Today, the influence of Durie Tangri alumni extends beyond the courtroom, permeating the in-house legal departments of Alphabet, Meta, Amazon, and OpenAI...

2025-11-28 · Source: Pascal’s Substack · Field: Legal & Regulatory — Intellectual Property & Patents, Compliance & Risk Management · Depth: Advanced, long

Summary

The legal landscape of generative AI intellectual property is significantly influenced by a network of attorneys originating from the defunct San Francisco boutique firm Durie Tangri, acquired by Morrison Foerster (MoFo) in early 2023. This group, now dispersed across Big Tech companies like Alphabet, Meta, Amazon, and OpenAI, and concentrated at MoFo, shapes AI copyright defense strategies. Their influence stems from establishing "transformative use" precedents, notably in *Authors Guild v. Google*, and developing the "Fair Learning" doctrine, articulated by Mark Lemley. This unified legal front defends AI training on copyrighted material by arguing it constitutes non-exploitative, intermediate copying for learning statistical relationships, not expressive content reproduction. MoFo currently represents OpenAI in approximately one-third of its federal litigation, defending valuations up to $852 billion.

Key takeaway

For CTOs and legal counsel navigating generative AI copyright litigation, recognize the concentrated legal expertise defending AI models. Your strategy should anticipate defenses based on "transformative use" and "Fair Learning." Proactively audit training data provenance to avoid "piracy chokepoint" vulnerabilities and prepare to challenge "independent creation" claims with output logs. Leverage potential conflicts of interest within the defense network to gain tactical advantages in negotiations or litigation.

Key insights

A specialized legal network dictates generative AI's intellectual property boundaries through "transformative use" and "Fair Learning" doctrines.

Principles

• AI training is transformative fair use.
• Intermediate copying for learning is permissible.
• Niche legal expertise drives tech IP strategy.

Method

The "Fair Learning" doctrine posits that ML systems copy works to access uncopyrightable elements like facts and linguistic structures, not to exploit creative expression, justifying wholesale data ingestion as a facilitative, intermediate step.

In practice

• Prioritize provenance in AI training data discovery.
• Challenge "black box" defenses with RAG technology evidence.
• Demand ethical screen accounting from multi-client firms.

Topics

• Durie Tangri Alumni
• Intellectual Property Litigation
• Generative AI Copyright
• Fair Use Doctrine
• Fair Learning Theory

Best for: CTO, VP of Engineering/Data, Executive, Legal Professional, Director of AI/ML, Consultant

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