The music industry’s nightmare came true in 2023, and it sounded a lot like Drake.
“Heart on My Sleeve,” a convincingly fake duet between Drake and The Weeknd, racked up millions of streams before anyone could explain who made it or where it came from. The track didn’t just go viral — it broke the illusion that anyone was in control.
In the scramble to respond, a new category of infrastructure is quietly taking shape that’s built not to stop generative music outright, but to make it traceable. Detection systems are being embedded across the entire music pipeline: in the tools used to train models, the platforms where songs are uploaded, the databases that license rights, and the algorithms that shape discovery. The goal isn’t just to catch synthetic content after the fact. It’s to identify it early, tag it with metadata, and govern how it moves through the system.
“If you don’t build this stuff into the infrastructure, you’re just going to be chasing your tail,” says Matt Adell, cofounder of Musical AI. “You can’t keep reacting to every new track or model — that doesn’t scale. You need infrastructure that works from training through distribution.”
The goal isn’t takedowns, but licensing and control
Startups are now popping up to build detection into licensing workflows. Platforms like YouTube and Deezer have developed internal systems to flag synthetic audio as it’s uploaded and shape how it surfaces in search and recommendations. Other music companies — including Audible Magic, Pex, Rightsify, and SoundCloud — are expanding detection, moderation, and attribution features across everything from training datasets to distribution.
The result is a fragmented but fast-growing ecosystem of companies treating the detection of AI-generated content not as an enforcement tool, but as table-stakes infrastructure for tracking synthetic media.
Rather than detecting AI music after it spreads, some companies are building tools to tag it from the moment it’s made. Vermillio and Musical AI are developing systems to scan finished tracks for synthetic elements and automatically tag them in the metadata.
Vermillio’s TraceID framework goes deeper by breaking songs into stems — like vocal tone, melodic phrasing, and lyrical patterns — and flagging the specific AI-generated segments, allowing rights holders to detect mimicry at the stem level, even if a new track only borrows parts of an original.
The company says its focus isn’t takedowns, but proactive licensing and authenticated release. TraceID is positioned as a replacement for systems like YouTube’s Content ID, which often miss subtle or partial imitations. Vermillio estimates that authenticated licensing powered by tools like TraceID could grow from $75 million in 2023 to $10 billion in 2025. In practice, that means a rights holder or platform can run a finished track through TraceID to see if it contains protected elements — and if it does, have the system flag it for licensing before release.
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