February 13, 2026

The year 2025 was a riveting year for those of us in media and technology—including me. I launched Meeka J. Bondy, PLLC to bring my big law, big media, and big tech background to the cutting-edge businesses shaping the future of entertainment. No doubt 2026 will bring a wave of dealmaking for media and technology companies alike. But before we get too far into the year, allow me to share my first post under my new banner.

Copyrighting the Uncopyrightable

2025 brought us the first fair use decisions—one against and two in favor—out of the dozens of copyright cases filed against AI companies since 2022. The first AI fair use ruling, Thomson Reuters v. Ross Intelligence, held that it was not fair use to use copyrighted works for AI training purposes. That rejection, however, was carefully cabined, applying only in the context of a nongenerative AI tool developed for the purpose of creating a competing product. More remarkable than the court’s fair use analysis was its soliloquy on copyrightability and the apparent fine art of lawyering.

After confirming the longstanding legal precedent that judicial opinions themselves are uncopyrightable, the court began on familiar ground, finding that Westlaw headnotes, taken as a whole, constitute a copyrightable “factual compilation,” based on the “minimal degree of creativity” reflected by the compiler’s “choices as to selection and arrangement.” But the judge went further, concluding that each Westlaw headnote, standing on its own, constituted “an individual, copyrightable work”—“even any that quote judicial opinions verbatim.”

Analogizing “the lawyer’s editorial judgment to that of a sculptor,” the court explained:

“A block of raw marble, like a judicial opinion, is not copyrightable. Yet a sculptor creates a sculpture by choosing what to cut away and what to leave in place. That sculpture is copyrightable. So too, even a headnote taken verbatim from an opinion is a carefully chosen fraction of the whole.”

While I wholeheartedly agree that the practice of law involves a fair amount of creativity, I have my doubts that a lawyer—through the craft of verbatim copying and basic paraphrasing—can, simply by lifting important quotes and placing them in a proprietary database, infuse copyrightability into uncopyrightable judicial opinions.

For me, the seemingly lower bar for “originality” here is hard to square with the U.S. Copyright Office’s approach to AI-generated works, which largely refuses copyright registration for AI-generated outputs (or at most, limits protection to the human author’s “selection, coordination, and arrangement”). In the age of AI, it feels disjointed to protect the work of human lawyers chiseling verbatim quotes from uncopyrightable judicial opinions, while discounting the work of human artists chiseling visual artwork from uncopyrightable AI-generated images. I, for one, will be looking for the Third Circuit to clarify this copyrightability issue (if not the rest of the fair use analysis).

Getting Paid for Sitting Idle

On the heels of the first rejection of the fair use defense in a nongenerative AI training case came two back-to-back fair use decisions—Bartz v. Anthropic and Kadrey v. Meta—both ruling in favor of fair use where the purpose of the AI training was to create a generative AI tool. Both cases arose out of the Northern District of California. Both involved the use of books to train LLMs. Both found generative AI to be highly “transformative.” Yet their copyright analyses were worlds apart, leading to some interesting, if not ironic, results.

For the Bartz court, two issues loomed large. First, did the AI company actually purchase the initial training copies? Second, were those copies actually used to train the LLM?

On the first issue, the judge blessed AI training only if the initial copies were purchased “fair and square.” Under the court’s reasoning, AI companies that source their training materials from pirated shadow libraries are barred from invoking fair use. No amount of subsequent transformative use could cleanse the illegitimate origins of the initial copies. For some, it may seem fair and equitable to require AI companies to pay the list price for the books in order to be eligible to raise the fair use defense. For fair use purists, however, this reasoning cuts against the doctrine’s core premise—that fair use excuses what would otherwise be infringing conduct.

On the second issue, the court drew a sharp distinction between copies that were used for training and those that were not. If the copy was actually used to train an LLM, that use was fair use. If the copy merely sat in a centralized library and never made its way to the training dock, that use was not fair use—and therefore constituted copyright infringement. At first glance, that binary distinction appears sensible. But it leads to a somewhat absurd result—authors whose books were used to train AI received no compensation (other than any royalties owed for a single copy), while authors whose books were never used for training were entitled to be made whole for the harms caused simply by having their books sit idle in a corporate database. A less absurd framing of the ruling might go like this—using books to train AI is fair use, but stockpiling “all the books in the world” in a “general purpose” library “forever” is not.

Shortly after the decision, the parties settled the remaining claims for a staggering $1.5 billion dollars—roughly $3,000 per book spread across 500,000 titles. But importantly, that settlement applied only to the pirated books that sat unused in the central library—not to the books actually used to train AI. For now, this headline-grabbing settlement offers little guidance of what AI training license fees for books should be.

The Maybe Not So Vertical Fair Use Lane

Taking a markedly different approach to fair use, the Kadrey court agreed that generative AI is highly transformative, but sharply criticized the Bartz court for fixating on the first fair use factor (the purpose and character of the use), while “blowing off” the most important factor, the fourth factor (the effect of the use on the potential market for or value of the copyrighted work). Rejecting what it called an “inapt analogy,” the Kadrey court countered:

“[U]sing books to teach children to write is not remotely like using books to create a product that a single individual could employ to generate countless competing works with a miniscule fraction of the time and creativity it would otherwise take.”

For the Kadrey judge, the issue was not just about generative AI’s ability to memorize, regurgitate, or produce exact replicas or even substantially similar outputs. It was more existential. The court elaborated:

"So, by training generative AI models with copyrighted works, companies are creating something that will often dramatically undermine the market for those works, and thus dramatically undermine the incentive for human beings to create things the old-fashioned way.”

The opinion reads almost as a plea, urging future plaintiffs to regroup and focus squarely on the fourth factor. Reluctant but nonetheless compelled to rule against the authors in this case, the judge captured well the angst shared by so many whose livelihoods depend on the value of human creativity:

“No matter how transformative LLM training may be, it’s hard to imagine that it can be fair use to use copyrighted books to develop a tool to make billions or trillions of dollars while enabling the creation of a potentially endless stream of competing books that could significantly harm the market for those books.”

For now, the fair use shield remains intact, at least in the context of generative AI training. But as AI becomes more competitive, more specialized, and more vertical, cracks may begin to form. Read together, the fourth factor focus in Kadrey and the first factor emphasis in Thomson-Reuters could spell tougher terrain for the next generation of AI. In the spirit of Kadrey and Bartz, broadly “horizontal” AI tools may continue to qualify for fair use protection. But as AI becomes more verticalized, it may be harder for AI companies to claim fair use when the copyrighted works they seek to emulate both fuel their models and define their outputs—and when the competing tools they seek to build are capable of displacing the entire market for those copyrighted works.

© 2026 Meeka J. Bondy, PLLC

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