It was hard not to dismiss the headline posted by The Verge:  How AI copyright lawsuits could make the whole industry go extinct. The article summarizes a new Decoder podcast hosted by Nilay Patel, joined by Sarah Jeong to discuss the copyright lawsuits filed against generative AI developers. Most of the program is devoted to a discussion of fair use, which is reasonable because that’s likely how these cases will be decided. It’s clear that Patel and Jeong view copyright as a barrier to technological innovation, but when people trained in the law misrepresent the law as purely whimsical, it is counterproductive to the conversation.

I could critique nearly every segment in the podcast, but as that would be both long and tedious, I selected a few highlights for this post. Setting the more-hip-than-helpful tone of the program, Patel (who went to law school) describes fair use as a “vibes based” doctrine. Jeong (also law school) echoes the sentiment when she says that litigation against generative AI has “Napster vibes to it,” teeing up her thesis statement: “When Napster happened to the law, companies went bust; entire industries went bust; copyright changed forever in a way that was not great; it was an extinction level event; and AI has a similar thing going on there.” Here, Patel summarizes that Napster went to the Supreme Court—it did not—and that the Court “made some changes to copyright law.” Seriously? “Made some changes” is not how people with legal training talk about court rulings, even when they disagree with the outcome.

The next comment that caught my attention was Patel saying that “fair use is not deterministic” as a doctrine. He’s right, but in context, the listener will take him to mean that fair use is unpredictable to the point of capriciousness. Although a good attorney will demur to predict the outcome of any case, a thoughtful copyright expert is unlikely to agree that fair use findings are a “coin toss,” as Patel puts it. In fact, the choice of the word deterministic provokes the rebuttal that anticipating a fair use outcome is more accurately described as probabilistic, which is funny because that’s also how generative AI works.

If a defendant asks an attorney to handicap the likelihood of prevailing on fair use, the attorney’s response should be a reasonable prediction based on how closely the facts of the present case resemble fair use findings in the circuit of jurisdiction. Although Patel alludes to this analysis, he overlooks the fact that counsel could describe a probability outcome, which is precisely how a generative AI produces its outputs. If one prompts a visual AI to generate an image of a dolphin drinking a Slurpee, the output is the machine saying, “Based on the available data, this image is probably a dolphin drinking a Slurpee.” So, of all defendants, AI developers should grasp the nature of fair use case law.

Jeong echoes the idea that fair use considerations are erratic by alleging that the “Court changed copyright law after Napster,” referring to the Ninth Circuit’s 2001 finding that the P2P music filesharing platform was not shielded by fair use. Here, she argues that the Supreme Court’s fair use finding in the Sony “Betamax” case (1984) expressed a philosophical adaptation of copyright law to foster new technology but that this general view was reversed when the Ninth Circuit decided against Napster—and then when the Supreme Court ruled in 2005 that the filesharing platform Grokster could be liable for copyright infringement.

Although one cannot reasonably argue that ideology never skews the courts, Jeong elides the many factual and legal distinctions between the VCR and filesharing platforms and, by extension, the distinctions between those technologies and generative AI. Her declaration that “copyright was changed” after Napster and Grokster is unfounded, as the Court itself notes that Grokster was its second case considering contributory liability for copyright infringement—Sony being the first. Two cases, twenty-one years apart, addressing the same legal question presented by substantially different technologies is not a basis for claiming that the law was “changed forever” by the outcome in the latter case.

Holding the opinion that copyright stifles technological innovation does not excuse misrepresenting the courts as rolling dice to rule on fair use. For instance, in Grokster, the Court directly addresses the balance between copyright and technological innovation thus:

The more artistic protection is favored, the more technological innovation may be discouraged; the administration of copyright law is an exercise in managing the trade-off….The tension between the two values is the subject of this case, with its claim that digital distribution of copyrighted material threatens copyright holders as never before, because every copy is identical to the original, copying is easy, and many people (especially the young) use filesharing software to download copyrighted works.

Does that describe the technological function of the VCR? For those who’ve never used a VCR, the answer is No. The home video tape recorder, a relic of pre-internet life, functioned nothing like a filesharing platform, which facilitates mass copyright infringement on a global scale. Fair use is a fact-intensive inquiry, and “technology” is not a monolith. The leap from the VCR to generative AI is roughly the distance between the telegraph and the iPhone, and it is unhelpful, even irresponsible, to obscure so much factual detail behind a conversation about the courts’ alleged randomness on copyright and fair use.

Everything cited above was expressed in the first 5-6 minutes of the podcast. Tempted not to listen any further, I winced as both Patel and Jeong proceeded to make some astonishing remarks about the four-factor fair use test in regard to generative AI. Again, a couple of highlights stand out.

On factor two, nature of the work used, Patel says, “Factor two is whatever the judge thinks it is.” Then, a few seconds later, he says, “If the judge decides they don’t like the New York Times that day…” this will determine whether factor two tilts in the Times’s favor. NYT v. Open AI is before the Southern District of New York in the Second Circuit, which holds the largest trove of copyright case law of any circuit in the country—including several major fair use cases. If Patel or Jeong want to handicap the court’s findings based on that case law and then offer their own views of what they think is right, fine. But the implication that the court is just going to wing it is ridiculous.

Jeong does not push back on Patel’s coin-toss implication but says the “dial is in the middle” on factor two, which she reasonably (if not very clearly) argues because the Times contains both protectable expression and unprotectable factual material. But then comes the biggest spit-take in the program, when Jeong predicts that factor four, potential market harm to the work used, weighs against the AI developers because of the Supreme Court decision in Warhol. She states, “We have not seen that heavy an emphasis on factor four before.” Notwithstanding the fact that prior to the Campbell decision (1994), many experts would say that factor four was the most determinative factor in fair use jurisprudence, Warhol was unequivocally NOT a factor four case. As the opinion states:

In this Court, the sole question presented is whether the first fair use factor, “the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes,” §107(1), weighs in favor of AWF’s recent commercial licensing to Condé Nast. [emphasis added]

From her comments about Warhol, Jeong confuses the question of “substitutional purpose” with the question of “market harm substitution,” which are weighed under factors one and four respectively. It is true that where the court finds substitutional purpose, market harm substitution is more likely to be found, but as the opinion explains the distinction in one footnote in Warhol:  While the first factor considers whether and to what extent an original work and secondary use have substitutable purposes, the fourth factor focuses on actual or potential market substitution. They are two separate, albeit interdependent, questions. I do not know whence Jeong gets the idea that Warhol was a factor four case, let alone an unprecedented outcome in its emphasis of that factor.

Fair Use is a Fact-Intensive Inquiry

Generalizations like those articulated in the Decoder podcast sidestep the relevant facts about a given technology, what it does in context to legal questions, and why the technology may or may not be socially valuable. “Artificial intelligence” encompasses a wide range of development, some of which is promising, some of which is questionable, and all of which has been identified as potentially dangerous without proper oversight. As for generative AI in the creative industries, if Jeong is right that the copyright lawsuits pose an existential threat to those companies, so what? It is not clear that the world needs machines to make images of dolphins drinking Slurpees.

As discussed in this post, AI developers may have taken a gambler’s approach to fair use, and if their business plan included liability at the scale of mass copyright infringement, that’s a risk they chose to take. If any of those companies fail because of that liability, it will not be the result of whimsically applied or tech-hostile copyright law, or indeed the fault of the creators whose rights are infringed in the process of machine learning. Moreover, it is certainly not incumbent upon creators to abdicate their rights and get out of the way because “innovation” is happening. Fair use considerations in generative AI lawsuits may result in some novel opinions, but if influencers like Patel and Jeong are going to misstate case law and describe the courts as casinos, then one must wonder why they mention their legal credentials in the first place. After all, anyone can flip a coin.