“Fair use” in generative AI means rethinking intellectual property in a financially driven digital…

“Fair use” in generative AI means rethinking intellectual property in a financially driven digital economy.

Understanding “fair use” as a financial accountability issue.

The litigation in Kadrey v. Meta has drawn public attention to one of the most significant economic questions of the decade: how should copyrighted material be valued when it underpins commercial artificial intelligence (AI) products? Meta’s internal financial disclosures, as revealed in unsealed court filings and internal slide decks from the Kadrey v. Meta case, project $2–3 billion in generative AI revenue for 2025 and between $460 billion and $1.4 trillion by 2035. These revenues are anticipated to derive from subscriptions, advertising, and API access, rather than theoretical research, but rather structured monetization across multiple business verticals.

This raises a fundamental challenge to the current application of the “fair use” doctrine. When content generates capital returns on this scale, the argument that its use is non-commercial begins to break down. What was previously framed as a legal exception now signals a gap in financial attribution. The controversy is not simply about how AI systems train on data, but whether the frameworks governing copyright are equipped to reflect the asset-like role that content now plays in digital markets.

Financializing intellectual property rights

The debate over copyright in the AI era increasingly parallels the treatment of securities and other regulated asset classes. Just as securities require disclosures, attribution, and rules against insider trading, intellectual property in AI training arguably demands transparent sourcing, authorized licensing, and fair compensation.

If copyrights were reclassified as financial instruments, then unauthorized training data usage would not merely be a tort; it would parallel financial misconduct akin to securities violations or anti-counterfeiting offenses, requiring regulatory oversight and structured compliance. Copyright infringement under such a regime would resemble counterfeiting: the production of synthetic outputs derived from unauthorized intellectual inputs. Likewise, laundering those outputs through commercial APIs or monetized services would mirror financial money laundering, bypassing origin attribution to produce cleansed, untraceable value.

Emerging models suggest that this reclassification is feasible. Recent developments in tokenized royalty markets allow for fractional ownership, traceable provenance, and real-time compensation across content ecosystems. Platforms like NIM’s Copyrighted-as-a-Service (CaaS) use distributed ledgers to assign, track, and reward rights holders as content circulates through licensed channels. These mechanisms embed copyright enforcement into the financial stack, not the legal back office.

Economic governance of content in AI systems

The convergence of programmable finance and creative rights provides an alternative to reactive copyright litigation. Systems designed to treat content as capital can enforce attribution and licensing dynamically at the code and contract levels. By integrating copyright logic into settlement protocols, AI platforms can become compliant by design, rather than through post hoc negotiation.

A governance layer based on content registration, tokenized royalties, and automated payouts changes the conversation. In this framework, AI developers not only avoid infringement but also participate in a market of licensed intellectual assets, where value flows transparently and rights holders earn proportionally to usage.

This model protects creators and builds confidence among investors and regulators. By clarifying content origins, pricing access, and structuring royalties as financial flows, such systems bring predictability to what is currently a legal gray zone. This predictability benefits creators by ensuring timely and transparent compensation. At the same time, developers gain access to a stable and compliant framework that reduces legal risk and fosters trust with users and regulators. The very act of training and deploying AI models become auditable, fair, and economically sustainable.

Fair use, as traditionally defined, cannot encompass the economic magnitude of AI-generated value. The need is not only for new legal standards but also for a financial infrastructure that treats intellectual property as a tradable, enforceable asset. The current disputes over AI and copyright underscore the lack of such infrastructure and the expense of relying solely on judicial remedies.

As generative AI scales, the systems that support it must adopt the standards of financial accountability applied to any other trillion-dollar industry. Recognizing copyrighted content as capital is not an ideological claim but a practical necessity. This recognition redefines stakeholder responsibilities, requiring platforms and AI developers to incorporate licensing and attribution into operational workflows. It also creates new investment channels, allowing rights holders and financial institutions to treat copyrighted works as yield-generating assets within structured and regulated markets.

Enforcing its integrity through modern financial tools is the logical next step.

The future of fair use is not only a matter of interpretation. It is a matter of infrastructure.