Coasean Bargain Framework for AI Copyright & Data Scraping

A new research paper published on arXiv introduces a provocative legal-economic framework for addressing one of the most contentious issues in artificial intelligence: the scraping of copyrighted data to train generative AI models. Titled "Agentic Copyright, Data Scraping & AI Governance: Toward a Coasean Bargain in the Era of Artificial Intelligence," the paper applies the economic theory of Nobel laureate Ronald Coase to propose market-driven solutions for the collision between copyright holders' rights and the voracious data appetite of modern AI systems.

The Copyright Crisis Facing Generative AI

The current landscape of generative AI—spanning text-to-image models, video synthesis systems, voice cloning tools, and large language models—depends fundamentally on massive datasets, many of which include copyrighted material scraped from the open internet. This has triggered a wave of litigation, from Getty Images vs. Stability AI to the New York Times vs. OpenAI lawsuit, and has left the industry operating in significant legal uncertainty.

For creators of synthetic media—including AI-generated video, deepfake technology, and voice synthesis—the outcome of these disputes is existential. The training data that powers models like Stable Diffusion, Runway Gen-3, Sora, and ElevenLabs' voice models consists substantially of copyrighted images, video clips, and audio recordings. How regulators and courts resolve these tensions will directly shape what generative AI can legally produce and at what cost.

Applying the Coase Theorem to AI Training Data

The paper's central thesis draws on the Coase Theorem, which argues that when property rights are clearly defined and transaction costs are sufficiently low, private parties can negotiate efficient outcomes regardless of which party initially holds the rights. Applied to AI copyright disputes, this framework suggests that the key bottleneck isn't the law itself but rather the absence of clear property right assignments and the prohibitively high transaction costs of negotiating licensing at scale.

The research examines how "agentic copyright"—a concept wherein AI agents themselves participate in or automate copyright negotiations—could reduce these transaction costs. By deploying AI-powered systems to manage rights clearance, licensing, and royalty distribution, the paper argues that a more efficient market for training data could emerge organically.

Key Proposals and Mechanisms

The paper explores several governance mechanisms that could facilitate a Coasean bargain between AI developers and content creators:

Automated licensing platforms: Systems where AI agents negotiate and execute data licensing agreements at scale, dramatically reducing the per-transaction cost that currently makes individual rights clearance impractical for datasets containing billions of items.

Collective rights management: Drawing parallels to music industry models like ASCAP and BMI, the research envisions collective organizations that could represent creators' rights in bulk negotiations with AI companies, with automated royalty distribution based on actual model usage.

Clear property right allocation: The paper argues that regulatory clarity—whether through legislation or judicial precedent—on whether AI training constitutes fair use is a prerequisite for any market-based solution to function efficiently.

Implications for Synthetic Media and Digital Authenticity

For the synthetic media ecosystem, this research has several critical implications. If a Coasean framework were adopted, it could fundamentally alter the economics of training generative AI models. Companies developing AI video generation, face-swapping technology, and voice cloning systems would face new cost structures based on the market price of training data rather than the current de facto model of scraping without compensation.

This could create a two-tier market: well-funded companies like OpenAI, Google, and Meta could afford premium licensed datasets, while smaller competitors might be priced out—or driven toward synthetic training data generated by other AI models. The implications for model quality, diversity, and bias are substantial.

The framework also intersects with digital authenticity concerns. If AI-generated content becomes subject to clearer provenance requirements as part of licensing agreements, this could accelerate adoption of content authentication standards like C2PA. Knowing which copyrighted materials contributed to a generated output could become both a legal requirement and a technical challenge—one that would demand new approaches to model transparency and attribution.

The Road Ahead

While the paper offers an intellectually compelling framework, practical implementation faces enormous challenges. The sheer scale of modern training datasets—often containing billions of data points from millions of creators—makes individual negotiation impractical even with AI-powered agents. Additionally, international jurisdictional differences in copyright law complicate any unified governance approach.

Nevertheless, the research contributes a valuable economic lens to a debate that has been dominated by legal and ethical arguments. As courts worldwide continue to adjudicate AI copyright cases, and as regulators from the EU AI Act to proposed US legislation shape the governance landscape, frameworks like this Coasean approach may prove instrumental in finding workable compromises between innovation and creator rights.

For developers and users of generative AI—particularly in the high-stakes domains of video synthesis, deepfake creation, and voice cloning—understanding these governance trajectories is essential for strategic planning in an industry whose legal foundations remain in flux.

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