News & Commentary

BMG has announced a global alliance with Suno, following Universal's settlement with Udio and Warner's agreements with both. Every deal follows the same pattern: the AI company trained on the catalog first and paid for it after.

Read that carefully. Training consent is now a licensed commodity with a price. The majors did not win by blocking AI. They won by being able to prove ownership, prove non-consent, and negotiate from documentation.

Those deals cover major-label catalogs. If you are an independent artist, you were not at that table, and no one is coming to negotiate for you.

The asset the majors leveraged was not just copyright. It was the record: what they own, since when, and the fact that they never said yes. Independent artists can build that same asset today. AIAPS registers a perceptual fingerprint of the recording, a timestamped public registry entry, and a machine-readable notice that the work is not authorized for AI training. No watermark, no altered audio, just a durable public record.

If the last two years taught the industry anything, it is that "they trained on it anyway" ends in a settlement for whoever can prove they never consented. Make sure that is provable for your music.

A Munich court has issued Europe's first ruling on AI music training, finding in GEMA's case against Suno that training on unlicensed recordings infringes copyright, even where the training happened in the United States. In the US, the majors' case against Suno reached a summary judgment hearing in Boston, with trial not expected before 2027. Sony has settled with no one, and Universal continues its case against Suno despite its deal with Udio.

One detail from the US case deserves attention. Audio fingerprinting is how the labels showed what was in the training data, a complaint that has grown to more than 61,000 recordings. The evidence layer of this fight is fingerprints plus documented ownership and non-consent.

Either outcome makes the record valuable. If training is ruled infringement, a timestamped declaration of non-consent is exactly the evidence a claim is built on. If training is ruled fair use, licensing deals and opt-out regimes become the norm, and a machine-readable public declaration is what an opt-out looks like.

The artists who benefit from either future are the ones whose catalog was documented before the ruling landed, not after.

RIAA, IFPI, A2IM, WIN, IMPALA, the Recording Academy, SAG-AFTRA, and the Human Artistry Campaign have announced a unified, voluntary labeling standard distinguishing AI-Generated from AI-Assisted sound recordings, intended for adoption across digital music services.

This is real progress. For the first time, the industry has a shared vocabulary for disclosing how a recording was made.

But a label is a claim, and a claim is only as strong as the record behind it. A tag in a metadata field can be edited, stripped, or misapplied, and nothing connects it to the actual audio.

This is the problem AIAPS is built around. A declaration in the AIAPS registry is bound to a perceptual fingerprint of the specific recording, timestamped, and publicly checkable at a permanent certificate URL. As disclosure labels roll out, expect the question to shift from what the tag says to who declared it, when, and whether it can be verified. That is what a registry is for.

As of July 15, TIDAL labels wholly AI-generated tracks and no longer pays royalties on them, the first streaming platform to do so. Deezer, which began tagging AI-generated uploads in 2025, has now tagged more than 13 million tracks and keeps them out of editorial playlists and algorithmic recommendations.

The demand side of provenance has arrived. Platforms now need to know, at scale, whether a recording was made by a person, because payout policy and playlist placement depend on the answer.

Both platforms lean on detection, and detection is an arms race. Deezer's latest detector claims 99.8 percent accuracy; across tens of millions of tracks, the remainder is still tens of thousands of wrong calls, and generative models improve every quarter.

Detection tells a platform what a file looks like. A registry tells it what is on record: who registered the recording, when, and under what policy, bound to a fingerprint that survives compression. As platforms start pricing the difference between human and synthetic music, verifiable records are the durable half of the answer.

In October 2025, Universal settled its copyright suit against Udio and signed a licensing agreement. In November, Warner followed, settling with both Suno and Udio and licensing its catalog for new AI models. Udio has since signed agreements with Merlin, Kobalt, and the National Music Publishers' Association.

The sequence matters. These companies trained on unlicensed catalogs, were sued, and then paid for licenses. The lawsuits did not shut the products down. They converted unauthorized training into a commercial deal.

What gave the labels that outcome was documentation. They could show what they owned, since when, and that they never authorized training. Evidence first, leverage second, revenue third.

That playbook is not available to an artist whose ownership and non-consent exist nowhere but their own hard drive. The AIAPS registry gives independent artists the same first step: a timestamped, fingerprint-bound public record that a recording is not authorized for AI training.