Who’s responsible for labeling AI music?
The digital music supply chain is facing a new kind of systemic shock, driven by the exponential growth of generative AI. The underlying tech not only rewrites the rules of musical creativity, but also fundamentally challenges the industry’s incumbent systems of distribution, attribution, and royalty payments.
As a flood of AI-generated music begins to overwhelm streaming services, a new, high-stakes battle has erupted over a single, critical question: Who’s responsible for labeling it?
This is not a theoretical question about transparency in the abstract. It represents the industry’s first coordinated attempt to impose governance on a technology that threatens to destabilize its core infrastructure. Reading between the lines of interviews and press releases, the fight over AI labeling reveals itself as aproxy war for control, value, and the very definition of what qualifies as "music" on commercial platforms.
Importantly, this debate isn’t new. The Coalition for Content Provenance and Authenticity (C2PA), backed by major tech players including Adobe and Microsoft, first introduced its framework for content credentials in 2021; Believe, the parent company of TuneCore, teased its own internal “AI Radar” detection tool in 2023.
But with rights holders now feeling a direct threat to their market share from AI tracks, labeling has quickly emerged as the first and most popular line of defense.
In response, competing philosophies have emerged. Streaming service Deezer has adopted a top-down, platform-enforced model, using proprietary technology to detect and label AI content automatically. In contrast, market leader Spotify is advocating for a collaborative, supply-chain disclosure model that relies on voluntary metadata submissions from creators, rights holders, and distributors.
These divergent strategies open up a crucial question: Who bears the responsibility for keeping listeners informed? Is it the streaming platforms, the labels and distributors, the artists themselves — or some combination of them all?
Beyond merely defining the future of transparency, the answer to these questions will determine the economic value and cultural weight of human artistry in an increasingly synthetic media landscape.
Why this matters: A multi-stakeholder dilemma
While the logistical details may feel esoteric, the push for AI labeling is not a niche issue. It affects the entire music ecosystem, from creator to consumer, with each group possessing distinct and compelling reasons to care about how, and if, AI-generated music is identified.
For rights holders, the primary concern is market share. As AI-generated songs begin to compete with human-made tracks in the marketplace, labeling becomes a tool to differentiate and protect the value of human artistry and the traditional music-making process. Clear proof-of-AI signals also provide crucial data, quantifying the flow of synthetic content and giving rights holders leverage in licensing negotiations with both AI developers and DSPs.
For platforms, it’s about preserving economic and catalog integrity. Deezer reported in September 2025 that up to 70% of all streams for fully AI-generated tracks on its platform are fraudulent, involving bots or streaming farms that divert royalties from legitimate human artists. For services like Spotify and Deezer that are already battling an unsustainable volume of daily uploads, filtering AI-driven spam is a matter of financial necessity.
For distributors, the gatekeepers between artists and platforms, the focus is on quality control and relationship management. Flooding platforms with low-value AI content can damage their standing with streaming services, potentially leading to lower acceptance rates for their entire catalog. Many distributors now use automated detection systems to flag or block AI content before upload, essentially becoming the first line of enforcement.
For consumers, this is a crisis of trust. A recent YouGov survey found that only one in five Americans are confident they can spot the use of AI in music. This uncertainty could erode the entire digital music experience by threatening the artist-fan relationship that underpins the industry. Labeling lets listeners consciously direct their streams toward human artists or AI-assisted creators, essentially allowing them to "vote with their wallet."
The three tiers of trust: How AI labeling paradigms work
In response to these pressures, three distinct models for AI music labeling have taken shape, which can be understood as a progression from centralized, reactive control to decentralized, proactive verification.
The models are: a system of Platform-Led Enforcement, an industry-led system of Supply Chain-Led Disclosure, and a technology-led system of Source-Level Provenance. The table below provides a high-level comparison of these three paradigms, outlining their core principles, responsible parties, and fundamental tradeoffs.
Tier 1: Platform-led enforcement (the Deezer model)
Deezer has taken the most aggressive stance against AI of all the major DSPs, positioning itself as a hands-on, high-trust, proactive curator of its catalog.
Some important commercial context here is that Deezer is far from a market leader: MIDiA Research estimated the platform’s share of global streaming subscribers at just 1.3% in 2023. As a smaller player, Deezer has much greater flexibility to take risks and experiment as a first mover, compared to competitors like Spotify and Apple Music that are under a brighter public spotlight. In fact, Deezer’s academic research team has quite an active history of publishing on AI; its proprietary detection tool was in development for over a year prior to its public launch in 2025.
AI detection models typically work by spotting subtle audio imperfections that emerge in the generation process. In a June 2025 paper, Deezer described these artifacts as frequency "peaks" that act as an architectural fingerprint of the AI model used. Beyond audio analysis, Deezer researchers are also developing methods that analyze sources beyond simple audio patterns — such as lyrical content and paralinguistic speech characteristics (e.g. prosody and intonation) — to make AI detectors more robust.
Once a track is flagged by its system as "100% AI-generated," Deezer's policy is swift and unforgiving. The album hosting the track is tagged with a visible, non-disableable "AI-generated content" label, and the track is removed from all algorithmic and editorial playlists to protect the royalty pool. All in all, this approach communicates a clear value judgment: artists using generative AI as their primary creative tool are less worthy of discovery.
While this top-down, automated enforcement model allows Deezer to act decisively, it is riddled with inherent limitations.
Audio AI detectors are brittle in the face of simple audio manipulations like resampling or pitch shifts, which are common in the creative process for modern music. They can also struggle to keep up with the rapid release of new and open-source AI models — creating a perpetual and costly cat-and-mouse game, where detection methods are always one step behind the most cutting-edge generation technology. And as more producers integrate AI into hybrid workflows, the boundary between “human” and “AI” music becomes increasingly fuzzy, and arguably unmappable.
Distributors face the same challenges when deploying automated detection pre-upload. Companies like DistroKid, TuneCore, and CD Baby are increasingly using AI detection tools to filter content before it reaches DSPs, essentially adopting Deezer's enforcement model one step earlier in the chain. As for what they do with that information, the stance varies. TuneCore and CD Baby outright reject 100% AI-generated tracks, while other distributors allow AI tracks through, with restrictions on areas like impersonation and high-volume uploads.
This creates a double layer of automated gatekeeping — but also a critical point of failure. If a distributor's detector is overly aggressive, it blocks legitimate artists from releasing music; if it's too lenient, it lets AI slop through, damaging the distributor's relationship with platforms. In the coming months, for better or for worse, we will likely see distributors take an increasingly public stance as de facto arbiters of what counts as “too AI.”
Tier 2: Supply chain-led disclosure (The Spotify/DDEX model)
In contrast to Deezer’s enforcement-led strategy, Spotify is championing a more distributed approach: voluntary disclosurethrough existing metadata standards. This positions Spotify as a facilitator of transparency, rather than its enforcer.
Spotify’s official announcement on the topic appears to respond directly to Deezer’s top-down approach, arguing that the industry needs a more nuanced solution (emphasis added):
“We know the use of AI tools is increasingly a spectrum, not a binary, where artists and producers may choose to use AI to help with some parts of their productions and not others. The industry needs a nuanced approach to AI transparency, not to be forced to classify every song as either ‘is AI’ or ‘not AI.’ ”
Spotify’s strategy is built on the framework of the Digital Data Exchange (DDEX), a consortium that has set the standards for metadata in the digital music industry since 2006. DDEX essentially acts as the industry's plumbing, defining the formats that labels and distributors use to deliver music and its associated data to platforms.
In response to the rise of AI, a DDEX "Artificial Intelligence Ad hoc Group" was announced in April 2025, with the goal of updating its standards to let creators disclose if and how AI was used in a track. Spotify’s press release confirmed further that the standard is designed to be granular, allowing rights holders to specify whether AI was used for vocals, instrumentation, or post-production.
There is industry precedent for this kind of standards-level adaptation to emerging tech. In 2019, at the height of the smart speaker boom, DDEX introduced the Media Enrichment and Description (MEAD) standard to support more detailed metadata for voice-activated search. That standard took two years to implement — which, while relatively fast for the music industry, demonstrates that widespread adoption of new standards is a slow and deliberate process.
For Spotify's DDEX-based approach to be effective, it will require near-universal adoption from hundreds of global distributors and all major streaming platforms — a collaborative effort that is historically difficult to achieve in a competitive, political industry.
Moreover, the system's greatest weakness is that it is entirely voluntary. The responsibility to provide this data rests not with the DSPs, but with the artists, labels, and distributors during the upload process.
This essentially creates a "liar's dividend,” where bad actors have no incentive to self-disclose. Even good actors have no incentive to disclose, either, since labeling their music as “AI” will likely lead to major public backlash in the current climate.
The most problematic content — the very "AI slop" that platforms are trying to filter — will likely remain unlabeled, forcing the likes of Spotify to rely on separate, reactive spam filters to catch it.
Tier 3: Source-level provenance (the C2PA Model)
A third, more fundamental paradigm exists outside the immediate music industry conversation, but offers a potential solution to the flaws of the other two models.
Content Credentials, a metadata type developed by the Coalition for Content Provenance and Authenticity (C2PA), move beyond external labels to a system of verifiable, cryptographic proof at the source of creation. Far from a hypothetical concept, C2PA specs are already being implemented in Adobe products and on major social platforms including TikTok, Instagram, and LinkedIn.
While its adoption in music is still nascent, it represents a potential north star for building a truly trustworthy digital ecosystem from end to end. In fact, music and audio orgs such as the RIAA, Roland, and Avid (maker of Pro Tools) are already C2PA members, indicating that the key stakeholders needed for audio adoption are already at the table.
Instead of an external tag, a Content Credential is a secure, tamper-evident history of a digital file's creation and modification that is bound directly to the file itself. This manifest acts as both a digital birth certificate and a running log of changes.
Let’s consider this hypothetical scenario for music: An AI tool like Suno generates a track and digitally signs an assertion in the file's log stating, "This was created by AI." If a human artist then imports that track into a DAW like Pro Tools to add vocals, the DAW software would add a new, cryptographically signed assertion to the manifest, listing the original AI track as an "ingredient.” This would create an unbroken, verifiable chain of custody that clearly distinguishes human and machine contributions.
At its best, the C2PA guidelines improve on the others by solving their core flaws — replacing Deezer's fallible detection with cryptographic verification, and Spotify's voluntary disclosure with automated provenance, providing proof that other systems lack. It shifts responsibility from the end of the supply chain (platforms and distributors) to the very beginning (the creation tool itself). In practice, Content Credentials could function as a foundational data layer that automatically populates the corresponding DDEX fields for AI usage, or trigger automatic labels on Deezer.
Its primary barrier to adoption in music is not technological advancement, but rather ecosystem complexity. Unlike DDEX, which only involves the final delivery step of the supply chain, C2PA requires buy-in from every link — AI model developers, DAW manufacturers, distributors, and streaming platforms — to generate, preserve, and display the credentials.
In reality, critical provenance metadata can easily be stripped at each of these steps in the creative workflow. And getting a fragmented industry to adopt the same standard is a familiar, decades-old problem in music, now exacerbated further by the speed and scale of AI.
Strategic outlook: A hybrid, bifurcated future
So what's the most plausible path forward? Neither Deezer's enforcement model nor Spotify's disclosure model is a complete solution on its own. The immediate future will likely be a hybrid — where platforms use automated detection as a blunt instrument to fight the most blatant spam, while simultaneously supporting voluntary disclosure standards like DDEX to serve legitimate, transparent creators.
The C2PA framework could serve as the north star. While end-to-end integration is a massive hurdle, its core principles — cryptographic verification and tamper-evident provenance at the source of creation — provide the benchmark against which all other solutions will be measured.
As deepfakes and fraud become more sophisticated, the need for a system based on verifiable proof, rather than fallible detection or voluntary trust, will become undeniable. Its adoption by adjacent industries like social media and news makes its eventual arrival in music a matter of "when," not "if.”
However, for a large and growing segment of AI-generated music, the debate over labeling on traditional DSPs may become a secondary non-issue.
AI music platforms like Suno and Udio are rapidly evolving from mere tools into self-contained ecosystems with tens of millions of active users. As the recent virality of OpenAI’s video AI app Sora suggests, a future is emerging where users increasingly create, share, and listen to AI music within AI-native apps, bypassing traditional DSPs altogether.
This points to a potential bifurcation of the market: a "traditional" DSP ecosystem grappling with how to label human-made versus AI-assisted music, and a separate, AI-native ecosystem where the content is unapologetically synthetic and lives on its own terms.
The primary barrier to this future is monetization. Until creating and listening on Suno or Udio generates significant revenue for creators, DSPs will remain the primary distribution goal. But as these platforms mature and potentially build out their own monetization systems, this dynamic could shift dramatically.
Ultimately, the responsibility for labeling AI music is a shared one, requiring a layered strategy over time. The journey is not about choosing one of three competing options, but about recognizing each model's distinct role in a developing governance system.
Automated enforcement is the reactive, necessary baseline to fight today's fraud; collaborative disclosure is the pragmatic, industry-wide step toward tomorrow's transparency; and verifiable provenance is the ultimate goal for building a digital music ecosystem where the distinction between human and machine creation is not a matter of opinion or honesty, but of cryptographic fact.