Rethinking IP in the Age of Scaled Creativity
By George Karalexis & Donna Budica
AI has not disrupted the creative process — it has revealed its true nature. To future-proof the creative economy, we must stop fighting the mechanics of creativity and start building systems that reward it at scale.
Executive Summary
As artificial intelligence becomes deeply embedded in creative workflows, the debate over its use of intellectual property (IP) for training has reached a fever pitch. Much of this discourse centers on whether it is fair — or even legal — for AI systems to ingest copyrighted material.
That framing, however, misses the forest for the trees.
Creativity has always been derivative. AI does not break the creative process; it mirrors it — accelerating what humans already do. The core issue is not whether AI should learn from existing works. It’s how to build systems that reflect the reality of creative development and ensure those who contribute to it get paid.
If the media and entertainment industry hopes to lead the next creative wave, it must shift from protectionism to enablement. This means architecting infrastructure that rewards participation, builds traceable influence models, and scales compensation in real time.
1. Creativity Has Always Been a Remix
There is a persistent myth in the creative industry: that great work springs from pure originality. In truth, influence is the bedrock of every art form.
Hip-hop wasn’t the first to sample; cinema wasn’t the first to reference. Every genre, movement, and innovation stands on the shoulders of what came before. Artists learn by consuming, borrowing, and reinterpreting.
Generative AI is not a disruption of this cycle — it’s a continuation of it at scale. Training models on large corpuses of films, songs, or images mimics what creators already do manually. The difference is speed and scope. Where a human might study five great directors over a decade, an AI can learn structural patterns from thousands of films in minutes.
The fear that AI systems will somehow destroy creativity misunderstands the process entirely. These systems don’t replace creators — they amplify them.
2. The Current IP Framework Wasn’t Built for Learning Systems
Legacy IP law was designed to manage tangible reproductions: records, DVDs, books. But AI models don’t copy content — they internalize patterns. They learn rather than duplicate. This distinction breaks traditional licensing logic.
That doesn’t mean creators shouldn’t be compensated. They absolutely should. But compensation in the AI era must reflect influence over ownership. We need models that track contribution — not through binary yes/no copyright flags, but through scalable influence attribution.
The current framework is overly focused on scarcity. It protects archives instead of enabling innovation. In a world where culture is constantly remixed, we need systems that unlock value rather than wall it off.
3. Global Markets Are Already Moving
As the West debates the legality and ethics of AI training on copyrighted content, other regions are already sprinting ahead. In China and parts of Southeast Asia, generative models are being trained on everything from music catalogs to entire libraries of historical dramas — often without the same legal friction seen in the U.S. or Europe.
China’s track record on IP enforcement has been inconsistent. That makes it difficult to emulate their approach wholesale. But it would be a mistake to ignore the strategic clarity they’re operating with. They’re not bogged down by legacy frameworks. They’re not paralyzed by lawsuits. They are building infrastructure for the next creative era — and doing so with speed, scale, and ambition.
The result? A faster innovation cycle, more market-ready tools, and a creator ecosystem that is adapting in real time. While Western markets are still arguing about what's allowed, these ecosystems are focusing on what’s possible.
The uncomfortable truth is this: cultural and technological leadership is shifting. If the U.S. and Europe continue to legislate based on legacy assumptions and protectionist fear, they won’t just fall behind technically — they’ll lose cultural leverage. The next generation of creators won’t wait for slow systems to catch up. They’ll go where the tools — and the opportunities — already exist.
4. From Protection to Participation: The New Infrastructure
Preserving the intent of IP law while modernizing its function is not only possible — it’s essential. The roadmap begins with these structural changes.
Trainable Licensing Pools
Rightsholders opt in to structured data sets that allow training in exchange for licensing fees. Think of it like a PRO for machine learning — a collective rights body that supports innovation while ensuring monetization.
Attribution-Based Influence Models
AI training should be traceable. Systems can estimate how much influence specific works had in the training process. Weight that influence, and distribute credit accordingly. This creates a meritocratic feedback loop — one that’s measurable and fair.
Built-In Compensation Protocols
Monetization must be embedded at the infrastructure level. Smart contracts, on-chain attribution, or embedded tracking layers can ensure that when derivative works are created, compensation is automatically routed to contributors.
Cross-Media, Cross-Territory Standards
Fragmentation kills innovation. This isn’t a music problem or a film problem — it’s a systemic challenge. We need frameworks that transcend formats and borders, enabling interoperability between rights types, regions, and creators.
5. A Creator’s Lens: This Is Evolution, Not Erosion
I’m not just speaking as a CEO — I’ve been the artist in the deal. I’ve felt that pressure firsthand: fighting to protect my masters while seeing my work lifted and repurposed with zero credit. It’s infuriating. But it comes with the territory.
At Ten2 Media, we use reference-based creation every day. We study what works. We replicate success patterns. We reverse-engineer what audiences love — not to copy, but to build. AI doesn’t change that; it accelerates it.
For the creator willing to adapt, this is a superpower. It lowers the barrier to production. It increases the fidelity of creative iteration. It gives more people a seat at the table — and more tools to deliver their vision at scale.
6. The Strategic Imperative: Don’t Protect the Past at the Expense of the Future
We are standing at a cultural inflection point. One road leads back to static control models and endless litigation. The other leads to a dynamic ecosystem where creativity scales through shared learning, structured licensing, and instant attribution.
Pandora’s box isn’t the issue. Refusing to build around what’s inside is.
The right response to generative AI isn’t to block the future. It’s to shape it — with infrastructure that rewards participation, systems that reflect how art is made today, and incentives that ensure creators are paid for their influence, not just their output.
Anything less is a strategic error — and a disservice to the creative economy of tomorrow.
