
Photo Credit: Brett Jordan
This telling stat emerged during a new Hollywood Reporter sit down with Apple VP of music, video, sports, and international Oliver Schusser. By the outlet’s description – meaning its summary of Schusser’s remarks – Apple Music last year “identified and demonetized as many as 2 billion fraudulent streams.”
And while those illicit plays might not have exclusively reached AI uploads, logic and plenty of evidence suggest that machine-made slop is comparatively well-suited for gaming the system.
Already ahead of the curve when it comes to spotting AI audio, Deezer recently revealed that it’d flagged and demonetized roughly 85% of the relevant works’ streams due to fraud, for example.
As reiterated by Schusser, Apple Music, besides demonetizing fake streams, has since 2022 been slapping “fraudsters” with a sliding fine of between 5% and 25% of would-be royalties.
Now, as of yesterday, these percentages have doubled to 10% and 50%, per Schusser, who attributed the move’s timing in part to the ongoing AI audio avalanche. In general, tackling fraud is definitely a positive – though the retooled penalty policy raises questions about the underlying approach.
As tracked by DMN Pro, on Spotify and rival DSPs, AI slop peddlers have weaved elaborate featured-artist and playlist webs to drive streams. If properly handled, one instance of verified fraud could probably enable a number of justified takedowns.
An adjacent consideration: Also as covered by DMN Pro, judging by their upload volume and per-work streams, crafty AI slop specialists are avoiding directing thousands upon thousands of plays to individual tracks.
DMN contacted Apple Music for additional enforcement details – including about when exactly it’s time to upgrade a fine to a straight ban – but didn’t immediately receive a response.
Elsewhere in the interview, Schusser took a shot at ad-supported listening, called out competitors that are “really struggling” with fraud, and emphasized the perceived “lot of work” needed in the industry to arrive at a universally accepted definition of AI music.