In an earlier article, I posed nine questions to music industry leadership about AI and music. The fifth question was: Is the existing music tech infrastructure "fit-for-purpose"?
One of the clearest recent responses to the question of whether today's music technology infrastructure is fit-for-purpose comes from Andreea Gleeson in Music Business Worldwide.[1]
Gleeson argues that the industry's central AI challenge is no longer the technology itself, but the commercial and technical infrastructure through which AI-enabled music moves, from creation to distribution, attribution, monetization, and consumption. Her central insight is that trust depends not simply on better AI detection, but on interoperable systems capable of carrying provenance, rights information, and creator intent throughout the music value chain. That diagnosis raises a further question:
What kind of governance is required to ensure that this infrastructure develops in a coordinated, interoperable, and trustworthy way? The most important governance problem in AI music may be infrastructural, but infrastructure alone is not enough. Infrastructure requires governance.
Effective governance depends on an ecosystem capable of moving trusted information reliably between creators, creative tools, distributors, labels, publishers, digital streaming platforms (DSPs), collecting societies, rights holders, and listeners. Consent, attribution, compensation, labeling, takedown, discovery, and listener trust all depend on information moving reliably through the chain. When that information breaks down in the chain between the studio, the distributor, the label, the publisher, the DSP, the collecting society, and the listener interface, even strong ethical principles become weak operational practice.
From Detection to Disclosure
Detection technologies will continue to play an important role in identifying AI-generated content, unauthorized voice replicas, or potentially infringing works. However, these detection systems often attempt to reverse-engineer a complete track. These systems may ask whether AI was involved, which model may have been used, whether a voice sounds similar to another artist, or whether a melody resembles a protected work.
That approach will remain necessary, but it is inherently reactive. As Gleeson argues, a more durable approach begins earlier in the creative process, rather than attempting to reconstruct the history of a recording. If the digital audio workstation (DAW) or creative tool writes the "recipe" while and where the music is made, then downstream systems can know the recipe's ingredients and more about what happened: who contributed, what models were used, which assets were licensed, and what rights restrictions apply.
This is why the DAW layer is strategically important. It is where human intent, software assistance, source material, prompts, stems, edits, collaborators, and session metadata come together.
DDEX's Recording Information Notification (RIN) standard was created to communicate studio-session metadata and contributor information into the wider music value chain.[2] AI adds new questions to that existing metadata challenge: model identity, type of AI contribution, source authorization, voice consent status, output permissions, and human modification.
The question is no longer simply whether that information can be captured. It is whether the ecosystem can agree on what should be captured, how it should be represented, and where it should travel. Not everyone wants to know the entire recipe or detailed list of ingredients—but they should be there for anyone to use, as needs require, rather than having to be deduced after the fact.
Beyond Technical Interoperability
Interoperability is often treated as a technical challenge involving metadata standards, APIs, or data exchange protocols. Those capabilities are essential, but they represent only one layer of broader governance architecture. Sustainable AI governance depends on interoperability across multiple layers of the music ecosystem, each requiring coordination among different stakeholders and serving a different governance function.
Systems need technical interoperability, so information can move between tools and platforms. They need semantic interoperability, so the meaning of terms such as "AI-assisted," "AI-generated," or "authorized voice" is preserved when they move between tools, processes, systems, and organizations. They also need rights interoperability, so permissions and restrictions can remain attached to a work as it moves through the value chain.
The Platform Layer
The DSP layer holds another set of levers. Platforms can label AI contributions, enforce impersonation rules, remove fraudulent uploads, alter recommendation eligibility, adjust monetization rules, and provide listeners with more meaningful credits.
One DSP's commitment to a DDEX-developed industry standard for AI disclosures in music credits illustrates how platform policy and metadata standards can reinforce one another. But platform-by-platform disclosure is not enough. If each service invents its own labels and rules, creators will face another fragmented compliance burden. A disclosure could mean one thing on one platform and something different on another.
The value of disclosure therefore depends not only on whether information is displayed, but also on whether its meaning remains consistent across the value network.
Building Compatible Layers of Trust
The ecosystem is already developing some of the building blocks.
For example, DDEX describes its mission as making the exchange of data and information across the music industry more efficient.[3] C2PA similarly offers an open standard for content provenance and edits that can help establish the origin and history of digital content.[4]
Neither approach solves the AI infrastructure problem alone. Music will need both domain-specific rights metadata and broader provenance methods. The goal is not a single universal standard, but a set of compatible layers that allow identifiers, disclosures, claims, and permissions to travel together. That is what turns interoperability from a technical convenience into a pre-requisite for trust.
Developments in Regulation
Regulatory pressure is moving in the same direction. The EU AI Act's general-purpose model obligations include technical documentation, a copyright policy, and publication of training-content summaries.[5] Those requirements address model providers, but music-specific infrastructure must address the rest of the value chain. A training summary is useful, but it will not tell a DSP how a particular track was made. A track label is useful, but it will not prove that a vocal replica was authorized. The system needs both upstream and downstream records.
The Opportunity for Engagement
For the Music Technology Coalition, the infrastructure challenge is an opportunity to build a neutral testbed. The Coalition could map metadata handoffs from DAW to distributor to DSP, prototype AI-disclosure fields, run interoperability tests among music-tech startups, and publish implementation guides for smaller creators and companies.
It could also convene technical, legal, and creative stakeholders around practical questions: What information should be mandatory? What should be optional? What should be private? What should listeners see? What should auditors see? These decisions will determine whether infrastructure supports consent, attribution, compensation, and trust rather than merely moving more data.
Without connected infrastructure, the AI music ecosystem will rely on self-reporting, inconsistent labels, and after-the-fact disputes. With connected infrastructure, transparency can become a workflow rather than a slogan. Governance is what makes that workflow trustworthy.
Do you want to learn more about the challenges facing the music technology ecosystem? Then head over to Unity Gain - our hub dedicated to the music tech industry, with insights on issues from the increasingly important role of AI in music to issues around interoperability, transparency, and trust.
[1] "The Music Industry Is Trying to Guess the AI Recipe After Dinner was Served," MBW, 6 August 2026.
[2] DDEX, Recording Information Notification (RIN).
[3] DDEX, Digital value chain standards for the music industry.
[4] Coalition for Content Provenance and Authenticity, C2PA: Verifying media content sources.
[5] European Commission (2025). General-purpose AI obligations under the AI Act.