Recently, I came across news that Beatport, a music streaming and download platform, has banned the sale of AI-generated music. Under their operational policy, fully AI-generated tracks are rejected at the submission stage, while human-led tracks that utilize AI tools in a supplementary manner are permitted but tagged accordingly.
The Boundaries of Policies
Similar moves have spread across multiple platforms this year.
In January, Bandcamp took the step of completely banning AI-generated music. In June, Traxsource began labeling tracks that use AI in a supporting role as “AI-Assisted,” while excluding them from chart eligibility. Tidal ceased royalty payments for fully AI-generated music, and Deezer took measures to exclude such content from its recommendation engine. In August, Spotify also announced a system to identify AI-created music and virtual artists.
Behind these movements seems to be a two-tiered classification introduced by an industry association in July 2026: “AI-Generated” (where AI generates the majority of the creative elements) and “AI-Assisted” (where a human substantially creates the work, with AI used for specific elements).
Upon first reading, this strikes me as a reasonable distinction. How AI is used as a practical tool within the production process is an entirely different matter from publishing a piece under one’s own name when its creation was almost entirely surrendered to AI. If only the latter is deemed problematic by platforms, it seems a regulation with little practical harm to most composers.
The True Nature of Ambiguity
However, there remains an aspect of this distinction that no one has clearly explained. Nowhere in the terms of service is it defined what level of involvement satisfies terms like “majority” or “substantially.”
For instance, if a human decides the melody, harmony, and structure, while using AI to generate options for timbres or accompaniments, can that be classified as “AI-Assisted”? Or what if a human provides broad directives, but AI generates almost all of the sonic material?
The issue is that a boundary cannot be drawn cleanly by merely comparing the volume of work done by human and AI. Beyond just what and how much a human performed, the overall perspective changes entirely depending on where human judgment was exercised in the process.
Detection mechanisms also vary wildly from platform to platform. Bandcamp reportedly possesses no automated detection system at all, relying solely on user reports to decide on takedowns. As a result, there have been reported instances where human-made works were suspected and removed simply because of cover art that resembled AI generation or an unusually high release frequency.
Even if the language of these boundaries appears crisp, their enforcement seems to rest on a surprisingly shaky foundation.
Behind the Numbers
With this ambiguity in mind, another detail that caught my attention was the statistical data published by Deezer. According to the company, while more than 40% of the tracks uploaded daily are entirely AI-generated, they account for a mere 1 to 3% of actual streams.
Looking strictly at the numbers, one might read this as a reflection of listener preference—that people, after all, choose music crafted by human hands.
However, examining these figures more closely reveals a situation that cannot be so simply summarized. First of all, tracks detected as AI-generated are automatically excluded from Deezer’s recommendation features and editorial playlists in the first place.
This means the 1 to 3% figure is not the result of listeners choosing under completely open conditions. At the very least, one cannot conclude from this metric alone that “people do not choose AI-generated music.”
Furthermore, it was revealed that the vast majority—over 80%—of those 1 to 3% streams represent non-human, artificial stream farming rather than genuine listening. Considering that the proportion of artificial streams across their standard catalog is less than 10%, the contrast for AI-generated tracks exceeding 80% is striking.
Deezer’s executive team has also cited fraudulent streaming royalty extraction, rather than sharing the music itself, as the primary motive behind mass uploads of AI tracks. Here, I believe, lies a distinction that must not be overlooked when reflecting on the broader discussion surrounding AI music.
Conflating Two Distinct Ethics
When synthesized up to this point, a perspective emerges that the regulations and statistics in question are actually cramming two fundamentally different issues under a single term.
One is the question of how to clamp down on fraudulent activity within the music industry. The act of mass-uploading auto-generated tracks to illegitimately siphon streaming royalties via bot playback is something that unequivocally warrants enforcement. This carries ethical dimensions that extend beyond mere institutional governance, such as preventing unfair harm to others and avoiding the distortion of revenue distribution in the music ecosystem.
The other is the question of who can be said to have “created” music produced by generative AI, and in what sense. This is not a matter of fraud; it is a quieter, far more elusive aesthetic and ethical inquiry concerning the intrinsic nature of the creative act itself.
These two discussions ought to exist on entirely separate axes. Yet, within platform terms and media reporting, both are often lumped together under the single label of “AI-Generated.” That a piece of music was generated by AI is not identical to using it to fraudulently extract revenue.
Nor is the absence of fraud equivalent to the presence of sufficient creativity or artistic value. Two inquiries that demand separate consideration appear to have been tied together by the single word “AI.”
Perhaps institutional incentives play a role here as well. The narrative of “human creativity versus AI” captures societal interest far more easily than the dry, economic reality of combating stream farming, thereby garnering broader support for regulation.
It would be no surprise if a dynamic were at work where the practical necessity of anti-fraud measures slips, almost imperceptibly, into a broader narrative of caution against AI-assisted creation as a whole.
Final Thoughts
What concerns me is the side effect of this narrative shift. Even if fraudulent practices make up a significant portion of AI uploads, it would be unfortunate if composers who attempt to treat AI as a creative tool—an extended instrument, as it were, through which to reflect their own musical discipline and craft—were swept up in the same net and shut out.
While fraud must be policed, it seems undesirable if, as a byproduct of that enforcement, newly emerging forms of creation that have yet to find their language are discarded under coarse criteria.
Personally, I have always believed that responsibility for music is assumed precisely through physical engagement with materials. That conviction remains unchanged for now. However, I must admit that I cannot yet fully foresee how the handling of AI as a tool will evolve, nor how the very contours of the act of composition will be reshaped by it.
While platform policies hastily attempt to draw boundaries, I suspect that the question of what to create and what to take responsibility for within those lines is something that can only be tested over time, through one’s own hands and ears.
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