AI is changing how music can be made, marketed and discovered—but the industry’s biggest questions are about more than songwriting tools. They concern whether copyrighted recordings can be used to train generative models, who controls a synthetic imitation of a person’s voice or performance, how AI use should be disclosed, and whether generated tracks should qualify for streaming revenue or chart positions.
The effects are not all settled outcomes. Some are industry policies or proposed disclosure practices; the headline economic figures are forecasts, not measured losses. Here is what those changes mean for creators, companies and listeners.
Where AI is changing the music business
Generative AI can contribute to musical works and recordings, while other AI uses may assist parts of a human-led process. That distinction matters: a track primarily generated by AI raises different questions from a substantially human-created recording that used AI for some expressive elements. AI’s effects therefore extend across the creative process, rights and licensing, distribution, creator economics, and how listeners and chart compilers identify music.
The industry debate is not simply whether musicians should use new tools. It is also about the conditions under which those tools are trained and used, how much human contribution is present in a finished recording, and whether that information travels with the track.
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What copyright questions does AI raise?
Can AI-generated music be copyrighted?
In the United States, the Copyright Office has addressed digital replicas and the copyrightability of generative-AI outputs in separate parts of its AI report series. Part 1, on digital replicas, was published July 31, 2024; Part 2, on copyrightability of generative-AI outputs, was published January 29, 2025. These are parts of a U.S. policy process, not a worldwide ruling. Their subjects reflect two separate concerns: whether an output can qualify for copyright protection, and whether a person’s identity or performance has been digitally imitated.
The relevant practical question is how much human creative contribution is present and what role AI played. The report series addresses copyrightability; it should not be read as establishing that every AI-generated song either automatically qualifies for protection or can never qualify.
Can AI use copyrighted recordings to train?
Training is a distinct rights question from ownership of a generated track. The U.S. Copyright Office’s Part 3 on generative-AI training was released in pre-publication form on May 9, 2025. The published sequence identifies the topic as under examination, but the materials available here do not establish whether a final version had appeared by October 7, 2026. Nor does this U.S. process settle the rules in other jurisdictions.
Rights concerns can arise both from the use of existing works to train models and from a generated output competing with human-made music. The legal and commercial outcome depends on applicable law, authorization and the circumstances of use; the cited policy materials do not establish a universal answer for every model or recording.
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What about an AI imitation of a voice or performance?
Digital replicas raise questions about identity and performance that are not identical to copyright in a composition or recording. The Copyright Office’s first report part specifically addresses digital replicas. For musicians, the distinction matters when evaluating an imitation of a recognizable voice or performance as well as the use of a copyrighted song.
What do the economic forecasts say—and not say?
A 2024 study commissioned by CISAC and conducted by PMP Strategy modeled possible effects of generative AI. Its figures describe forecasts, not losses or market shares already observed. The study’s stated concern is that creators could face pressure both when works are used in training without remuneration and when generated outputs substitute for traditional revenue streams.
| Measure | Study estimate | How to read it |
|---|---|---|
| Music creators’ revenues at risk of loss | 24% by 2028 | Forecast in the 2024 CISAC-commissioned PMP Strategy study; not a measured loss. |
| AI-generated music’s potential share of traditional music-streaming platform revenues | About 20% by 2028 | Forecast in the same study, not an observed streaming share. |
| AI-generated music’s potential share of music-library revenues | About 60% by 2028 | Forecast in the same study, not an observed library-market share. |
| Annual music-sector revenue for generative-AI providers | €0.1 billion in 2023; projected €4 billion by 2028 | Study estimates of provider revenue, not independently established causal measurements. |
The study’s conclusion is that creators could lose revenue on two fronts: unremunerated use of works by generative-AI models and substitution by AI-generated outputs. That is the commissioned study’s analysis, not proof that either effect will occur at the forecast scale.
For broader context, IFPI reported recorded-music trade revenue of US$29.6 billion in 2024, up 4.8% year over year, and subscription-streaming revenue growth of 9.5%. These are reported market figures, not evidence that AI caused either growth or decline.
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How are AI use and chart eligibility being handled?
Track-level labels: “AI-Generated” and “AI-Assisted”
On July 10, 2026, an IFPI-led group that included RIAA, A2IM, WIN, IMPALA, The Grammys, SAG-AFTRA and Human Artistry Campaign announced two proposed track-level categories. The distinction is based on how much generative AI contributed to a recording’s creative elements:
| Category | Meaning in the announced system |
|---|---|
| AI-Generated | Generative AI created the entirety or primary portion of the recording’s creative elements. |
| AI-Assisted | The recording is substantially human-created, but AI contributed some expressive elements. |
The announcement describes metadata and delivery systems as the means of supporting these labels. It did not cover AI use in lyrics, composition, music videos or cover art, and described the labels as becoming available in the near future. That is not evidence of universal adoption by streaming services or other platforms.
IFPI chart principles
On July 30, 2026, IFPI announced principles for recordings developed with AI to qualify under its chart approach. They call for use of properly authorized and lawful services, substantial human creation, compliance with relevant rights laws and service terms, appropriate downstream signaling of AI use, and no manipulation concerns. IFPI said it was rolling the principles out to charts it directly manages and working with its national-group network to extend them.
These are IFPI network principles, not a single rule governing every music chart. The announcement’s emphasis on authorization, human contribution, transparency and integrity shows that chart eligibility is being treated as more than a question of whether AI was involved at all.
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What the changes mean for musicians, companies and listeners
Artists and songwriters
- Distinguish a limited creative aid from AI that supplies the primary expressive content of a recording.
- Keep clear records of human contributions and the authorization or terms associated with tools used when rights, registration or distribution questions may arise.
- Do not assume that a tool’s output, a training source or an imitation of a voice is cleared simply because it can be generated. Applicable rights and laws still matter.
These are practical precautions suggested by the emerging policy and disclosure frameworks, not legal advice.
Labels, distributors and streaming services
Implementation depends on reliable rights information and metadata moving through delivery systems. Companies also have to consider disclosure, applicable service terms and detection of stream or chart manipulation. The July 2026 announcements set out proposed labels and IFPI principles; they do not verify that platforms have adopted them consistently.
Listeners and chart audiences
Track-level labels are intended to distinguish primarily generated recordings from substantially human-created ones that use AI in some expressive elements. Their usefulness depends on availability and adoption: in the July 2026 announcement, labels were described as forthcoming, not already universal. IFPI’s chart approach adds a separate question about authorization, human contribution and integrity.
Is AI an opportunity for music?
IFPI CEO Victoria Oakley described the industry’s position this way: “When properly licensed and authorised, AI provides exciting opportunities to enhance the creative toolkit.” That is an industry position conditional on licensing and authorization, not evidence that benefits have already been realized for creators or the market as a whole.
The opportunity and the dispute can coexist. AI may serve as a tool in human-led work, while unauthorized training, imitated performances, unclear disclosures or substitution of generated music raise different concerns. The industry’s emerging responses focus on drawing those distinctions rather than treating all AI use as equivalent.
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