Fair Use as an Industrial Policy: What 'AI Progress' Is Really Arguing For — and What It Leaves Out. Critics can point to any counterexample—model outputs that substitute for works...

2025-11-28 · Source: Pascal’s Substack · Field: Legal & Regulatory — Intellectual Property & Patents, Regulatory Affairs & Government Relations · Depth: Advanced, medium

Summary

The "AI Progress" initiative, through its website and the report "AI Models: Addressing Misconceptions About Training and Copyright" by Chauvet & Kumar, advocates for fair use as a critical industrial policy driving U.S. technological dominance, particularly in AI. The initiative asserts that fair use, historically fueling innovation, is essential for AI to access broad data for training, thereby preventing risks to medical/scientific breakthroughs, economic growth, and national security against competitors like China. It frames fair use not merely as a copyright limitation but as a strategic national asset, arguing that AI models learn statistical patterns rather than storing expressive works, and that training constitutes fair use even if copying occurs. The report also warns against mandatory training data disclosure, citing trade secret concerns and potential barriers to U.S. leadership.

Key takeaway

For CTOs and VPs of Engineering navigating AI development and regulatory landscapes, understand that the "fair use as industrial policy" argument is a coherent, but potentially fragile, legal and political stance. Your teams should prioritize verifiable transparency, implement robust anti-memorization controls, and establish clear redress mechanisms to build legitimacy and mitigate legal risks, especially concerning data provenance and potential market substitution by model outputs. Relying solely on abstract "transformative purpose" without addressing governance gaps may invite significant backlash and regulatory intervention.

Key insights

Fair use is presented as a national policy engine for AI innovation, crucial for U.S. technological leadership.

Principles

• Fair use enables transformative uses of information.
• AI models learn patterns, not direct copies.
• Training data access is vital for AI progress.

Method

The initiative employs a policy positioning strategy by framing fair use as an innovation driver and national security imperative, preempting interventions like mandatory dataset disclosure.

In practice

• Frame AI training as a transformative fair use.
• Anchor AI policy to national security concerns.
• Explain LLM mechanics to counter "database" misconceptions.

Topics

• Fair Use Policy
• AI Training Data
• Copyright Law
• LLM Governance
• Market Substitution

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, Legal Professional, AI Ethicist

Related on AIssential

• The legal theory used against commercial AI companies may also reach academic AI research, open models, university labs and public-interest research infrastructure. — The Hendrix v. Apple case could redefine fair use for AI training, impacting academic research and open models.
• ChatGPT about Anthropic's filing: "Training fair use is strongest when paired with demonstrable, continuously improved technical and policy measures to prevent substitute outputs." — AI training on copyrighted works is argued as transformative fair use, distinct from potentially infringing model outputs.
• Industry welcomes rethink as UK drops preferred AI copyright plan - Research Information — The UK government dropped its preferred AI copyright exception, prioritizing fair licensing and creator protection.
• Microsoft trained its MAI models on unlicensed web data despite promising "enterprise grade, clean and commercially licensed data" — Microsoft's MAI models used unlicensed web data, challenging its "clean data" claims amidst ongoing fair use legal disputes.
• Given AI is trained on the work that the public has produced and legally owns and has made available on the internet, should all of these models be nationalised and taken into public ownership too? — The debate centers on whether AI models, trained on public data, should be nationalized or treated as public utilities.
• Intellectual Property vs. Machine Learning: Data Ownership and AI Models in the Age of Litigation — AI's reliance on vast datasets without clear consent creates profound, unresolved intellectual property and ownership disputes.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pascal’s Substack.