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:
Dilution Effect: New content continuously fragments revenues, making it harder for artists to retain meaningful market share.
Fraud Vulnerability: Pro-rata models incentivize stream manipulation, distorting payouts and rewarding volume over genuine engagement.
Non-Scalable Compensation: As volume grows, payouts shrink, decoupling creator compensation from actual value or influence.
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.
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:
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.
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.
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:
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.
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.
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.
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.
