It started with a song that wasn’t real. In 2023, “Heart on My Sleeve”—a convincingly synthetic duet between Drake and The Weeknd—took over streaming platforms. This happened before the industry could even catch its breath. No one knew who made it, where it originated, or how to stop it. But what it represented was clear: AI Is Hijacking Music, and AI-generated music had broken through. This left the music industry with no system in place to respond.
Now, the response is forming. But rather than ban AI music outright, the industry is building something deeper. This includes a detection infrastructure embedded across every level of the music creation and distribution process. From training data and licensing databases to streaming platform algorithms, the goal is more comprehensive. It aims not just to react to synthetic music, but to trace, tag, and govern it before it ever reaches your ears.
The Viral Moment That Changed Everything
“Heart on My Sleeve” wasn’t just viral. It was a seismic shock to the idea of artistic identity and control. For decades, digital rights management focused on copying, distribution, and monetisation. However, those rules suddenly seemed laughably outdated. This occurred in the face of generative models capable of creating fake hits indistinguishable from real ones.
That one track forced the industry to confront an uncomfortable truth. The genie was out of the bottle, and fighting AI with takedown notices wouldn’t be enough.
AI Is Hijacking Music – And Now the Industry’s Fighting Back
Building Detection Into the Pipeline
“You can’t keep reacting to every new track or model—that doesn’t scale,” says Matt Adell, co-founder of Musical AI. “You need infrastructure that works from training through distribution.”
That mindset is now guiding a new generation of music tech startups. Their mission? Bake detection directly into the systems that power licensing, distribution, and playback.
Platforms like YouTube and Deezer are already using internal tools to flag synthetic audio at the point of upload. These tools aren’t just identifying tracks—they’re determining how visible those tracks become in algorithmic recommendations and search results. In essence, they’re applying content moderation to music itself.
Other platforms such as SoundCloud, Audible Magic, Pex, and Rightsify are also rolling out detection tech across their platforms. Some of it is built to analyse the source data behind songs, while others are tuned to identify imitation at the stem level.
Vermillio and the Stem-Level Revolution
Among the most advanced tools being developed is Vermillio’s TraceID framework. It goes beyond whole-track detection by breaking songs into individual components—such as vocal tone, melody, and lyrical phrasing. The tool scans each component for synthetic mimicry. It’s a tool that could mark the end of AI clones slipping through undetected.
Instead of punishing infringers post-release, TraceID allows rights holders to proactively licence tracks before they drop. Vermillio believes tools like TraceID could scale the authenticated music licensing market from $75 million in 2023 to a staggering $10 billion by 2025.
Rather than kill the rise of synthetic music, TraceID is about making it legitimate. If a song uses an AI-generated approximation of Drake’s voice, the system can tag it. It can also trigger a licensing workflow, and direct royalties accordingly.
Attribution From the Training Data
While most detection tools focus on finished music, others are zooming out even further—to the source. Some companies are analysing the training datasets used to build AI music models. They are identifying how much of the data comes from specific artists or works.
That level of attribution could fundamentally change licensing. Instead of lawsuits after a song goes viral, it allows for proactive agreements based on influence and contribution. Think of it as moving from plagiarism to inspiration—with a royalty system attached.
The implications are vast. As Sean Power of Musical AI puts it: “Attribution shouldn’t start when the song is done—it should start when the model starts learning. We’re trying to quantify creative influence, not just catch copies.”
Deezer’s Detection Playbook
At the platform level, Deezer is leading the charge. Its detection tools currently flag around 20% of all uploads as fully AI-generated. This is a dramatic jump from earlier in the year. These flagged tracks don’t get removed. Instead, they’re quietly deprioritised: stripped of algorithmic boosts and buried from editorial playlists.
Deezer’s Chief Innovation Officer Aurélien Hérault describes it as a balance between openness and protection. “We’re not against AI at all,” he explains. “But a lot of this content is being used in bad faith—not for creation, but to exploit the platform.”
Within months, Deezer plans to start publicly labelling AI music. It’s a move that mirrors social media’s attempts to flag synthetic content. This move could define how listeners relate to AI art going forward.
The Do Not Train Protocol: Music’s Opt-Out Movement
While some companies focus on detection post-creation, others are pushing for consent at the source. Spawning AI’s DNTP (Do Not Train Protocol) is a system that allows musicians to tag their work. It specifies that their work is off-limits for AI model training.
This concept mirrors similar efforts in the visual art world. In that realm, artists have successfully fought to prevent their styles from being absorbed into generative models. But audio, with its complex licensing landscape and lack of unified datasets, is proving slower to adapt.
There’s also a trust issue. As technologist Mat Dryhurst warns: “The opt-out protocol needs to be nonprofit, overseen by a few different actors, to be trusted. Nobody should trust the future of consent to an opaque centralised company that could go out of business—or worse.
Academic Research and Detection Algorithms
Recent papers published on arXiv and IEEE have attempted to formalise the detection of synthetic audio. Researchers are training models to identify subtle inconsistencies in waveforms and spectral patterns—telltale signs of generated content.
These tools are still in early stages, but they could eventually be deployed alongside commercial detection systems. Academic input is also essential to create standards—something currently missing from the patchwork of detection tools used today.
The Role of Global Regulation
Europe is taking the lead in digital content governance. The EU AI Act sets early precedents for consent, disclosure, and traceability. Discussions in the UK Parliament and legislative reviews in Canada, South Korea, and Japan suggest a trend. AI music detection will soon become a compliance issue, not just an ethical concern.
Licensing infrastructure providers are preparing for that shift. Rightsify and Pex are adapting their platforms. This ensures compliance with transparency clauses in regulatory drafts. These would mandate detection tags on synthetic music distributed across Europe.
The Economics of Detection Infrastructure
The detection and licensing ecosystem isn’t just about governance—it’s also a fast-emerging revenue stream. Vermillio’s projection of $10 billion by 2025 reflects a broader trend. Synthetic content will be allowed, even encouraged, but only if it pays.
Grimes’s platform Elf.Tech is a live example of this. She allows AI-generated music using her voice, provided creators share royalties. That model, while experimental, hints at how future infrastructure could monetise creativity without erasing identity.
What’s Next: Infrastructure as the New Gatekeeper
The industry is moving from reactive moderation to systemic governance. Platforms like Deezer, YouTube, and SoundCloud are beginning to act like regulators. They decide what gets seen, surfaced, and monetised. This isn’t based on genre or fame, but on synthetic provenance.
As tools like TraceID, DNTP, and academic detection models evolve, we may see a new industry standard. In the future, being synthetic isn’t a problem—being unlicensed is. The future of music might not be human or AI—it might simply be traceable.
