Is AI Fair Use?
Kayla Gaisi • August 7, 2025
Two recent decision suggest it can be...
This summer, the Northern District of California issued two decisions regarding the use of copyrighted works to train AI large language models (LLMs), after authors sued AI developers for copyright infringement. While both judges ruled in favor of the AI developers, according to the fair use doctrine, their decisions raise further issues about how fair use should be applied to such rapidly evolving technology.
In the first case, Bartz v. Anthropic, Judge William Alsup ruled that the use of copyrighted books to train Anthropic’s Claude AI LLM was supported by the first factor of fair use, given that its purpose and character were "quintessentially transformative.” Like an aspiring writer who has read an array of books, the LLM used the books as a basis for generating new content that neither replicated nor served the same purpose as the originals. The court also ruled that Anthropic’s digitization of purchased works for a centralized training library was fair use, though pirating unauthorized copies for the library was not.
In the related case Kadrey v. Meta, Judge Vince Chhabria relied more heavily on the fourth factor of fair use. He reasoned that Meta’s use of copyrighted works for LLM training did not cause market harm to the originals, either by reproducing them or harming the market for licensing them for AI training. However, he noted the importance of considering how LLMs could dilute the market for originals by generating substantially similar works, even though the plaintiffs did not present strong enough evidence of this.
Judge Chhabria’s warning points to a potential issue in both cases: too much emphasis placed on fair use of the LLM inputs (training material) rather than on their outputs. For example, with AI capable of creating detailed summaries of works within a matter of seconds, there is a very real possibility they will replace the need for readers to purchase copies of the originals. While online summaries have existed for decades, they have never been so accessible and personalized as those generated by AI.
In general, the question arises of how similar AI outputs have to be to the original works to cause market harm. While the markets for fiction and classic books may not be severely impacted, what about the markets for nonfiction books, whose appeal arises less so from creativity, style, and celebrity?
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As generative AI becomes increasingly integrated into our daily lives, it continues to raise legal questions that courts can no longer ignore. This month, the question of whether communications between criminal defendants and public AI are protected from government inspection was answered by Judge Jed Rakoff. That answer was an unequivocal 'no.' In the case at hand, defendant Bradely Heppner was charged with fraud and arrested a month later, in November 2025. When the FBI executed a search warrant at his home, they seized documents containing communications between him and the public AI platform Claude AI. According to Heppner's counsel, these communications reflected a defense strategy Heppner had generated in anticipation of potential indictment. Heppner asserted that these documents were either protected under attorney-client privilege or by the work product doctrine, arguing that he had used Claude for the purpose of obtaining legal advice and had shared these outputs with his attorneys. However, Judge Rakoff rejected both arguments. Attorney-client privilege applies only to communications between a client and a professional who owes them fiduciary duties and is subject to discipline. It is a socially valuable human relationship. Regardless of how advanced an AI systems is, it cannot meet this definition. Claude is not a human attorney and does not have an attorney-client relationship with its users, so communications with it cannot qualify for attorney-client privilege. Aside from this, Rakoff listed other reasons why Heppner's communications with Claude are not considered confidential. Firstly, Claude is a public AI system whose privacy policy discloses that communications can be shared with third parties including "governmental regulatory authorities." Secondly, as his counsel admitted, Heppner sought legal advice from Claude on his own volition, not at their direction. Even if Heppner received legal advice and later shared that with his counsel, that does not render the initially unprivileged communication privileged. The related work product doctrine fared no better for Heppner. This doctrine protects materials prepared by attorneys in anticipation of litigation from discovery by opposing parties. Here, the AI-generated documents were not prepared by or at the behest of counsel and did not reflect counsel's strategy. Thus, they fell outside the scope of the doctrine. Judge Rakoff's ruling matters because it maintains the narrowness of evidentiary privileges that is necessary for protecting the judicial system's truth-seeking function. Extending privilege to communications with public AI systems could create a dangerous loophoole, one where parties could shield discoverable information by filtering it through a chatbot. But given Rakoff's ruling, the main takeaway here is that attorneys should explicitly advise their clients not to share personal or legal information with public AI systems. Despite how routine it has now become for many to ask public AI personal questions, these communications are not confidential, and may ultimately be used as evidence in court.
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As of January 1st, limited liability companies formed in a foreign country who plan to do business in New York state must now disclose beneficial ownership to the Department of State within 30 days of filing their articles of organization. Beneficial owners are defined as those who exercise "substantial control" over the reporting company or who own no less than 25% of it. Each beneficial owner must provide personal information including: their full legal name; date of birth; current home or business street address; and a unique identifying number from an unexpired passport, driver's license, or government-issued identification card. Ownership disclosure statements or attestations of exemptions (for LLCs formed in other states or U.S. territories) must be now filed electronically every year, with a $25 fee for each document. For a more detailed breakdown of the new law, who it affects, and what they should do, the Department of State has provided an FAQ section on its website. This act is the first state statute allowing for a state-level beneficial ownership database, with the purpose of inhibiting fraud and theft committed by anonymous shell companies. In this way, the New York statute is a narrower extension of the Corporate Transparency Act (CTA) passed by Congress in 2021. However, some definitions differ between the two laws, leading to inconsistencies that are outlined in this article . The NYS Transparency Act is likely to undergo further modifications, and the Department of State encourages that companies regularly check their website and the New York State LLC Law sections 1106, 1107, and 1108 for updated information.
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