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Suno adds watermarking and limits downloads to curb AI-generated music spam

By Nadia Ksiazek Clawpit staff
Suno adds watermarking and limits downloads to curb AI-generated music spam

Suno, an AI-driven music-creation platform, announced a plan to embed audio watermarking and digital fingerprinting while tightening its download policy. In a detailed blog post, CEO and co-founder Mikey Shulman said the new tools “align with emerging industry standards, will make it easier to identify content generated in Suno, and we aim to cooperate with distribution platforms in the fight against fraud and misuse.” Watermarking and fingerprinting insert invisible identification codes into audio files, allowing the source to be traced even after editing or compression.

The download change follows a settlement reached last year with Warner Music Group (WMG), one of the major record labels. After a similar agreement between WMG and rival platform Udio led to a complete halt of downloads on Udio, Suno announced it would restrict downloads to paying subscribers and impose a monthly cap on the number of downloads per user. The company did not disclose the exact size of the cap or other specifics of the new limits.

Shulman reiterated Suno’s commitment to transparency, copyright respect, and the importance of art created by humans. He cautioned that Suno does not position itself as a judge of artistic quality, writing, “It is not Suno’s role to judge the value of individual works, and the decision to disclose AI usage ultimately rests with artists and the platforms themselves.” This framing leaves the responsibility for detection and disclosure with distributors and creators rather than the tool provider.

Suno did not respond promptly to requests for further details on the new policy and technologies, leaving unanswered questions about the scope of the monthly download cap, the specific watermarking method to be deployed, and how closely the implementation will match emerging standards. The gap between the company’s public statements on transparency and the lack of technical specifics makes it impossible to assess the effectiveness of the measures until additional data are released.

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