^{(Article voiceover performed by Pelczynski’s AI voice model via Voice-Swap.ai)}

The law did not lead the voice economy. But it has now established transparency as one of its first meaningful conditions of entry.

Long before that requirement took effect, artists were asking where their voices might travel. Rightsholders wanted to know whether models could be copied or extracted. Brands needed clarity once outputs left the studio. Partners asked whether permissions could be limited, audited or withdrawn.

These were not abstract ethical questions about a distant technological future. They were immediate conditions of participation, raised by the artists, rightsholders and brands already building the voice economy alongside us. At Voice-Swap, they translated into verified consent, controlled training inputs, secure source-of-truth voice models, usage-scoped permissions and auditable approvals.

We built those protections before law required them because the people and organizations placing their trust in us already did. The EU AI Act has now brought one part of that logic into law. The important question is what its transparency requirements actually solve for AI voice, and what remains unsolved.

What Article 50 actually changes for voice

Since 2 August 2026, Article 50 of the EU AI Act has applied a new set of transparency obligations to providers and deployers of certain AI systems.1 The distinction between those roles is important.

A provider is generally the organization that develops an AI system, or has it developed, and places it on the European market or puts it into service under its own name. A deployer is the organization using that system under its authority for professional purposes: a brand, label, studio, broadcaster or other business deploying the resulting content. The same organization may occupy both roles in different circumstances. The obligations can also extend beyond companies established in Europe where the resulting AI output is used within the EU. For AI voice, Article 50 establishes three particularly relevant forms of transparency.

First, where an AI system interacts directly with a person, the provider must generally ensure that the person knows they are interacting with AI, unless that fact is already obvious from the circumstances. This matters for conversational voice agents, interactive characters and other systems in which a synthetic voice is speaking directly with an audience rather than simply appearing within produced content.

Second, providers of systems that generate synthetic audio must ensure that outputs are marked in a machine-readable format and detectable as artificially generated or manipulated. The marking must be effective, reliable, robust and interoperable, taking account of technical feasibility, the nature of the content and the state of the art. This is the technical transparency layer. Its intended audience is not only the person hearing the voice. It is also the platforms, detection systems and downstream services that need a scalable way to distinguish synthetic content from conventional recordings.

Third, deployers must provide a clear, human-perceivable disclosure where AI-generated or manipulated audio constitutes a deepfake. This is separate from the provider’s machine-readable mark. Where the deepfake threshold is met, an embedded signal that requires a technical tool to detect it is not enough on its own.

Importantly, the Act does not define every synthetic voice output as a deepfake. The content must resemble an existing person, object, place, entity or event and falsely appear to be authentic or truthful. The European Commission’s guidance treats this as a contextual assessment. It considers the degree of resemblance, the substance of the content, the way it is deployed and the reasonable expectations of the intended audience.

That distinction matters enormously for creative and commercial voice. A synthetic voice used to make a real person appear to have said something they did not say presents a clear deception risk. A clearly fictional character used within an obviously produced advertisement, performance or entertainment experience may create a very different audience expectation. Even where creative or fictional content falls within the deepfake definition, the Act permits disclosure to be made proportionately and without unnecessarily disrupting the experience of the work.

The result is not a blanket requirement to place a visible “AI-generated” label on every authorized voice output. Providers must build technical detectability into inscope systems. Deployers must separately assess whether the particular presentation creates the false appearance of authenticity that triggers humanfacing disclosure. The Act therefore recognizes an important difference between synthetic creation and deceptive presentation.

These obligations are not simply policy guidance. Article 50 is legally enforceable. The European Commission’s Code of Practice on Transparency of AI-Generated Content is voluntary, but has been recognized by the Commission and the AI Board as an adequate framework through which providers and deployers may demonstrate compliance. Organizations that do not follow the Code remain responsible for showing that their alternative measures are equivalently adequate.

What transparency actually solves

More simply said, Article 50 makes synthetic content more legible. At the technical level, machine-readable marking creates a signal that an output was generated or manipulated by AI. Where that signal remains attached and detectable, it can support platform identification, provenance systems, content moderation, audit and future interoperability.

At the audience level, disclosure helps people understand when they are interacting directly with AI or encountering content that could otherwise falsely appear authentic. It gives them information with which to calibrate their trust.

At the market level, the Act establishes clearer responsibilities across the value chain. The provider must consider how synthetic outputs will be marked. The professional deployer must consider how the content will be perceived. Transparency can no longer be treated solely as a voluntary decision made at the point of publication. This is meaningful progress, particularly for the creative and commercial spheres. It helps answer, “Was AI involved in creating or manipulating this content?” For voice, that is an important question. It is simply not the only one.

What transparency leaves unsolved

An unauthorized voice clone can be correctly marked as AI-generated and remain entirely unauthorized. An authorized voice model can generate an output that is transparent, but outside the permitted territory, term or use category. A machine-readable mark may identify an output as synthetic without identifying the model that generated it, the human identity represented, the party that granted permission or the scope of that permission. Transparency describes something about the content. It does not, by itself, establish the legitimacy of the underlying use.

A label is not a license.

Article 50 does not create a new property right in a voice. Nor does it establish whether the recordings used to train a model were lawfully sourced, whether the relevant person consented to the creation of that model, who is entitled to operate it, how they may use it, whether they should be compensated or how authorization may later be revoked. It also does not, on its own, resolve what happens when an unauthorized model or output is discovered.

Existing legal routes still depend on the facts. Copyright may apply where protected recordings, scripts or other works have been copied. Performer rights may apply where an actual protected performance or recording has been captured or exploited. Trade marks, passing off, contractual restrictions, confidentiality, impersonation policies and fraud rules may each provide leverage in the right circumstances.

But vocal similarity alone does not automatically satisfy any of those legal tests. Transparency may improve the evidence available in an enforcement action. It does not create the underlying right or guaranteed removal. This is the authorization gap.

What the voice economy must build next

The practical implication is that responsible voice infrastructure requires at least three connected layers. The first is transparency, “Was AI involved?” The second is authorization, “Was this identity permitted to be represented by this model?” The third is accountability, “Was this particular output generated within the approved scope, and can that be demonstrated?”

At Voice-Swap, our work is to connect all three. That begins with verified authority and controlled training inputs. It continues through the creation and secure custody of a source-of-truth voice model. It requires usage permissions that define the approved purpose, channel, territory and term. It then carries those permissions into generation through model identifiers, output records, approved-user logs and machine-readable provenance. Every authorized use should ultimately be capable of resolving to one assertion:

This vocal derives from model X, representing person Y, under authorization Z, valid for use category A.

That assertion goes beyond identifying an output as synthetic. It connects the technology back to the human authority that made the use legitimate. The same distinction must carry into protection and enforcement. Providers and their partners need to distinguish unauthorized models from unauthorized outputs, copyright infringement from independent imitation, and a legal removal right from a discretionary platform-policy route. They need systems capable of monitoring, preserving evidence, identifying the applicable right and escalating action to the party authorized to enforce it. This is also why the next phase of policy and standards work cannot stop at labeling.

Voice-Swap is contributing to emerging policy, legislative and standards initiatives because existing IP infrastructure tracks works more effectively than it tracks the person, or elements of the person, implicated in AI generation. Through that work, we are exploring how identity, authority, permitted scope and provenance can become part of a more complete infrastructure proposal, informed by real-world applications, industry practice and the needs of the wider voice ecosystem.

Technical exchanges through DDEX and other standards initiatives will be necessary to make those assertions interoperable. Authorization cannot scale if it remains trapped in private contracts or disconnected databases. It must be capable of traveling with the model and its outputs across the commercial ecosystem.

The EU AI Act has established an important transparency floor. It makes synthetic content more identifiable, places obligations on both system providers and professional deployers, and recognizes that disclosure should respond to context and deception risk.

What it does not do is prove whether the identity inside the content was authorized. Transparency is now a legal expectation. Authorization must become the market standard. The legal minimum asks whether AI was involved.

The market we are building must also be able to prove that the human identity involved said yes.

This article was originally published August 27, 2026 on Voice-Swap.ai.

Voiceover model created by Instant Voice Clones utilizing twenty seconds of Pelczynski’s voice input from this interview, with text-to-speech conversion in Voiceover_Studio.

Congratulations to Forms + Shapes client Sureel on their acquisition by Warner Music Group.

As discussed across recent engagements, public forums, and policy work, the first wave of AI licensing deals to date will continue to face pressure – not due to lack of effort, nor because of walled gardens, but because their underlying economic mechanics remain overly complex.

In many cases, that complexity is overcompensating for the absence of a functioning attribution engine capable of doing the heavy lifting, cleanly and efficiently (which simplifies business model design).

As outlined in The AI Era: Building Sustainable AI Business Models for the Music Industry (Parts I & II), standards are formed through thoughtfully designed, simplified commercial models while technology manages complexity (aka doing the heavy lift).

That remains the blueprint.

This acquisition marks the inflection point.

For Sureel’s partners –and the industry at large – this represents a foundational step in monetizing generative AI independent of whether platforms operate as closed or open ecosystems.

As generative content begins to challenge traditional streaming share, the market requires a model that can account for value creation with precision. Attribution share is positioned to meet that need.

What was once a theoretical framework has become part of the industry’s commercial discussion, including recognition in Billboard’s analysis of the challenges surrounding AI licensing and attribution.

The next phase is not conceptual, it’s implementation.

Congratulations to all involved – MP

^{(Article voiceover performed by Pelczynski’s AI voice model via Voice-Swap.ai)}

My train of thought, on a daily basis, circles a recurring focal point--almost a nuclear theme: the inevitable death of my family, my friends, and myself. The impermanence of what we do and build is unavoidable. I’ve touched on this across several recent pieces because it remains the quiet architecture beneath everything else. Why am I writing about this again? Because the purpose-driven nature of our lives, the evolutionary signal we’ve spent millennia reinforcing, demands that we do. As sentient beings we make small decisions that shape our days, our relationships, and, when shared widely enough, our systems, societies, and this fragile experiment we call Earth.

But there is a sibling to that tragic grandeur, an adjacent truth with equal weight: our need for a source of truth.

In a world defined by impermanence, we reach for anchors. We try to construct something that can withstand decay long enough to guide us. Families create them. Institutions formalize them. Technologies attempt to mechanize them. And in every era, the absence of a shared source of truth cracks the foundation of whatever we are trying to build. Without one, purpose becomes performative. Signals distort, systems drift, societies fragment.

The paradox is that our desire for permanence arises precisely because nothing is permanent. And so we return, again and again, to the search for a source of truth--not as a fixed object, but as a stabilizing force. A compass. A shared reference point that lets us navigate the entropy.

And now, more than ever, we need to confront how truth becomes not just a social challenge, but a systems and design challenge in the AI era.

We are now able to infer realities (pun intended) against previous limitation and looming impermanence.

And in addition to the existential, let’s address the economic.

Economics remain the blunt instrument behind most conflict and most order. Whoever understands the mechanics, steers the incentives, or controls the bottlenecks (Straight of Hormuz), often sets the terms of engagement for markets, for culture, and eventually for everyday life (gas prices).

That is why I have always been drawn to the mechanics and economics of industries, to the creative, technological or political. They end up shaping and influencing the other.

I’ve been waiting for a particular convergence in technology and infrastructure before writing this piece. That convergence has now arrived.

Where we come to the realization (at scale) that the voice is one of the most personal, potent, valuable, and singular expressions of a human life. Not just creatively, but now legally, economically and existentially.

That is why, three years ago, as the AI voice market was beginning to break into public view, I chose to go deep with Voice-Swap. Not because voice was fashionable, but because the direction of travel was already clear. Building this company, with colleagues and partners I trust, to meet this moment of convergence years later seemed inevitable to me. We knew there would eventually be a need for trusted infrastructure. The World Intellectual Property Organization has now launched its Technical Exchange Network, bringing together companies working on the real technical challenges at the intersection of AI, music, and copyright. We will be working alongside leadership from Google, Anthropic, SAG-AFTRA, Sureel, WMG, UMG among others. That matters because the next phase of this market will be defined by practical systems.

And we knew that policy, while necessary, would not be enough.

I recently spoke about the foundational concept (MUSICx), that trust must be built into the practical systems of AI’s next phase. That technology if designed properly, can become a kind of caretaker of what is most valuable to a person in the AI era.

The music business is already beginning to learn this lesson. Major deals between generative platforms and record or publishing companies may cover broad swaths of industry rights, but they do not resolve authority over the human voice and likeness embodied in a performance in this newly formed partnership application space. Those rights remain with the individual. The person behind the voice, or their estate, remains the true point of gravity and authority.

Do you see where I am going with this?

That is where this gets real.

We all think about our lives in terms of what we leave behind: our families, our property, our work, our estates, our inheritance. In the AI era, another category now belongs on that list. You must decide who the custodians of your voice are. Who holds it. Who can authorize it. Who can protect it. Who can prove its origin.

Because the world has moved quickly to establish the commercial domain around AI, but much more slowly in building the societal and technical infrastructure needed to preserve human agency inside it.

That work matters.

There are those of us who have spent the last several years focused not only on capability, but on the systems required to protect creativity, identity, and trust while still allowing technology to advance. That is the harder work. It is also the more necessary work.

And it forces a new realization: parts of us may now outlive us in usable form (ie the potential end of impermanence).

What do I mean by that?

My voice will likely live beyond my years on this planet. My voice will be used by those I trust to look after my estate, my identity, governed according to choices I make now. That possibility unsettles people because it challenges something deeply embedded in human life: the old assumption that mortality naturally limits expression. Technology no longer respects that boundary by default (ie challenging the evolutionary signal we’ve spend millennia reinforcing).

Three years in this space has made one thing very clear: when pressure rises, you find out what companies are built for and where their loyalties actually lie.

I know why I am a part of this frontier, and I know where I come from. I know who I have chosen to build with. And I am reassured that many others have trusted us with something incredibly consequential: their livelihoods, their rights, and their singularly human voices.

When a partner trains a voice model with Voice-Swap, they are not just creating a new digital asset. They are securing rights around it, productizing control over it, and establishing the means to authorize its use beyond any single platform. That distinction matters.

From writers, podcasters, educators and other public-facing creators to voice actors such as Lindsay Sheppard and Dave Fennoy, and artists such as Robert Owens and Imogen Heap, there is real pride in seeing this space attract people whose values align around integrity, authorship and care.

And as expected, more and more generative platforms are moving into voice.

Why?

Because voice is valuable terrain, it is hallow ground. It is emotionally powerful, commercially powerful, and deeply tied to identity. Control over voice is not just a product feature. It is a position of authority.

That is why creators need to read the terms carefully.

Many of these platforms do not simply want to let you train a voice model. They want broad rights in connection with that process. Rights that may extend across monetization, promotion, model improvement, and broader platform usage. Even where the language appears reassuring on the surface, the structure often points elsewhere.

They don’t just want to give you the ability to train a voice model, they want to secure broad and sometimes questionable rights as part of the process.

Creators should not treat a generative music platform as the home of their canonical voice asset.

You would never leave your original masters sitting indefinitely on Spotify or another DSP’s servers and call that your archive. You keep the source files somewhere protected, accessible, and under proper control, while distribution happens through appropriate channels.

The same logic now applies to voice.

Your canonical voice model, your source of truth, should live with a rights focused provider built around consent, control, attribution, and licensing. From there, you can decide how that asset is deployed or shared, through APIs, through partners, through campaigns, through collaborations via VST plug-ins, through downstream creative tools. But the core asset should remain anchored somewhere designed to protect it.

That is the difference between infrastructure built to govern an asset and infrastructure built primarily to absorb it.

As I have said for years, as AI infrastructure extends its reach and its functions continue to branch outward, pay attention to who designed their systems with both the economic and existential stakes in mind. Look for those who understood from day one that this was not just about tooling. It was also about answering the custodianship question.

So heed this clearly folks:

Treat your voice — your source of truth — the way you would treat any valuable asset or important body of work. Give access only when you understand how it will be used. Train and host it where representation is controlled. Keep it where you can license it outward on your own terms: to another platform, to a producer, to an agency, to a collaborator, to a future partner not yet known to you. Have it live beyond your years, on your terms with yours left behind.

That future is not theoretical anymore.

Your voice may outlive you. The question is whether it does so on your terms.

All is possible if you are able to house what is yours where you know it will always be. That is only possible if what is yours is housed somewhere you trust, somewhere built to preserve provenance, authority, and control.

And above all, read the terms. Have your lawyer read them. Because once your voice enters the world under someone else’s structure, it becomes much harder to pull back its influence without both the legal and technical means to prove origin.

This decision is larger than most people realize.

Take care of yourself. Take care of the people around you. And take care of the source of truth that may one day speak for you when you no longer can.

Make your own AI voice model

Episode 2 of the World Intellectual Property Organization’s Road to AIII puts the spotlight on something often overlooked: infrastructure isn’t neutral. It shapes the rules of the game.

In my closing conversation with WIPO’s senior policy officer Garrett Levin and fellow voice of reason Dani Deahl, we explored how copyright infrastructure must adapt to the realities of AI. This is not just about technology — it’s about the systems of attribution, licensing, and enforcement that decide who holds leverage and who gets left behind.

This builds on themes from earlier pieces like The AI Era (how generative systems reconfigure value and require new business models) and Tenets of the Music Business (why ownership and leverage are foundational). Together, they frame infrastructure not as plumbing, but as governance.

Three points stood out in the interview:

• AI is a spectrum. 100% machine-generated works should not be treated the same as AI-assisted creativity. The law and its infrastructure must recognize this nuance or risk flattening creative value.

• The “point of inference” is where trust begins. If detection and attribution aren’t built into the moment of creation, every downstream system — from licensing to royalty flows — inherits uncertainty. How will royalties actually make their way to the artist or voice talent?

• Infrastructure is a commercial loop, not just a compliance tool. If it only demonetizes, it fails. If it builds sustainable flows of value for human creatives, it unlocks resilience.

This is why the Artificial Intelligence Infrastructure Initiative (AIII) matters. The decisions we make now will set the conditions for whether AI destabilizes or empowers the music and creative industries.

Watch Episode 2 (our segment begins at ~48 minutes): https://www.wipo.int/meetings/en/details.jsp?meeting_id=88977

Learn more about WIPO’s AIII: https://www.wipo.int/meetings/en/2025/ai-infrastructure-interchange.html

The game of life requires many things.

To challenge the status quo. To humble your intentions while pursuing higher purpose. To keep your mind open to the world you live in—and the one still foreign to you.

Above all, a life worth living requires that we never stop learning.

The music industry is no different. It is a system perpetually reeling from its own lessons, desperately in need of renewal. The industry has always relied on agents of change—those willing to pierce the veil of convention, to risk re-shaping its design. But the question remains: where does meaningful change actually occur?

The ethos behind Forms + Shapes comes from one of my own lyrics:

“As forms take shape, an era awaits.”

We define ourselves in eras because eras help us make sense of time: they frame what came before, where we stand now, and how we might seize the future. And yet the music business too often gets stuck between eras—haunted by its past, clinging to legacy structures, even as new technologies accelerate far beyond its grasp.

Right now, we stand at an inflection point. Traditional models are exhausted, technological disruption is unavoidable, and the stakes—financial, cultural, even existential—have never felt higher.

This is the moment for tenets.

Tension as Design, Not Desperation

If there is one constant across every cycle of disruption, it is tension. We see it in sex, in sport, in love, in war. But music—music is different. It does not require domination, though it can reflect it. It does not demand tension, though it thrives within it.

Music is tension made resonant. It is conflict, release, and renewal rendered audible.

When I published frameworks for AI music business models (The AI Era series), I did so because I could feel the tension crystallizing into desperation. CEOs telling me, “my team hasn’t built a model yet.” Boards operating on half-measures. The risk wasn’t just poor strategy, it was the design of desperation itself—repeating the mistakes of UGC-era chaos, this time at AI’s accelerated scale.

Better to share the playbook and let others “steal my homework” than to let tension calcify into panic. Because tension—unlike desperation—can be designed for.

This isn’t just an industry problem. It’s a human problem. Perhaps even the grand thesis of humanity itself: the perpetual state of tension.

The question is whether we treat tension as a destructive force, or as the driver of progress.

Tenets for an Era of Flux

The human experiment is cyclical: life, art, and technology are forever interdependent.

As I argued in A Beautiful Mess, systems are never neat. They are layered, overlapping, often contradictory. Progress doesn’t come from eliminating the mess but from navigating it—treating friction not as failure, but as feedback. The music business has always been shaped by this reality: strategy emerging not from order, but from the way we move through complexity.

To navigate the next era, we need recurring guideposts. I call them tenets:

• Ownership — The Foundation

• Boundaries — The Discipline

• Freedom — The Aspiration

Ownership — The Foundation

Ownership is the hinge upon which every outcome in this business swings. It is not just a legal condition; it is a state of clarity about who controls what, and why.

For executives and founders, ownership means equity and skin in the game. Without it, decisions become transactional rather than transformational, incentives collapse into short-term survival. A company without owners is a bureaucracy; a company with owners is a living organism, where outcomes matter because they are tied directly to those who shape them. Foundational architects of companies must hold equity not simply as wealth, but as responsibility.

For artists and songwriters, ownership means doing everything possible to hold onto masters, rights, and the catalog they are building. Not because ownership is sacred for its own sake, but because it provides leverage. With leverage comes the ability to choose: when to license, when to sell, when to hold, when to bet on yourself.

For voice talent—singers, actors, creators—ownership now extends into new domains. AI makes the human voice a tradable, replicable asset class. That means the only path to sustainable agency is to create and control your own certified voice models. Owning the AI version of yourself is no different than owning your master recordings: it is the foundation of your future licensing power.

It also means building ecosystems around ownership—partnering with distribution companies that are innovative and aligned, able to evolve with artists and rights-holders, and designed to extend ownership and control into the marketplace without losing integrity.

But ownership also goes deeper than contracts and capital. To own your intentions is to have clarity in decision-making. If you don’t own your intentions, someone else will redirect them. Strategic clarity—the “why” behind every move—is the most overlooked form of ownership in this industry.

And finally, ownership is cumulative. Each new work, each deal, each idea adds to a growing portfolio. No one can predict which piece will break through. But long-term success comes from continually expanding the body of work, building a portfolio that ensures at least part of it will resonate. Ownership of your body of work is not just holding rights; it is the strategy of persistence.

Ownership is the only true hedge against desperation. It is the foundation. Without it, the other tenets—boundaries and freedom—cannot exist.

Boundaries — The Discipline

Boundary-setting is fundamental to interaction. To set a boundary is to define what is sacred, to claim what cannot be encroached upon. And when we loosen or forgo boundaries, we send an equally powerful message: that we are willing to experiment, to work with forces outside our comfort zone, to open ourselves up to transformation.

For artists, boundaries mean protecting creative identity against commodification. It is the choice to draw lines with labels, platforms, or even fans—deciding how much access is too much.

For executives and founders, boundaries mean discipline. Knowing which deals to refuse, which growth opportunities to delay, which distractions to block. Without boundaries, companies drift into mission creep, pursuing growth for growth’s sake rather than strategy with integrity.

For voice talent, boundaries are the guardrails on AI licensing. Owning your model is not enough; you must also set the rules of its use—contexts, conditions, ethics. A voice without boundaries becomes a tool of exploitation.

For fans, boundaries define intimacy and community. In the fan economy, boundaries can be loosened to allow fan-to-fan communities to create value with an artist’s IP. But if boundaries collapse entirely, parasocial overload sets in, turning connection into intrusion.

For the individual, boundaries mean protecting attention, time, and energy. Without them, burnout becomes inevitable. Creative work requires both openness and protection; boundary-setting is how we preserve the space to create.

Boundaries are the discipline, the guardrails that keep ownership intact.

Freedom — The Aspiration

Freedom is the aspiration toward which all ownership and boundary-setting is directed. It is the calm power of self-authorship, self-discovery, self-awareness—the ability to choose how you live, work, and create.

For artists, freedom is the dream of making without constraint: to record and release in ways that reflect their vision, not the coercion of platform algorithms or contract obligations, without economic coercion.

For executives and founders, freedom is the ability to innovate within and outside legacy structures. To chart new markets, build new models, design without inherited chains of command. But this freedom only exists if ownership and boundaries are secured first.

For voice talent, freedom is deciding how and when their AI voice is used. It is the autonomy to refuse, the agency to control, the assurance that technology extends their power rather than extracts it.

For fans, freedom means choosing how to engage, whether in direct support, in peer-to-peer exchange, or in community building, without being locked into exploitative platform mechanics that siphon value away from culture.

For the individual, freedom is time-richness: the space to rest, reflect, recalibrate, and live well. Success without freedom is another form of captivity.

Freedom is the aspiration. Ownership builds the foundation. Boundaries provide the discipline. Freedom is the horizon toward which they point.

Ownership = foundation.
Boundaries = discipline.
Freedom = aspiration.

Together, they form a systemic triad.

This triad is not unique to music, but in music it is magnified—because music has always been where human tension becomes audible. If we design for ownership, guard it with boundaries, and direct it toward freedom, the industry can renew itself. If we don’t, desperation will design for us.

But orientation alone is not enough. We live in motion—in choices, actions, and recalibrations that unfold over time. To make sense of those choices, I’ve come to rely on cycles: four recurring stages that apply as much to launching a business as they do to releasing a body of work or recalibrating your personal life.

Cycles are how we visualize decision-making: strategic, practical, or life-affirming. They help us see not just where we are, but what choices are available, and how the tenets—ownership, boundaries, freedom—can guide us through each turn.

Cycles, Renewal, Legacy

Every artist and company must contend with legacy. As I wrote in What I Carry, What I Create and Legacy and the AI Proposition, legacy is not just what you own or what you leave behind—it is the pattern of choices you set in motion, the cycles you repeat or refuse to repeat.

Over thirty years I’ve distilled these cycles into four stages:

• Foundation — Build pillars of strength where stability can be sustained.

• Frontier — Push into untamed territory; confront systemic neglect.

• Recovery — Acknowledge loss as part of progress; reset strategically.

• Refinement — Sharpen what has been built; cultivate, invest, prepare.

Then the cycle renews. At times, cycles reset unexpectedly at any stage—back to the need for stability—because life, in all its unpredictability, rarely unfolds in straight lines.

On a more macro level, projects and lives move through sub-cycles, while an overarching grand cycle defines eras. I often see my own work through this lens: asking where I am in the grand cycle, what I’m building, and what stage I must lean into. The only thing that truly disturbs this focused thinking is when survival itself is shaken.

Survival is inherent—we endure because we must. But freedom is chosen. It is the highest ambition: to live and create with clarity, to design with purpose, to find dignity in the balance between what we own, what we protect, and what we release.

And that is what the tenets return us to—no matter where we are in the cycle.

So it goes...

The music industry has always been a proving ground for human design under pressure: from the failures of streaming economics (WT$ Is Streaming Economics), to the messy carve-outs that created today’s fractured rights landscape (The Big Carve Out), to the looming AI economy that tests whether we can build sustainable frameworks in real time (The AI Era series).

It is a beautiful mess. It is a story of legacy, cycles, invention, and renewal. And it is ours to shape—if we heed the tenets, resist desperation, and design for freedom.

Like any business, music rewards those who’ve sat on both sides of the glass.

Not just the executive chair—but the artist’s chair. The one with no feedback loop. The one where you press “upload” and release something into the void. And the most informed are often those who’ve lived the cycle, not just modeled it.

There are people who speak in hypotheticals. And then there are those who speak from memory.

For those unaware, I’ve been writing and recording music since my teens, across a few decades now. One of my most personal records is one I never released. It’s called Tales From Lonely Mountain, and I wrote it quietly in 2010, just me and a Rhodes piano, over four nights.

The first song to be shared is Carousel Blues, recorded in a single late-night take during that same stretch. For the past 15 years, I’ve returned to this record more than most others I love. It’s been my private compass. A therapy piece. A friend recently said what I’d quietly avoided: "Why haven’t you released this?"

So here we are.

Some context for the industry-minded: during one of my parallel lives, I had a co-publishing deal with BMG from 2011 to 2015. These songs, including Carousel Blues, were never officially released, but they lived quietly in sync libraries, occasionally surfacing. One clip even made it into a Katie Couric segment on weed policy. (Truly a different era.)

Now I own them again. And I have nothing to lose. It’s been a while since the last record. And these songs, long tucked away, still feel alive.

This is another node in a larger thread I’ve been weaving—about ownership, authorship, the quiet power of creative control, and what it means to make and release work in a landscape shaped by performative momentum more than meaning. More on that soon.

Until then, here’s Carousel Blues. One of my favorites. Out now, from the forthcoming record Tales From Lonely Mountain. Streaming wherever you need it to be.

Apple Music
SoundCloud
Spotify
Amazon Music
YouTube Music
Bandcamp

[PRESS RELEASE]

Mokotow releases “Carousel Blues,” the first single from Tales From Lonely Mountain
Out July 18 via Heeled & Heavy Records

“Carousel Blues” was recorded in a single sitting on a Rhodes piano in 2010. The first single from Tales From Lonely Mountain, a minimalist, ambient record written over four nights and left untouched for over a decade, it captures the undercurrent of solitude, seasonal change, and emotional stillness.

Raw and reflective, the track serves as an entry point into a record long kept private. For fans of Max Richter, Ólafur Arnalds, and cinematic quietude—for when presence matters more than pace.

Cover photography by . Visuals by Dan Hayes.
Mixed by Jeff Berner. Mastered by Kim Rosen.

Listen now on your preferred platform.

Let’s get right to it—music streaming’s value as a revenue generator is increasingly at risk. The introduction of play threshold mechanics into its economic structure only underscores this fragility, exposing the fault lines of a model that was never built to scale equitably.

What Is a Play Threshold?

Over the past two years, streaming platforms have implemented performance thresholds to filter out low-engagement tracks, redirecting payouts toward music deemed "actively" streamed. It’s a mechanism that claims to weed out artificial or incidental plays, but in reality, it’s more of an economic reflex—a desperate reaction to a structural problem, not a measured solution.

While the 1,000-play threshold was initially introduced as a way to curb fraudulent streaming activity and redirect value toward music with widespread engagement, its application has grown beyond its original scope. Major rightsholders—most notably Universal Music Group—publicly advocated for its implementation, positioning it as a corrective step toward a fairer, cleaner streaming economy.

But like many stopgap solutions in music economics, a measure intended to restore order has ended up creating its own form of distortion.

The truth is, these thresholds are less about ensuring fairness and more about maintaining profitability. The platforms are fumbling for a stopgap to reinforce a fractured revenue model.

The Core Issue

The problem is foundational: The pro-rata model on which streaming platforms were built is inherently flawed. As more music floods the platforms, earned royalties become increasingly diluted. The mathematical reality is blunt: unless the model is fundamentally restructured, growth for some can only come through exclusion or elimination of others.

What this begins to resemble is a turf war—a quiet economic détente where power consolidates under a few dominant players, shoring up profitability by managing scarcity.

Market Realities

The numbers are telling.

Independent labels—those outside the major label system—outpaced the growth of majors in 2024, growing 8.4% compared to 5.4%.

But streaming revenue growth overall sharply slowed, climbing just 6.2% in 2024 compared to 10.3% in 2023.

Most telling of all, self-releasing artists (via distributors) grew their revenue by 4.7%, while their numbers expanded more than three and a half times faster than their revenue.

(Statistics from MIDiA Research’s Recorded Music Market Share 2024 report)

This suggests that the fastest-growing segment of the market is not the traditional label system but rather self-releasing artists and independent labels. And these are precisely the players being most adversely affected by royalty thresholds.

What Does This Mean Today?

The platforms implementing royalty thresholds are systematically carving out revenue streams from the very demographic that’s driving the most dynamic growth in the industry. When segments of a catalog fall below the threshold, they effectively cease to exist from a revenue standpoint, regardless of their cultural or creative value.

While some successful self-releasing artists and labels may not be affected in totality, individual tracks within their catalogs are being demonetized—even within otherwise "above threshold" albums.

The unspoken question remains: What happens when these thresholds continue to rise, deal after deal, renewal after renewal?

The Hard Truth About Music Licensing

Negotiating numbers, percentages, and calculation-based provisions is a manageable process. Metrics can be benchmarked and traded for something of equivalent value.

But when it comes to negotiating out an economic lever like the play threshold, that’s a different story. Once it’s embedded in licensing deals, removing it requires significant concessions or alternative mechanisms to compensate for perceived loss. As this becomes the standard ask to remedy a broken model, let’s call it what it is: the big carve out.

The Iceberg Principle: Your Plays In The Dark

While developing economic frameworks during my time at SoundCloud, I coined the "Iceberg Principle" as a means to illustrate ebb and flow dynamics. Reviewing global economics and managing monthly content cost evaluations, this principle became a straightforward way to illustrate how rightsholders and platforms are impacted by dilution factors, advertising revenue shifts, territory-specific dynamics, or fluctuations in a given area’s market share.

The threshold floor—or ceiling—is a contractually predetermined gate to monetization—a foreign economic lever imposed on organic market share dynamics. It’s designed to prevent dilution in theory.

But as dilution accelerates from multiple sources, thresholds will inevitably be redefined and raised. From a 1,000-play threshold to 10,000, 25,000, or higher.

The hard truth? More and more music will fall below the "water line" into non-monetization.

Picture your catalog, your label, or your body of work as an iceberg. The waterline symbolizes the royalty play count threshold—everything below it is rendered invisible from a revenue standpoint.

As years pass, as competitor growth from new technologies and fraudulent gaming of outdated systems intensifies, thresholds will be pushed higher and higher. The result? Increasing portions of catalogs submerge below the line of financial legitimacy.

Culture Exits

So what happens when culture is forced to exit mainstream music streaming platforms because it can’t generate revenue or value despite genuine engagement?

Culture will adapt. And while direct-to-fan options are emerging, the convenience of on-demand music remains a fundamental consumer need. But as platforms continue to raise thresholds, the accessibility and opportunity for emerging artists will continue to erode.

New Entrants

The introduction of play thresholds is a tacit acknowledgment that the streaming payout model is broken. But rather than addressing the core issue, the solution has been to add layers of ineligibility mechanics on top of an already fraying economic structure.

Instead of propping up a broken model, the industry should be pivoting towards user-centric frameworks that prioritize where actual attention is placed. SoundCloud launched its user-centric model in 2021, demonstrating that alternative models are not only feasible but essential.

New entrants like Vocana are setting up indie-only platforms that aim to build a "haven" market for emerging artists—one grounded in user-centric economics and social connectivity, designed to accommodate creativity rather than impose punitive economic gates.

Bandcamp, while long held as a favorite for independent artists due to its direct-to-fan commerce model and reliable download revenue, has never fully bridged the gap of offering on-demand streaming at scale. That gap remains open for new entrants with the foresight to provide both fairer economics and the convenience listeners expect.

The Closing Cut

The "Big Carve Out" isn’t inevitable. But if the industry wants to avoid it, it needs to confront the foundational flaws of its current model rather than continue shoring up a crumbling wall. Because the deeper truth here is this: systems designed to keep value out will ultimately keep creativity out. And a model that walls off creativity will ultimately sign its own death warrant.

I come from a place where identity is something you carve out, not something handed to you, from a lineage where music wasn’t just expression—it was survival.

My parents, Józef Pełczyński and Danuta Bogaczyk, were working musicians who fled Cold War tyranny, carrying only their instruments, their voices, and the will to carve out a future on their own terms. I grew up watching them turn sound into sustenance, watching music pay the bills, cross borders, and fight back against a system that sought to silence people like them.

Their struggle—balancing art with survival—was my first real education. It taught me that music has weight. It isn’t just something you make; it’s something you wield. I was immersed in an environment where music wasn’t just art; it was a lifeline. It wasn’t just entertainment; it was a language, a survival tool, a way to process what couldn’t be spoken outright.

I understood early that creativity is a form of defiance, a way to push forward when the odds are against you. My parents' courageous path as working musicians and immigrants shaped my understanding of what it meant to fight for freedom and expression. That idea became the foundation of how I see the world: there’s no passive existence—only what you build, what you fight for, and what you refuse to compromise.

Growing up, I lived between two worlds—one rooted in my parents’ sacrifices and traditions, the other pulling toward reinvention. One world spoke of discipline and survival; the other of possibility and adaptation. I had to learn how to switch fluently between Polish and English—not just in language, but in mindset—to navigate the insulated immigrant community and the melting pot of American culture. Each carried its own expectations, and I had to reconcile both. My upbringing was an intricate mix of discipline and defiance. I still obsess over the concept of duality—how opposing forces coexist, how tension fuels transformation, and how symbolism reveals truth through contrast, distilling meaning from the collision of forces. I wasn’t handed a map for how to navigate that duality, so I built my own. I gravitated toward sound, philosophy, and storytelling—anything that made sense of the chaos.

That ethos—of questioning, refining, and refusing to take things at face value—defines everything I do. Whether it’s music, strategy, or anything I put my name on, the throughline is the same: excellence without pretense, depth without excess, and power without noise.

I’m drawn to the pursuit of calm power—the kind that isn’t performative but undeniable. I don’t separate my work from my identity because, for me, they are the same thing. Where I come from is in everything I do.

There’s a thread I’ve been pulling at for years—a thread that runs through everything I do, think, and create. It’s about existence, about what it means to be human across an insurmountable span of time. But more than that, it's about the individual—the self navigating a reality shaped by systems, instincts, and the interplay of the seen and unseen forces that drive us.

Lately, I’ve been circling around this idea with more clarity than ever before. Maybe it’s a natural progression of thought, or maybe it’s just the way my mind works—how I wake up some days locked onto an idea, obsessing over it, sensing the shape of its interconnectedness before I can fully articulate it. That feeling of knowing without knowing. Of instinct paired with reason. The same way we all move through life: milestone to milestone, age to age, absorbing, reacting, surviving, creating.

What I’ve realized is that we don’t just exist in systems. We exist as part of them, constantly influencing and being influenced in return. The strategic mind, the creative mind, the emotional mind, the economic mind—all of them operating within different frameworks, different rule sets, but all fundamentally connected. We shift between them instinctively, without always realizing how much they shape us. And yet, we rarely take the time to truly map them out—to visualize these systems, their intersections, and how we can move within them with greater awareness.

This piece is about that visualization. It’s a thought exercise, a tool for understanding. It's an honest attempt at simplifying the complexity that is the collective 'beautiful mess' we call life.

If we can see the forces shaping our lives, we can better navigate them. And if we can navigate them well, we can create ripples—impacting not just our own lives but those of our families, our communities, and even the generations that follow.

The Systems We Live In

To exist in the modern world is to be embedded in overlapping, interwoven systems. Some are natural, dictated by the laws of physics and biology. Others are human-made, constructed over centuries of civilization. And then there are the internal systems—the mental frameworks, the ways we process and interact with reality itself.

We can start by breaking these into broad categories:

The Natural System

The foundation. The world as it is without us—the ecosystems, the climate, the physical laws that govern existence. It’s indifferent to human ambition, yet it dictates our survival. No economy, no political structure, no philosophical construct can override the reality of water, air, and time itself.

We exist within nature, but modern life often blinds us to this fact. We insulate ourselves with cities, industries, and digital worlds, but we are still biological beings operating on biological imperatives. To ignore this system is to fight against reality. To understand it is to recognize the cycles of growth, decay, resilience, and adaptation—lessons that apply far beyond nature itself.

The Societal System

Civilization, law, economy, culture. The frameworks humans have built to organize themselves. It’s a machine running on invisible rules—some codified, others unspoken.

• Economic Systems define how we trade time, labor, and value.

• Political Systems dictate power, governance, and collective decision-making.

• Cultural Systems shape identity, belonging, and the stories we tell ourselves about who we are.

This is where most of us spend the majority of our mental energy. Navigating status, ambition, security, relationships. It’s also where strategy plays its most obvious role—where decisions have measurable consequences, where power structures demand awareness.

But the mistake many people make is thinking this system is everything—that success here equals success in life. That’s a limited view, to put it kindly.

While society provides structure, it is not the sole determinant of experience. Our interaction with it is further shaped by the technological and structural frameworks that define access, movement, and capability.

Technological and Civilized Structural Systems

In the modern world, much of our experience is mediated by Technological Systems and Civilized Structural Systems—the infrastructures that shape what is possible, accessible, and sustainable in daily life. Technology amplifies human capability, from digital networks that connect us across vast distances to the energy grids that sustain our cities. Meanwhile, civilized structures ensure the fundamentals of existence—access to food, water, transportation, and the broader logistical frameworks that make a functioning society possible.

These systems are often taken for granted until they fail. A power outage, a supply chain breakdown, a cyberattack—these moments expose just how dependent we are on intricate, interwoven systems that operate largely out of sight. But beyond their practical necessity, these structures also influence something deeper: the way we think and perceive the world. The digital landscape shapes our sense of reality, algorithmic feeds direct our attention, and urban design dictates how we move and interact.

While these systems construct the external conditions of our lives, they do not dictate our internal responses. How we experience power, success, culture, and relationships is not just determined by these infrastructures but by how our minds interpret and navigate them. This is where the Psychological System takes over—not as something separate from society, but as the internal mechanism through which we make sense of the world and find our place within it.

The Psychological System

If external systems shape the world around us, the psychological system is how we experience it. It’s where instinct meets awareness, where suffering meets resilience, where strategy meets emotion. We don’t just navigate life—we interpret it, layer by layer, through thought, feeling and meaning.

The Internal Architecture

Some of this is raw instinct—the survival-driven part of the mind that reacts before it reasons. But beyond that, we shape our reality through cognition, emotion, and philosophy. These are not separate categories but interwoven threads, constantly influencing and reshaping each other. To understand this system is to understand the self—why we think the way we do, why we feel what we feel, and how we construct meaning from existence itself.

• Cognitive frameworks determine how we problem-solve, learn, and adapt.

• Emotional frameworks govern how we react, connect, and find fulfillment.

• Philosophical and ethical systems shape our understanding of purpose, morality, and existence itself.

Thought, Strategy, and Adaptation

Cognition is the internal rulebook—the way we structure reality in our minds. It determines how we problem-solve, learn, and adapt to change. But thinking isn’t just about intelligence; it’s about mental flexibility, the ability to see beyond surface-level reactions and into deeper cause-and-effect relationships.

Our minds are constantly looking for patterns—in behavior, in opportunity, in the chaos of life. Some patterns are useful, guiding us toward better decisions. Others are misleading, illusions that trap us in cycles of misjudgment. Knowing the difference is what separates clarity from confusion.

Then there’s the fundamental divide: reactive vs. strategic thinking. Some people move through life purely by reaction—responding only to what’s immediately in front of them, making choices out of impulse or necessity. Others zoom out, playing the long game, seeing how today’s actions ripple into tomorrow’s reality. Strategic thinking doesn’t mean being emotionless or hyper-rational; it means being deliberate, knowing when to act and when to wait, when to push forward and when to reposition.

At its highest level, cognitive awareness allows us to think beyond the self—not just understanding how we operate, but how others do as well. It’s the difference between being controlled by a system and knowing how to move within it. This is what enables leadership, foresight, and the ability to play the game rather than just be played by it.

Living, Suffering, and the Depth of Experience

If cognition is the structure, emotion is the substance. It’s what makes life heavy, intense, meaningful. It’s what fuels passion, connection, and perseverance—but it’s also where suffering takes hold.

And suffering is inescapable. No system—economic, political, personal—can shield us from loss, failure, uncertainty, or isolation. These aren’t anomalies; they are fundamental aspects of existence. The question isn’t how to avoid suffering, but how to carry it without being broken by it.

There’s a reason why people who have been through real hardship often have a clarity that others lack. When you endure suffering and come out on the other side, you don’t just survive it—you learn from it. You see the things that matter more sharply, the illusions that once seemed important now stripped away. But this only happens if you allow yourself to face suffering rather than numb yourself to it.

There is also a false perception that emotional strength means feeling nothing—that the only way to be unshaken by the world is to detach completely. But the strongest individuals are not those who suppress emotion; they are those who feel deeply while remaining in control of their response. They allow pain to exist without letting it consume them. They experience joy without being afraid of its impermanence.

At some point, mere survival is no longer enough. The mind starts searching for something more. Some find it in work, others in art, relationships, philosophical exploration. Some go their entire lives never finding it at all. But a life without meaning is a slow psychological decay. If you don’t define what matters to you, the world will do it for you—and you may not like the answer.

The Challenge

This is where the greatest potential for self-mastery exists. Because while we can’t fully control the external systems, we can train the mind. We can become aware of our own conditioning, biases and limitations. We can sharpen our ability to think strategically while remaining emotionally present. I call this calm power.

And this is where interconnectivity becomes key. The best minds—the ones who shape history, who inspire others, who operate at the highest levels—aren’t just strategic or just emotional or just creative. They’re all of these things at once, moving seamlessly between frameworks, seeing the full system at play, even if at times through a haze.

The greater challenge is not just navigating these systems with awareness but challenging them—and doing so with intent. The best minds do not merely endure life’s absurdity; they engage with it, bending disorder into meaning.

Yet despite the disorder, true and lasting agency belongs to those who uphold their humanity, their decency, and their ability to empathize—because in the end, it is not just intelligence or strategy that defines impact, but the character to wield them wisely.

The Release

There is also the need for release—a pressure valve for the weight of awareness. If calm power is the art of moving through systems with clarity, then release is the act of letting the weight of it all escape. Not everything can or should be held in place. Some things need to be expressed, unraveled, let go. Mastery doesn’t mean suppression—it means knowing when to hold and when to let go.

Artists of different crafts—writers, musicians, painters, filmmakers—are often skilled at channeling this release, documenting it for others to share, feel, and unburden themselves through it. Art, in all its forms, isn’t just about creation; it’s about externalizing the internal, offering a way to confront, process, and sometimes even be free from what otherwise might stay trapped inside. Art is also a way of moving through systems, not just escaping them.

Both calm power and release are necessary forces—one sharpens the mind, the other allows the soul to breathe. To navigate systems without being crushed by them, we need both.

Seeing the System, Playing the Game

So how do we use this awareness? How do we navigate?

• Recognize Your Dependencies – Identify where you’re most locked into external systems. Where are you beholden to forces beyond your control? What rules do you unconsciously follow?

• Map Your Strengths and Weaknesses – Where do you thrive? Where do you struggle? Which systems come naturally to you, and which require more effort?

• Develop Multi-System Thinking – Don’t just operate in one mode. Learn to shift between strategic, creative, and emotional thinking fluidly. Train yourself to see the connections between different systems.

• Balance Instinct with Awareness – Some navigation is intuitive. Some requires conscious effort. The key is to refine both—trust your instincts, but also refine your ability to see beyond them.

• Create Ripples Beyond Yourself – Every system we interact with extends beyond us. The choices we make don’t just shape our own lives; they influence the people around us. If you understand how systems work, you can make decisions that create positive, lasting impact—for your family, your community, and the future beyond your own lifetime.

Final Thought: The Time Scale of Existence

We are momentary. Each of us a fragment of time, passing through systems that existed long before us and will exist long after. But the choices we make matter—not just in the immediate, but in ways we may never fully see.

None of these systems exist in isolation. Thought fuels emotion. Emotion shapes philosophy. Philosophy determines action. Action feeds back into thought.

And for those who would rather meet these systems with defiance or attempt to escape—perhaps the ultimate act of rebellion is not in rejecting the systems, but in moving through them with clarity—finding purpose where none is promised, choosing meaning where none is given.

To live well is to recognize both the smallness and significance of the self. To move through systems with awareness. To balance logic with emotion, instinct with strategy, the seen with the unseen.

That’s the thought I woke up obsessed with today. Maybe it’ll be a different one tomorrow. But I know—somewhere, somehow—it’ll all be connected.

AI-generated music is projected to generate a staggering $42 billion by 2028, according to CISAC’s latest study. With the rapid acceleration of AI platforms transforming the creative landscape, the music industry faces a critical moment to establish fair, scalable economic frameworks. Without proactive design, rightsholders risk being left behind in an era where technology outpaces traditional licensing structures.

In “Part 1” of this series, we introduced the foundational concept of sustainable business models for the AI era. Now, in “Part 2”, we take a closer look at the economic mechanics underpinning the Attribution Share business model. This framework offers the music industry a viable way forward, balancing innovation with equitable compensation. By introducing tiered attribution share models and leveraging familiar revenue structures, the model ensures rightsholders are positioned to thrive alongside AI platforms in this rapidly evolving landscape.

At the heart of this economic proposal lies an unprecedented reset opportunity for the industry—one that can finally rebalance the split of remuneration between recorded and publishing rightsholders. Unlike traditional streaming, where consumption volume dictated revenue allocation, AI-generated music shifts the focus to influence. The Attribution Share model recognizes the value of those who shape AI generative outputs, positioning publishers and songwriters to claim a greater share of the Industry Share (%). For publishers, this marks a chance to reverse the traditional market share revenue splits that record labels negotiated with platforms like Spotify and Apple Music during the early days of streaming.

This reset isn't just an evolution of the business model—it’s a transformation that redefines how value is attributed, aligning financial rewards with creative contribution.

Let’s dive into how Attribution Share works and why it holds the key to securing a fairer and more balanced future for the music industry in the AI era.

Attribution Share Calculation Models for AI Platforms

This comprehensive framework introduces tiered attribution share models that reflect familiar industry structures while accounting for the unique revenue potential of AI platforms. With real-time reporting, synthetic data protections, and premium catalog opportunities, it ensures equitable compensation for rights holders and dynamic growth for AI platforms.

NOTE: The provisional inputs have been purposefully oversimplified to illustrate the calculation mechanics.

The Mechanics

1. Transactional Model (Pay-per-Credit/Token):

Users purchase tokens to generate music, and the platform allocates a percentage (X%) of transaction revenues to rights holders. Attribution Share (Z$) is calculated based on the collective* influence (Y%) of a rightsholder’s assets.

Calculation Example:

• Per Unit Transaction Value: $0.10

• Number of Transactions (W#): 50,000,000

• Industry Share (X%): 60%

• Rightsholder A’s Influence (Y%): 30%

• Revenue Pool for Rights Holders:

• Attribution Share (Z$):

Proposed Formula:

2. Subscription Model (Recurring Fee):

The platform shares a percentage (X%) of total subscription revenues with rightsholders, distributed based on their collective* influence (Y%) within the generated outputs for the billing period.

Calculation Example:

• Monthly Subscription Fee per User: $10

• Total Subscription Revenue: $1,000,000

• Industry Share (X%): 65%

• Rightsholder A’s Influence (Y%): 25%

• Revenue Pool for Rights Holders:

• Attribution Share (Z$):

Proposed Formula:

3. Hybrid Model (Combination of Transactional + Subscription):

This model combines token-based and subscription revenues. Attribution Shares are calculated separately for each revenue stream before summing the totals.

Calculation Example:

• Total Subscription Revenue: $800,000

• Total Transactional Revenue: $200,000

• Industry Share (X%): 70%

• Rightsholder A’s Influence (Y%):

• 20% on Subscription-based Outputs

  • 35% on Transactional Outputs

Attribution Share Calculation:

1. Subscription Pool:

2. Transactional Pool:

3. Total Attribution Share (Z$):

Sample Attribution Share Table & Definitions

*collective influence:

the compounded total of attributed influence, by "stacking" every individual output's share of influence that attributes to a given rightsholder (ie similar aggregation mechanic to user-centric streaming model's aggregation of each individual's listener share).

Alternative Calculation Provisions (Modeled After Streaming Language):

• Monthly Reporting Period:
  Attribution Shares (Z$) are calculated and paid monthly, ensuring rightsholders receive timely compensation based on real-time influence tracking.

• Influence Caps:

• Economic: If a rightsholder’s influence (Y%) exceeds 50% of platform outputs during a reporting period, payout adjustments may apply to prevent market distortion, mirroring stream caps used by traditional streaming services.

  • Technical: Influence caps may be applied at the individual output generation level.

  • Example: for select assets from premium artist(s) or 'darling' catalog(s) – no AI platform output can be generated from select assets with an influence of 40% or higher due to 'substitutional' risk.

• Synthetic Data Safeguards:
  If synthetic datasets are used in AI model training, Attribution Shares will factor in estimated proportional influence, ensuring rightsholders remain compensated even when datasets extend beyond directly licensed material.

• Premium Tiering for High-Value Catalogs:
  AI platforms and rightsholders may introduce premium pricing for prompts that request specific high-value artists, catalogues, or repertoire. These tiered asset classes are defined through artist and rightsholder consent, allowing for differentiated pricing structures.
  Example:

• Standard Prompt: Generates outputs based on non-premium catalog at regular token cost (e.g., $5/credit).

  • Premium Prompt: Requests specific artists or songs from a premium catalog, charging users at a higher token rate (e.g., $15/credit).

  • Attribution Share Impact:The premium prompt revenue is distributed with the same formula, but influence within the premium catalog is weighted more heavily. For instance, a 20% influence on a premium output could generate a higher payout compared to standard catalog outputs due to the higher per-credit price.

• Proposed Formula Adjustment for Premium Assets:

This ensures rights holders of high-value catalogs receive proportionate remuneration reflecting their asset class’s market significance, while platforms benefit from dynamic monetization opportunities.

Conclusion: A New Chapter in Music's Value Proposition

The advent of AI music generation marks a profound expansion in the value of music, creating opportunities to complement the advancements of streaming while unlocking entirely new commercial frontiers.

By shifting the focus from consumption to influence, the Attribution Share model ensures that those who shape generative AI outputs are fairly and dynamically compensated. This framework doesn’t replace the economics of streaming; instead, it operates alongside it as a distinct model, addressing new opportunities and challenges presented by generative AI.

However, the stakes are high. The CISAC report warns that under current conditions, 24% of music creators’ revenues could be at risk by 2028, representing a cumulative loss of $10.5 billion over the next five years and an annual loss of $4.2 billion in 2028. These figures highlight the critical need for action: without the adoption of Attribution Share or similar frameworks, the rapid penetration of generative AI outputs could destabilize the revenue foundation that music creators rely on.

Together, Parts 1 and 2 of this series illustrate a vision for the future: preserving the progress made through streaming while addressing the existential risks posed by unchecked AI development. Attribution Share introduces scalable economic mechanics that align the music industry with the evolving technological landscape, ensuring that value expands proportionally with innovation.

This proposal is not a definitive solution—it is a foundational framework designed to empower stakeholders, economics experts, and licensing professionals to shape it further. It serves as an open blueprint, inviting the industry’s brightest minds to adapt it to their needs, building confidence in what can often feel like an uncertain path forward. The persistent question voiced by senior leaders—"But what is the business model?"—now has an answer, one grounded in fairness, scalability, and shared opportunity.

The projected $42 billion AI music market by 2028 is not just a number—it represents an unprecedented moment to redefine music’s worth in the digital age. Dr. Tamay Aykut of Sureel.ai puts it best, "the music industry needs accountability, not illusions." With the right structures in place, this evolution ensures that creators, publishers, labels, and platforms alike can thrive. The future of music is not confined to what has been; it is enriched by what can be.

Let’s transform uncertainty into opportunity and expand music’s value for generations to come.

[“Part 1” The AI Era: Building Sustainable AI Business Models for the Music Industry — originally published on October 29, 2024 below]

The music industry stands at a pivotal crossroads. As AI technology accelerates and generative platforms emerge, the industry is grappling with both opportunity and uncertainty. Without a framework guiding this transition, the risk is clear: we could repeat the same missteps that plagued the early days of music streaming—but on an exponential scale.

Despite years of warnings about the flaws of streaming's pro-rata model, the industry failed to course-correct, resulting in fragmentation, diminished value, and a steady erosion of creator compensation. Now, with AI as the next transformative force, we cannot afford to build on a weak foundation.

The solution starts now—not with reactive litigation or regulatory intervention, but with proactive, thoughtful design. This business model proposal introduces Attribution Share—a forward-thinking economic framework aimed at restoring control, value, and transparency to all stakeholders in the creative ecosystem.

Attribution Share shifts the industry away from the broken market-share-based or flat-fee licensing models and introduces a more dynamic, influence-based approach to monetization. As AI platforms evolve, harnessing these innovations responsibly is essential to ensuring sustainable growth and maintaining artistic integrity—not just for today’s creators, but for future generations.

At the same time, evolving the economics of music streaming is equally critical. One model is maturing and already breaking at the seams, while the other—AI-generated music—requires a solid commercial backbone before it scales out of proportion. In the short term, AI and streaming will coexist.

With real-time licensing dynamics and rightsholder’s control of their intellectual property (IP) firmly in place, we can prevent disruptive cannibalization between the two models. The opportunity lies in aligning both ecosystems—building a foundation for AI-generated music while continuing to improve the business framework of streaming to support fair creator compensation.

Inaction at this juncture will come at a profound cost. If we do not act now to establish clear economic structures, the value of music could be permanently diluted by an AI-driven landscape. This is my contribution to the industry—a business model framework to help guide the necessary shift toward Attribution Share. 

The following proposal represents an opportunity to build something sustainable, fair, and transparent—a model we can iterate on together as an industry, while we continue to reform and improve streaming.

My hope is that this framework will serve as a collaborative starting point for all stakeholders to align on, adapt, and expand the AI opportunity. With real-time licensing, dynamic attribution, and equitable monetization at its core, the future of music AI can work for creators and businesses alike. But it starts with us—now.

Attribution Share Business Model for Generative AI Music Platforms

Core Concept:

Unlike traditional streaming models that rely on market share-based pro-rata calculations, the AI generative music business shifts towards Attribution Share. This model rewards rightsholders proportionally to how their musical assets influence the AI-generated outputs. Attribution Share emphasizes the quality and relevance of contributions in generating music rather than raw consumption volume.

Challenges with Market Share and Flat-Fee Models:

1. Dilution Effect: New content continuously fragments revenues, making it harder for artists to retain meaningful market share.

2. Fraud Vulnerability: Pro-rata models incentivize stream manipulation, distorting payouts and rewarding volume over genuine engagement.

3. Non-Scalable Compensation: As volume grows, payouts shrink, decoupling creator compensation from actual value or influence.

4. Flat-Fee Blanket Licensing Risks: Licensing assets to AI platforms on flat-fee deals offers upfront payments but limits long-term remuneration. As AI scales beyond the revenue potential of these agreements, rightsholders risk being locked into subpar compensation. Unlike past models, such as Facebook’s multi-year deals, AI platforms will evolve in months, not years, leaving IP holders behind. Flat fees, no matter how high, can't match the exponential growth potential of generative AI.

5. Synthetic Data Threats: Synthetic data in AI training could bypass licensing entirely, severing IP holders from future earnings. Without clear frameworks, synthetic datasets undermine the ability to protect artist rights and ensure fair compensation, while disrupting opportunities to align innovation with sustainable business practices.

The Attribution Share model counters these issues by ensuring financial compensation is linked to asset usage and relevance within AI platforms, irrespective of content proliferation.

Revenue Streams and Service Tiers:

The business model requires tiered service structures to accommodate various ways AI platforms generate revenue:

1. Transactional Model (Pay-per-Credit/Token):

   • Users purchase credits to generate music or audio outputs.

   • Attribution Share allocates revenue directly from each transaction based on the influence of rightsholder assets in AI outputs.

   • Precedent: Transactional revenue resembles pre-streaming models like digital downloads or sync licensing.

2. Subscription Model:

   • Users access the AI platform for a recurring fee.

   • Attribution Share distributes subscription revenue proportionally, based on the cumulative influence of musical assets over the subscription period.

3. Hybrid Model:

   • Combines subscription and transactional revenue streams.

   • Attribution Share applies distinct formulas for each revenue source to maintain fairness across all contributors.

Implementation Through Sureel.ai:

Sureel.ai will serve as the infrastructure backbone for Attribution Share platforms, providing:

1. Dynamic Licensing API:

   • Rightsholders manage asset opt-ins and opt-outs in real time, allowing precise control over which assets can be used for training or generation.

   • Sureel.ai tracks usage through neural network analysis to provide granular data.

2. Real-Time Attribution Dashboard:

   • Stakeholders (labels, publishers, artists, songwriters) view in real time how their assets are influencing AI outputs.

   • Immediate remuneration flows, eliminating the need for complex audits and delayed reporting.

3. Compliance and Protection:

   • Sureel.ai integrates with a Do-Not-Train Registry (ATLAS) to ensure unauthorized training is blocked, providing creators with control over AI-generated content.

   • Attribution Models: Sureel.ai creates mathematical representations of rightsholders' assets, enabling their IP registries to be referenced in a language AI platforms can use effectively.

Key Benefits of Attribution Share:

• Sustainable Monetization:

  • As the volume of content increases, attribution-based remuneration grows with the influence of foundational assets, ensuring fair compensation over time.

• Transactional Flexibility:

  • Users pay directly for outputs they generate, offering an experience closer to physical media sales or sync licensing.

• Control and Transparency:

  • Real-time attribution data gives creators continuous insights into the impact of their music within AI systems, reinforcing trust and accountability.

Summary Framework for Stakeholders:

Strategic Outlook:

The Attribution Share model has the potential to reverse the devaluation of music caused by streaming fragmentation. As AI-generated music expands, rights owners will find new growth opportunities by capturing transactional and subscription-based revenues. By maintaining real-time asset control, the music industry can thrive in an AI-centric future, partially reclaiming dynamics lost to streaming and piracy eras.

This framework provides a path for establishing a sustainable, scalable, and transparent business model for music generative AI platforms while allowing the streaming model to evolve in parallel. Together, both ecosystems can coexist without cannibalization and foster a fairer future for all creators.

[Detailed ‘Part 2’ Attribution Share Calculation Models for AI Platforms published on December 9, 2024 below]

The human voice is an instrument that has passed on cannon, challenged culture and continues to archive the vulnerability of human expression throughout history.

At times a voice is capable of enrapturing us into an unexplainable state. At its sublime in sound and performance, a single voice can be a form of magic.

However the voice itself (its timbre, texture, personality, identity) has never possessed the intellectual property or commercial value as other elements of music. Its value was limited to its contributions to a musical recording or live performance.

Until now.

What I'm talking about here is AI likeness technology – or what some call voice modeling or voice cloning through the use of AI.

The reason this area of AI innovation has resonated with me is two-fold.

• Personally – my mother was a professional singer

• Professionally – I work on improving, innovating and protecting the value, rights and economics of music

Also what AI likeness technology has created is two-fold.

• newfound Intellectual Property (IP) that generates a new revenue stream for artists

• the means to create and preserve legacy

Legacy

What is this brief mortal life if not the pursuit of legacy?

Everyone of us would define legacy differently.

Our storied lives are shaped differently, as are our 'gifts', experiences and our pursuit of some form of purpose.

Ultimately – legacy is creating something enduring that can be passed on, hopefully through several generations.

And what I realized is AI likeness gave my mother the ability to preserve her singing voice for the next generation.

Fearless

Recently my mother had prompted the conversation about getting her affairs in order and discussing her eventual passing. As for most her age, it is not the easiest of conversations.

As the son of two working immigrant musicians, I grew up with not much possession wise but had the cultural wealth of being the child of two respected pillars of their community. Their work ethic and passion for the arts taught me much about life that is ingrained in everything I do.

When the subject of her will and testament came up, instead of speaking about possessions I asked her –

"What would you do if I could preserve your singing voice, forever?"

Instead of fear there was bewilderment and innocent jubilation at the idea.

It dawned on me there is little-to-no fear of this technology for someone beyond their active years as a performer or singer.

Why?

The reason for my mother's curious jubilation is my daughter, who is four years old and has just begun to show her natural affinity and passion for music – particularly singing.

Immortalization, Meaning, Collaboration

Unspokenly, we both thought about the possibility of my mother passing before my daughter was able to mature and share in that craft together.

Today they play and sing themselves into playful laughter, but I can imagine them in years to come collaborating on something that crosses generations and has some element of depth and meaning for both.

What I can also imagine years from now is my daughter singing a duet with her grandmother years after she has passed or after her voice has aged out of its ability to perform.

There is the opportunity to immortalize that potential.

But what of artists in their active years, coming to terms with such powerful unregulated accelerating technology?

There is a dire need to directly shape what is happening – technologically, culturally and ethically in this space. We as an industry need to get our affairs in order.

In partnership with the Artist Rights Alliance, 200+ artists including superstar household names have issued an open letter rightfully demanding protection, consent and responsible use of AI in the face of developing challenges.

For all creators pursuing careers in today's fragmented, confusing music economy – what is the best way forward?

As my parents had taught me and I, in turn, teach my children – we must lead by example.

That is why I have joined Voice-Swap.ai as Chief Strategy & Impact Officer, a company founded by industry veterans committed to advocating for artists' rights and the ethical use of AI.

There Is No Room For Desperation

Like any new technology that makes the impossible possible, rapid experimentation and adoption is met with rabid hunger for commercial gains.

Getting ahead of an untamed market, with little-to-no regulation, there is a need for companies to emerge that are able to commercialize the effort and do so ethically.

This is necessary for the welfare of the creator community and for their businesses to thrive.

And it starts with consent.

As we carefully and selectively outsource our humanity to AI, we need AI innovators to institute consent, compliance and certification of their models, while building the means to legitimately monetize this newfound proposition for the human voice.

But Where Is The Money?

Bluntly put – technological innovation in music becomes legitimate when it can both generate revenues and pay royalties based on the new technology.

Relying on royalties from broken systems such as music streaming is an afterthought for the AI likeness opportunity.

For such innovation to have the ability to support and advance human creativity – it has to generate new revenue for artists.

So how do we properly monetize AI likeness?

Do it as the source of the technology's use – meaning at the point of inference (i.e. when a voice is converted to another via the AI model, regardless if it is commercially released).

Integrity Based Commitment

Has anyone pulled this off yet? Yes.

Setting a precedent in the industry, Voice-Swap has issued its first cycle of royalties to its artists for the inference (or conversion) of voices utilizing their certified voice models.

Additionally the company has introduced a new artist dividends program. This ensures that as the platform grows, those artists who trusted Voice-Swap with their voices are also rewarded as key stakeholders while nurturing a long-lasting and trust-based relationship.

Lead by example.

And to clarify – inference based royalties are separate and in-addition to licensed use of these creations generating royalties on streaming platforms and the like.

So What Does This Mean?

While the economic landscape of music is forced to evolve, the human voice occupies a newfound space of significance, generating revenue for artists at the point of creation.

While this may challenge traditional notions of authenticity, AI tools like these are poised to complement, rather than overshadow, the artistry of human expression.

They also amplify and enhance our capabilities, facilitating new forms of collaboration and expression. Great example being Ruth Royall's collaboration with singers across the world to translate her song ‘Lullaby’ into multiple languages.

As we navigate this terrain, legacies are yet to be written.

And we must do so with a commitment to integrity, innovation, and above all, respect for the voices that enrich our lives.

Only then does the potential for artistic innovation know no bounds.

This is the power of user-centric.

I've been working on the #Superfan proposition, at the intersection of streaming, for over five years.

My vision was simple. The lift was complex.

Change how you pay, you change the outcome and data to more closely align with fandom (ie an individual’s natural behaviors).

You change the game.

This paper is a testament to that.

Having closely worked with Will Page on this effort, I'm elated that it is finally seeing the light of day.

Dive in, you won't regret it. I expect this to be 'the artifact' of the streaming debates going forward (right next to MIDiA's excellent paper).

Massive love and respect to those that were involved and especially those in the trenches to make this happen alongside Will, namely:

Lucinda Viljoen, Matt Jong, David Turner and John Atzberger.

[LINK] SoundCloud Rockconomics

This article was originally published Aug. 8, 2023 via Forms + Shapes and showcased on Water & Music via members-only community interview published Sept. 14, 2023.

These are the words a recording artist and friend texted me recently.

It's a great question (and headline).

Streaming models and content economics are among my bread and butter.

Now let's take a stab at both and simplify it all.

And I promise – little-to-no 'inside baseball' jargon in this one.

Misunderstood

By exploring a bite-sized, conceptual understanding of streaming economics we can explain why it is continually being misunderstood – even by industry experts.

We can also inform what actions to take as artists, partners and industry folk as we understand it a little bit better.

The statement to burn into the back of your mind is the following:

💡 Streaming payouts are not transactional, they are fluid.

Unlike traditional, physical and digital download eras, streaming payouts are calculated based on ebb and flow economics (aka constantly fluctuating factors).

That is why the debate is complex, often misconstrued and should not be measured by a static one-dimensional indicator such as – 'what is the industry/platform/market per play rate?'

Bare with me.

The 'Fluctuating' Parts

There is a constant ebb and flow to

• how streamed music is listened to by audiences

• how platforms generate revenues in different countries, and

• how platforms' various "bells and whistles" are applied to calculate it all

Let's focus on the first two of these factors.

Monolith

As streaming continues to be the monetary monolith of music (see below), while showing signs of innovation (new business models), we are presented with the need to understand its fundamentals.

What I'd like for you to walk away with is conditioning yourself into thinking and applying this ebb and flow concept across the business. Foundational incentives change with new models, however the ebb and flow nature of streaming economics will remain.

Seasonality

Your sense of cyclical or seasonal, in how you craft music and release it, should also be applied to your holistic business thinking. Ask yourself:

• What happens when there is an up-turn (in growing number of listeners, platform-wide new releases)?

• What happens when there is a down-turn (in platform-wide revenues and new releases)?

Let's set fragmentation and the current broken model of pro-rata aside.

Let's dive into an ebb and flow example.

This is what had happened to my artist friend earlier this year (hence the great question/headline).

A Fish In A Pond

To showcase the concept I'm going to illustrate:

• the nature of advertising seasonality

• tie it to the release of a record, and

• how the above factors could impact streaming payouts

At the start of every year, there is a seasonal reset in the advertising industry. Meaning in January of every year (and a good portion of Q1), industry wide advertising sales and revenues see a natural softening or down turn after the aggressive ad sales push of the previous end-of-year holiday quarter.

So how does this relate to an artist releasing a record or to streaming payouts?

Because most platforms have an advertising tier of service (ie their 'free' listening experience) and their payouts are generally tied to ad market seasonality.

So now back to ebb and flow!

Small Fish, Big Pond

You release a new record in November where:

• platform advertising revenues are at an annual high (larger $ pie)

• there are significant new releases from global artists this time of year (it's very crowded, difficult for you to gain market share)

• you market your new record (resulting in strong play count performance)

Result:

• there is a 'bigger pie' of platform money that month to be shared (ie Big Pond)

• there is a 'ballooned' competitive landscape of other new releases (other artists competing with you for a share of plays)

• you garner a marginal market share from strong play count performance against the 'ballooned' landscape (ie Small Fish)

• your payout is also smaller (ie Small Fish)

What happened?

Even though there was more money to share with all artists, you were ultimately dwarfed by others' performance.

Big Fish, Small Pond

Now you release a follow-up record in January where:

• platform advertising revenues significantly soften in January (much smaller $ pie)

• there are less-than-usual new releases from global artists this time of year (its less crowded)

• you market your new record significantly more than your last (more plays, more market share)

Result:

• You garner more plays, more market share for this release than the last (ie Big Fish)

• However you end up making less money than the last.

What happened???

Even though there were less releases to compete with your higher volume of plays, there was much less money to be shared with all artists.

How could this be right?

The fluidity and fluctuating factors were not in your favor. Even though you garnered more plays, and there were less releases on said platform – the ad revenues ebbed to a lower amount as a whole (as seasonaly expected).

Is this wrong? Arguably no its not, but it is an issue of the core principles of the pro-rata model. All or nothing via market share dynamics.

And for those experts trying to squeeze an 'economic equilibrium' out of music streaming – this is one of many reasons why the pro-rata model has been outgrown for use for many years (initially the pond was much smaller, beneficial for those early swimmers).

That said – what to action? How to action?

The Answer

Identify your ebb and flow and where most of your money is coming from streaming.

• Identify where most of your streaming payouts come from (platform wise).

• Then identify if most of that platform's payouts are coming from ad served or subscriber based listeners (your distributor reports should indicate this).

• If most of your money is coming a platform's advertising served listeners – you now know to consider the fluidity of streaming and advertising seasonality when releasing new music.

My personal recommendation to this artist was two fold.

1. Being knowledgeable of the above, and

2. Begin taking ownership of their fans within their greater audience across platforms.

Reaching and moving their most ardent fans to platforms where they are able to directly contribute to the artist isn't an escape from these other platforms but a means to expand the funnel of where your audience lives, listens and eventually thrives (ie generates your livelihood).

The above had turned this artist's payouts around, as well as reduced their susceptibility to shifts in streaming dynamics.

Bigger Takeaway: Lifers

Economists are lifer strategists. We think long-term. And so do artists and their teams.

Your musical world building is life-long and long-term.

Focus on what you can control.

Learn to weather the ebb and flow of your business, your creation and learn to insulate yourself from outdated systems, models and avoidable events.

The human instinct for self-preservation is high these days. As is the need for education.

Lean in where you can, share experienced knowledge (like this piece). That's how we de-mystify the industry.

There is no shortage of mystification and debate in music today (streaming models, fraud, generative AI, list goes on).

Let's keep shifting doubt and debate into action.

Let’s set the record straight – fraud is an ever evolving threat to every market in the modern world.

And the music industry is no exception.

Streaming, the dominant form of music consumption, contributes approximately 83% of the industry's revenue as of now. However, it is plagued by fraud, with estimates suggesting over $2 billion lost annually. 

Criminal enterprises and bad actors’ coordinated attention have resulted in the destabilization of the economic principles of music.

And all artists and stakeholders feel its impact. 

Over the past two years, I've openly addressed this issue, gaining insights from both internal and external perspectives, working across various DSP/streaming platforms to lead strategic efforts in resolving the problem. This involved architecting new streaming models and collaborating with anti-fraud coalitions and services like Beatdapp to bring attention and actionable technical capabilities to address this growing issue.

As both an economist and musician, I find this issue troubling, requiring organized attention and action. 

Let's delve into the problem, assess where we stand, and discuss the necessary next-step solution.

The Problem

I’ve said it before and I’ll say it again – undetected or unaddressed fraudulent activities pose a significant threat, potentially leading the music industry to invest in and bank on inauthenticity, impacting how we measure the success of music culturally.

Gambling 83% of an entire industry's income, which continues to grow globally, on broken systems susceptible to fraud is not a risk we can afford to take.

More importantly — the continued innovation in music tech should not be measured solely by its potential impact on already broken systems.

To understand how to combat fraud, let’s look at how other industries manage and combat these inevitable challenges. 

The Example

In the financial sector the Financial Services Information Sharing and Analysis Center (FS-ISAC) was created in 1999 as a platform for sharing information and best practices to combat cyber threats and financial fraud.

In the healthcare industry the National Health Care Anti-Fraud Association (NHCAA) is a prominent organization that works to combat healthcare fraud in the United States.

In advertising the digital advertising industry has been battling ad fraud, including click fraud and impression fraud. Companies have formed initiatives like the Interactive Advertising Bureau's (IAB) Anti-Fraud Working Group to develop standards and best practices for fraud prevention.

These organizations have been crucial in addressing the ever-evolving nature of fraud in the modern world in their respective industries.

By sharing information, best practices, and continually scaling crucial technologies, these organizations aim to stay ahead of fraudsters and minimize the impact of fraudulent activities on their respective sectors.

But before addressing their technical capabilities – how did these organizations come to fruition?

In almost all cases efforts began as industry coalitions. 

The realization that collective action is more effective than individual efforts is a crucial starting point. I believe this is where we are today.

Coalitions are a good thing. 

They are a sign of collective agreement by acknowledging there is a greater problem. 

However, they are not the solution. 

Forming these organizations can be complex, with challenges related to conflicting incentives among participants.  

Before proposing a solution, let’s revisit the music industry’s problem.

Subscribe now

The Music Problem Revisited

Streaming fraud is a significant threat to the cultural climate of emerging music, artists' livelihoods, and the industry's innovation. At the core of this issue is an outdated industry streaming model called pro-rata (full explanation here).

The pro-rata streaming model has become a potential 'goldmine' for criminal activities, allowing manipulation for monetary gain.

By industry estimates, a $10 premium account can generate up to $352 in revenue per month from streaming fraud.

The shortcomings of this model have attracted criminal enterprises, leading to issues like money laundering or simply bad actors pursuing monetary gain. 

Consensus 

The industry consensus is that the status-quo pro-rata streaming model must go, as it plays a foundational role in facilitating fraud. 

Large players in the market have finally started to make moves to penalize bad actors and potential enablers. 

Others have moved to overhaul and innovate the streaming model space in order to shift incentives and the means to defraud a system.

With such change, this eventual differentiation of how various platforms pay artists is a good thing (more here). 

However the title of this piece needs to be repeated – “fraud, the eternal frontier”.

Even with new models, the incentives and means of fraud shift.

As new streaming models inherently curb fraud, new and varied incentive structures are introduced. 

Sophisticated fraud will attempt to evolve alongside these new incentives and diminish their commercial potential. Bad actors are effective shapeshifters who respond to the incentives created by new models.

So what is the solution?

The Necessary Next-Step Solution

Considering the complexity of the above – let’s address the bigger need.  

We need a well-resourced, information, and data-sharing collective hub similar to those established in other industries decades ago. This hub must be built from active relationships within the music industry and function as an 'outside entity' to its members.

A collective hub would be key in prevention while clearing innovation's commercial runway.

From my experience across services and solutions, Beatdapp stands out as the well-positioned solution, trusted by major organizations, working with over 30 trillion data points across various partnerships, and continually improving capabilities to tackle complex fraud models. Also from personal experience in piloting referral cases, Beatdapp exemplified having a direct channel to law enforcement coordination. 

Most importantly of all, Beatdapp has no thumb on the scale. 

As a trusted third-party source of truth, a Beatdapp-backed global consortium would amplify the industry’s intelligence, knowledge, and shared practices – ultimately balancing competition with collaboration while ensuring commercial insurance for all. 

I am hard pressed to see if there is another more vital bigger-picture next-step needed in this space. 

Acknowledging the problem was the first step; now, let's build the ability to act unilaterally across industry stakeholders, working towards a common goal of combating fraud. 

The innovation and success of the music industry depend on it.

Subscribe now