France’s new AI surveillance on music platforms forces artists to navigate algorithmic filters, fundamentally altering how sound is valued and protected.
As France-backed AI tools began scanning music platforms recently, creators found themselves composing for algorithms as much as audiences, reshaping how sound circulates and survives in the digital age.
The announcement came in late January. Deezer licensed its AI detection technology to SACEM, France’s main rights management organisation, in what both described as a landmark deal. Few users noticed anything different. Songs still played, playlists still refreshed, and personalised recommendations arrived with their usual punctuality.
However, behind the interface, a new and invisible layer of scrutiny had begun operating across the catalogue.
Artificial intelligence systems were silently scanning audio files, identifying copyrighted material, and feeding that information directly into licensing mechanisms.
The aim was clarity, yet the result may be something closer to automated control over the creative process itself.
Copyright Enters the Machine
The detection tools mark a significant milestone in the automation of cultural governance. Their primary purpose is to identify protected works more efficiently and ensure that human artists are paid fairly in an ecosystem increasingly crowded by synthetic content. According to Deezer, the system successfully identified and removed up to 85 per cent of fraudulent AI-generated music streams from its royalty pool last year, flagging over 13.4 million AI tracks.
The platform now receives around 60,000 fully AI-created tracks every day, roughly 39 per cent of total daily uploads, up from 10 per cent in January last year. For regulators and industry executives, the logic is simple: streaming platforms now host millions of tracks, making human review impossibly slow and expensive. Automation promises the scale necessary to police the modern internet.
Yet this transition means that copyright enforcement has shifted from a matter of legal and human interpretation to one of computational pattern matching. In this new regime, sound is translated into data points and mathematical probabilities.
Music becomes readable to machines before it is even heard by people, creating a pre-filtering process that determines the economic viability of a song before it ever reaches a listener’s ear.
Self-Censorship by Design
Once creators realise that algorithms are the primary gatekeepers of their income, their behaviour inevitably begins to change.
We are seeing the rise of “algorithmic anxiety,” where composers and producers begin to anticipate the triggers of automated systems. Producers might avoid certain familiar melodies or samples, even those that fall under fair use, simply because they fear a “false positive” flag that could demonetise their work for weeks whilst a manual appeal is processed.
This self-censorship does not arrive through formal bans or government edicts; it arrives through economic calculation.
Hip-hop producers, who rely on the historical art of sampling, find themselves hesitating to use obscure textures that might be misidentified. Folk musicians worry that traditional melodies, which exist in the public domain, might be claimed by the system as belonging to a major label's catalogue.
The system does not strictly forbid creativity, but it reshapes it into a form that is safer, more predictable, and less likely to cause an administrative error.
Technical Visibility and Cultural Bias
Detection systems rely on vast databases, and these databases are inherently strongest where catalogues are complete, digital, and standardised. Mainstream Western music fits easily into this rigid structure, as it is often recorded in high fidelity with clear metadata.Â
This creates a digital divide where visibility becomes a technical privilege. Songs recorded in minority languages or those using non-Western tonal scales are statistically more likely to be misread or ignored by current detection frameworks. Informal recordings and oral traditions are even harder for software to classify correctly, leading to a situation where these cultural expressions are marginalised by the very tools meant to protect creators.
Deezer’s technology, trained on 94 million songs and supported by two patents filed last year, analyses audio signals to detect distinctive signatures from generators such as Suno or Udio. To exist safely and profitably in the streaming era, music must now be legible to software, potentially flattening the global musical landscape into a more homogeneous sound.
Platforms as Judicial Arbiters
In theory, copyright disputes are legal matters resolved through transparent judicial processes. In practice, they are increasingly settled by automated platform systems that act as judge, jury, and executioner. Once a track is flagged by an algorithm, payments are redirected or restricted instantly. The burden of proof then shifts to the artist, who must navigate opaque appeal processes that rarely involve a human interlocutor.
Streaming platforms now function as the ultimate judges of artistic similarity and ownership, yet they operate without the transparency or accountability expected of a court of law. Supporters argue that this automation protects artists from widespread exploitation and “noise” tracks that dilute the royalty pool. Indeed, many musicians benefit from the clearer licensing that these tools provide.
However, the fundamental question remains: should the definition of “originality” be outsourced to a black-box algorithm that even the developers sometimes struggle to explain?
Governing Sound Through Software
Historically, music has developed through a messy, organic process of borrowing, imitation, and variation. These creative cycles relied on a shared human understanding of tribute versus theft. Automated enforcement replaces that cultural flexibility with constant, high-resolution supervision.
Creativity becomes something that must be cleared in advance by a digital gatekeeper, shifting the risk from artistic failure to administrative error. France’s implementation of these systems is likely to become a reference point for other nations and the broader European Union copyright framework. Governance is moving directly into the software, where culture is regulated through code updates rather than public debate.
Whilst the intention is not hostile (protecting artists and preventing fraud are noble goals), the method itself changes the nature of the art. Over time, these systems will inevitably influence what is written and recorded. Music will continue to evolve, but it will increasingly travel through channels where machines listen first, and humans second.
For a culture built on improvisation and surprise, this represents a profound and irreversible shift.
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