026 episode of The Verge Decoder podcast, RIAA CEO Mitch Glazier detailed the legal logic behind the Recording Industry Association of America's lawsuit against AI music generation company Suno. His central statement was: 'We are clearly using this precedent-setting litigation to prove at the legal level that training on music does not constitute fair use.' The mechanism supporting this conclusion is the fourth factor of the fair use doctrine, which courts consider the most critical: the effect on the potential market for the copyrighted work.
Glazier explained this mechanism by stating: 'You are essentially creating music that competes side-by-side with human music on the same platform; users can click on either one and pay the same subscription fee. So you are affecting the economic pool, and you are effectively substituting in the marketplace.' In other words, the substitution argument is not separate from the training issue but serves as the legal tool to prove infringement, because the final output of training (AI-generated music) directly replaces the economic position of the trained works in the market.
This legal strategy is embedded within the broader governance philosophy described by Glazier. He stated: 'My global focus is: have we secured the basic rights of creators? If we have, fans can take over and drive the market.' Legal action is not the end goal but a prerequisite to create the conditions for market mechanisms to function. Once rights are established, subsequent steps such as labeling informing consumer choice, platform competition driving standardization, and fan preferences determining economic outcomes can take effect.
Glazier believes that, unlike the Napster era, positions are converging in the current AI controversy. He said: 'The good news is, unlike Napster, I don’t feel like we are facing it at odds.' He characterized the current struggle as 'keeping art human and preserving the connection between artists and fans,' rather than merely a business model dispute. In his view, fans are the ultimate market force: 'If you know a song is AI, your view of it changes,' and this preference difference will drive economic results.
On the legislative front, Glazier introduced the design concept of the NO FAKES Act. This is a proposed United States federal bill (not yet enacted into law) aimed at establishing a new intellectual property framework for voice and likeness rights. He pointed out the current legal gaps: 'United States federal law does not actually recognize an individual’s right to their own voice or likeness; state-level publicity rights typically apply only to advertising and endorsement scenarios.'
The takedown mechanism designed in the bill draws lessons from the Digital Millennium Copyright Act (DMCA). Glazier described a three-layer balance structure: the notice provider faces thresholds (including penalties for false notices), the recipient has counter-notice rights, and for the middle ground involving constitutional issues such as parody or news, a procedure for a judge to quickly rule on a temporary restraining order was designed. 'Nobody wants platforms to decide constitutional questions,' he said.
However, Glazier also acknowledged the boundaries of this strategy. He mentioned: 'I believe Warner partnered with Suno; they trained models on Warner’s music so they wouldn’t run into these issues.' Note that this statement carries uncertainty ('I believe'). If this licensing model becomes widespread and generated content no longer competes for the same subscription fees on the same platforms as human music, the conditions for the market substitution argument may no longer be met. His response in this scenario would shift to supply-side control: restricting the distribution scope of AI-generated content through licensing agreements to prevent it from diluting the economic share of human music on streaming platforms.
Regarding labeling, Glazier noted that California’s transparency law has required Suno to add machine-readable AI-generated watermarks since August 2026, and competition between Apple and Spotify will drive broader labeling practices. However, he admitted that for DIY creators uploading via distributors like DistroKid, the integrity of the technical labeling chain remains an unsolved problem: 'Your entry point is DistroKid, TuneCore, the distributors. Are they willing to use AI detection technology to determine if this is fully generated?'
Overall, Glazier presented a layered logic: legally, establishing through litigation that training does not constitute fair use (using market substitution as the argumentative mechanism); politically, establishing a new framework for voice and likeness rights through legislation; and commercially, guiding consumer choice through labeling and platform competition. The three layers are mutually conditional—markets cannot function without established rights, and consumers cannot make informed choices without labeling. The actual effectiveness of this approach depends on whether AI companies accept the legal outcomes, whether platforms cooperate with labeling, and whether licensing models alter the premises of the substitution argument.