Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases
from the blind-judges-and-the-ai-copyright-elephant dept
Within days of each other, two federal judges in the same district reached completely opposite conclusions about AI training on copyrighted works. Judge William Alsup said it’s likely fair use as transformative. Judge Vince Chhabria said it’s likely infringing because of the supposed impact on the market. Both rulings came out of the Northern District of California, both involve thoughtful judges with solid copyright track records, and both can’t be right.
The disconnect reveals something important: we’re watching judges fixate on their personal bugbears rather than grappling with the fundamental questions about how copyright should work in the age of AI. It’s a classic case of blind men and an elephant, with each judge touching one part of the problem and declaring that’s the whole animal.
I just wrote about Judge Alsup’s careful analysis, which found that training AI was likely protected as fair use, but building an internal digital library on unlicensed downloaded works was probably not. Before that piece was even published, Judge Vince Chhabria came out with a ruling that disagrees.
The summary: AI training is likely infringing. But here, the plaintiff authors failed to present evidence, and thus, their case against Meta is dismissed. Ironically, Alsup’s ruling was probably a win for AI innovation but a loss for Anthropic. Chhabria’s is the opposite: a clear win for Meta, but potentially devastating for AI innovation generally.
Chhabria’s Flawed Market Harm Analysis
Chhabria’s ruling seems to overweight (and, I think incorrectly predict) the “effect on the market” aspect of the fair use analysis:
Because the performance of a generative AI model depends on the amount and quality of data it absorbs as part of its training, companies have been unable to resist the temptation to feed copyright-protected materials into their models—without getting permission from the copyright holders or paying them for the right to use their works for this purpose. This case presents the question whether such conduct is illegal.
Although the devil is in the details, in most cases the answer will likely be yes. What copyright law cares about, above all else, is preserving the incentive for human beings to create artistic and scientific works. Therefore, it is generally illegal to copy protected works without permission. And the doctrine of “fair use,” which provides a defense to certain claims of copyright infringement, typically doesn’t apply to copying that will significantly diminish the ability of copyright holders to make money from their works (thus significantly diminishing the incentive to create in the future). Generative AI has the potential to flood the market with endless amounts of images, songs, articles, books, and more. People can prompt generative AI models to produce these outputs using a tiny fraction of the time and creativity that would otherwise be required. So by training generative AI models with copyrighted works, companies are creating something that often will dramatically undermine the market for those works, and thus dramatically undermine the incentive for human beings to create things the old-fashioned way
I find this entire reasoning extremely problematic, and it’s why I mentioned in the Alsup piece that I don’t think the “effect of the use upon the market” should really be a part of the fair use calculation. Because any type of competition can lead fewer people to buy a different work. Or it can inspire people to actually buy more works because of more interest. Chhabria’s example here seems particularly… weird:
Take, for example, biographies. If a company uses copyrighted biographies to train a model, and if the model is thus capable of generating endless amounts of biographies, the market for many of the copied biographies could be severely harmed. Perhaps not the market for Robert Caro’s Master of the Senate, because that book is at the top of so many people’s lists of biographies to read. But you can bet that the market for lesser-known biographies of Lyndon B. Johnson will be affected. And this, in turn, will diminish the incentive to write biographies in the future.
This is where Chhabria’s reasoning completely falls apart. He admits in his own example that Robert Caro’s biography would be fine because “that book is at the top of so many people’s lists.” But that admission destroys his entire argument: people recognize that a good biography is a good biography, and AI slop—even AI slop generated from reading other good biographies—is not a credible substitute.
More fundamentally, his logic would make any learning from existing works potentially infringing.
If you go to Ford’s Theatre in DC, where Lincoln was shot and killed, you can actually see a very cool tower of every book they could find written about Lincoln. Under Chhabria’s reasoning, this abundance should have killed the market for Lincoln biographies decades ago. Instead, new ones keep getting published and finding audiences.
If any of the authors of any of those books read any of the other books, learned from them, and then wrote their own take which did not copy any of the protectable expression of the other books, would that be infringing? Of course not. Yet Chhabria’s analysis seems to argue that it would likely be so.
Or take magazine articles. If a company uses copyrighted magazine articles to train a model capable of generating similar articles, it’s easy to imagine the market for the copied articles diminishing substantially. Especially if the AI-generated articles are made available for free. And again, how will this affect the incentive for human beings to put in the effort necessary to produce high-quality magazine articles?
This argument would be more compelling if the internet hadn’t already been flooded with free content for decades. Plenty of the internet (including this very site) consists of freely available articles based on our reading and analysis of magazine articles. This hasn’t destroyed the market for original journalism—it’s just competition. And, indeed, some of that competition can actually increase the market for the original works as well. If I read a short summary of a magazine article, that may make me even more likely to want to read the original, professionally written one.
So I don’t find either of these examples particularly compelling, and am a bit surprised that Chhabria does. He does admit that other kinds of works are “murkier”:
With some types of works, the picture is a bit murkier. For example, it’s not clear how generative AI would affect the market for memoirs or autobiographies, since by definition people read those works because of who wrote them. With fiction, it might depend on the type of book. Perhaps classic works of literature like The Catcher in the Rye would not see their markets diminished. But the market for the typical human-created romance or spy novel could be diminished substantially by the proliferation of similar AI-created works. And again, the proliferation of such works would presumably diminish the incentive for human beings to write romance or spy novels in the first place.
Again, even his murkier claims seem weird. There are so many romance and spy novels out there, with more coming out all the time, and the fact that the market is flooded with such books doesn’t seem to diminish the demand for new ones.
This all feels suspiciously like the debunked arguments during the big internet piracy wars about how downloading music for free would magically make it so that no one wanted to make music ever again. The reality was actually quite different: the fact that the tools for production and distribution became much easier and more democratic, meant that more music than ever before was actually produced, released, distributed… and monetized in some form.
So the entire premise of Chhabria’s argument just seems… wrong.
The Alsup vs. Chhabria Split
Chhabria also takes a fairly dismissive tone on the question of transformativeness. And even though he likely wrote most of this opinion before Alsup’s became public, he adds in a short paragraph addressing Alsup’s ruling:
Speaking of which, in a recent ruling on this topic, Judge Alsup focused heavily on the transformative nature of generative AI while brushing aside concerns about the harm it can inflict on the market for the works it gets trained on. Such harm would be no different, he reasoned, than the harm caused by using the works for “training schoolchildren to write well,” which could “result in an explosion of competing works.” Order on Fair Use at 28, Bartz v. Anthropic PBC, No. 24-cv-5417 (N.D. Cal. June 23, 2025), Dkt. No. 231. According to Judge Alsup, this “is not the kind of competitive or creative displacement that concerns the Copyright Act.” Id. But when it comes to market effects, using 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. This inapt analogy is not a basis for blowing off the most important factor in the fair use analysis.
Here we see the fundamental disagreement: Alsup thinks transformativeness is the key factor; Chhabria thinks market impact trumps everything else. Both can’t be right, and the fair use four-factor test gives judges enough wiggle room to justify either conclusion.
Chhabria does agree that training LLMs is transformative:
This factor favors Meta. There is no serious question that Meta’s use of the plaintiffs’ books had a “further purpose” and “different character” than the books—that it was highly transformative. The purpose of Meta’s copying was to train its LLMs, which are innovative tools that can be used to generate diverse text and perform a wide range of functions. Cf. Oracle, 593 U.S. at 30 (transformative to use copyrighted computer code “to create a new platform that could be readily used by programmers”). Users can ask Llama to edit an email they have written, translate an excerpt from or into a foreign language, write a skit based on a hypothetical scenario, or do any number of other tasks. The purpose of the plaintiffs’ books, by contrast, is to be read for entertainment or education.
But he thinks market harm is more important—a conclusion that would gut much of fair use doctrine if applied consistently.
Also, while Alsup focused heavily on the unauthorized works that Anthropic downloaded and then stored in an internal “library” and Chhabria goes into great detail about how Meta used BitTorrent to download similar (and in some cases, identical) copies of books, he leaves for another day the question of whether that aspect is infringing.
Indeed, in some ways, these two cases represent the old claim that the fair use four factors is just an excuse to do whatever the judge wants to do and then try to work backwards to try to justify it in more legalistic terms using those for factors.
The Plaintiffs’ Spectacular Failure
Given all this, you might think that Chhabria ruled against Meta, but he did not, mainly because the crux of his opinion—that these AI tools will flood the market and diminish the incentives for new authors—is so ludicrous that the plaintiffs in this case barely even raised it as an issue and presented no evidence in support.
In connection with these fair use arguments, the plaintiffs offer two primary theories for how the markets for their works are affected by Meta’s copying. They contend that Llama is capable of reproducing small snippets of text from their books. And they contend that Meta, by using their works for training without permission, has diminished the authors’ ability to license their works for the purpose of training large language models. As explained below, both of these arguments are clear losers. Llama is not capable of generating enough text from the plaintiffs’ books to matter, and the plaintiffs are not entitled to the market for licensing their works as AI training data. As for the potentially winning argument—that Meta has copied their works to create a product that will likely flood the market with similar works, causing market dilution—the plaintiffs barely give this issue lip service, and they present no evidence about how the current or expected outputs from Meta’s models would dilute the market for their own works.
Given the state of the record, the Court has no choice but to grant summary judgment to Meta on the plaintiffs’ claim that the company violated copyright law by training its models with their books.
In short, the court’s ruling in this case is that the winning argument is the impact on the market, while the plaintiffs in this case focused on the claim that the outputs of AI tools trained on their works was infringing. But, Chhabria notes, that argument is silly.
The irony is delicious: Chhabria essentially handed the authors a roadmap for how to beat AI companies in future cases, but these particular authors were too focused on their other weak theories to follow it. It’s a clear win for Meta, but potentially devastating precedent for AI development generally.
What we’re watching is how the fair use four-factor test can be manipulated to justify almost any conclusion a judge wants to reach. Alsup prioritized transformativeness and found for fair use. Chhabria prioritized market harm and found against it (even while ruling for Meta on procedural grounds). Both wrote lengthy, seemingly reasoned opinions reaching opposite conclusions from largely similar facts.
This case isn’t settled. Neither is the broader question of AI training and copyright. We’re still years away from definitive answers, and in the meantime, companies and developers are left navigating a legal minefield where identical conduct might be fair use in one courtroom and infringement in another.
Filed Under: competition, copyright, effect on the market, fair use, generative ai, llms, transformative, transformativeness, vince chhabria, william alsup
Companies: anthropic, meta
Comments on “Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases”
This seems relatively short sighted, based on the current capabilities of AI. If we’re setting broad precedent around AI/copyright, what happens if it can generate an equivalently good (or better) product? In the same way you wouldn’t be talking about AI based on pre-chatGPT 3 models. AI is the worst it will ever be, right now.
This isn’t even a hypothetical, there are already use cases where AI “slop” is good enough and interchangeable with human produced output. (And of course, not all copyrighted work has to be “good”. Some of them are just…average. Copyright does not only apply to the best works). And that also doesn’t get into things like auto-summaries.
Plenty have died on that model, too, without necessarily having new replacements pop up. It works in some cases. And while there’s a ton of original journalism that plays a role, I think it’s also fair to say they aren’t also filling all of the gaps. It’s not just competition from superior business models.
Eh, you’re kind of skimming over some very important details in that history, including things like Napster ultimately losing when it came to copyright. We’re not living in a copyright-free music utopia.
I don’t think it would. AI is genuinely unique in it’s ability to be a functional replacement for the thing it’s transforming, in a way that almost all fair use isn’t. And the reverse is not necessarily better. If you eliminate market harm as part of the test entirely, there are a lot of currently noninfringing uses that get harder to distinguish (like reproducing material for educational purposes or whatever). Having looser standards for noncommercial use is not a bad thing.
The four factor test is a bit loose, but ultimately it reflects the fact that you do kind of want to weigh the different factors on a case by case basis.
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No, because the longer this goes on the more of its training data is going to be made up of AI slop.
Re: Re:
That’s a fair point worth mentioning, although it’s not clear if that’s insurmountable. It’d be nice if this was the case- most of this hand-wringing would be moot. It seems pretty likely eventually they’ll find ways around it, either via better models, curation, or synthetic data. Nvidia’s already doing some pretty amazing stuff with their digital twins project.
And even in the most extreme case, they’ll still be able to use new techniques on stuff they’ve already collected, if nothing else.
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The very difficulty of judging market harm by AI is that many of the AI “slops” are indistinguishable from human-made work thanks to the AI training on copyrighted works that make AI better and better at masquerading.
While I think there should be legislation at all AI-generated content must be labeled, it’s anyway too early to tell whether the AI “slop” would have an actual impact on book sales.
That’s why the conflicting opinions among judges.
Speaking of this, my position is that AI training with copyrighted works is unethical and should be illegal except for AI deployments that do very limited purposes (e.g. translators, summary generators, grammar fixers).
The fair use arguments with AI are going absurd, by the way, by equating AI training with human learning we risk undermining humanity in the AI arms race (especially when they are aiming for “super-intelligence” that is far beyond what fair use has been legislated for).
The argument that AI content is going to compete with human authorship seems to come from a condescending and greedy perspective. People whose livelihood rests on the exploitation of creators to make money via copyright seem to think human audiences are all dumb and easily pacified by nonsense.
And sure, there are some people who aren’t so discerning in the content they consume. Those people are doomscrolling listicles that quote Reddit discussion threads. They aren’t the target audience for a lot of the content that book authors are looking to connect with.
The flood of AI slop that will hit the market has the potential to make authentic human creations more valuable and more noticeable. The readers will push back when the quality goes down and human authors can make a point of showing that they’re writing their own work.
But it seems like the media companies are basically saying their customers are idiots and they seem jealous that AI slop will distract their captured suckers from their own poor quality, but profitable en masse content.
And as long as AI slop isn’t able to be copyrighted, the purchase or leak of a single copy could ruin the market for those works because it would be legal to repost it for free somewhere else.
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The flood of AI slop doesn’t make human creations more noticable. It means you have to wade through hundreds or thousands of slop pieces in order to find one or two authentic books/paintings/whatever. People aren’t going to do that. I know I’m not. I’ll abandon the platform first. (dA, ArtStation, etc.)
Any supposition that human works retain their value rests on people being able to distinguish what’s AI and what isn’t. Right now we’re on the honor system, and that’s failing miserably. There’s too much money to be made by selling slop.
The fact that you can’t copyright slop isn’t going to make a difference when you can’t prove it’s slop in the first place.
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There will be a backlash. There will be platforms and publishers that require human authorship verification. There are already writers showing their writing process in online streams. There will be people who want to engage in the act of verification, so there will be curation lists and reviews. Hell, there will ironically be LLMs trained to identify LLM content.
Interestingly enough, there’s already a lot of poor quality content out there that is human authored. It’s already an issue that the market is flooded. Unfortunately, many authors have to build sales through social engagement rather than just standing on the quality of their work. An LLM won’t change that. And an LLM can’t engage.
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Keep in mind that the drawing process can still be faked by AI:
https://www.reddit.com/r/aiwars/comments/1auoy3x/so_apparently_ai_can_generate_videos_showing_the/
Re: Re: Re:2
Another evidence about “fake speedpaint” exists:
https://www.youtube.com/watch?v=E9vryBnKVz8
The term to search for is “fake speedpaint”.
Re: Re: Re:2
Notice that you had to reference a speedpainting video. I said online streams, as in live content. You can’t currently render AI video live at realistic quality (or if you can, it requires really expensive setups and if you have the money for that, why would you be faking a painting livestream?). Those videos also don’t show the artist. Those videos don’t show an artist responding to comments from the audience. We’re talking about Twitch and TikTok live streams, not YouTube videos. But also, you can’t use AI to convincingly fake a live video of typing and editing a manuscript while the author is narrating about the choices they’re making.
Re: Human Craft vs Mass-Produced Commodity
I dunno, I think what Chhabria is getting at is that certain types of works (e.g., fact-based books) could be relatively easily and cheaply produced by an LLM “factory” that a human couldn’t really compete with.
The analogy that your comment makes me think of is manmade furniture vs factory produced furniture. Some people will pay a premium for the manmade stuff because of the craft and knowing a human made it, but that is a pretty small market. By an large, in an industry that was once reliant on human production, people buy mass produced furniture made by fairly automated processes that leaves little room for the human artisan.
My main point is that a sufficiently accurate AI-produced text on something may, and probably will, be cheaper and faster to produce, while a human created version would be slow, laborious and expensive that would not differ greatly to much of the market.
Whether you view that as relevant to copyright and fair use, I think is another question and kind of depends on whether you think copyright is for the purpose of incentivizing human authorship, or simply authorship.
AI does not destroy the market
I think the authors failed to argue that their book sales were hurt by AI because they werent. Even if an AI could theoretically output large portions of a book, still no one uses it to opt out of buying the book. Judge Chhabria is mistaken to believe they could successfully argue that point because an argument isnt enough in any court. You need evidence too.
Re: The myth of market harm
Indeed. After all, even the publisher who won a lawsuit against the Internet Archive failed to show any market harm. You’d think they had an easy job, considering their lawsuit was about some 100 input books and very close copies/outputs thereof, as opposed to millions of books and billions of extremely dispersed (non)copies/outputs, but they didn’t even try, presumably because they knew they’d fail.
Perhaps this judge is hoping that the appeals court will send the case back, instructing the judge to make up any necessary evidence to reach the preordained conclusion, as was done in the Internet Archive case.
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It definitely does negatively impact some markets.
I have friends who supplement their low-income job with artwork commissions, specifically from tabletop gaming communities; they’ve seen a significant drop in requests thanks to slop machines like Dall-E. They’ve had to lower their prices and promote more-aggressively just to slow the descent.
Regardless, this slop degrades the online experience for everyone. It floods feeds with this low-effort, high-engagement garbage that at best annoys and at worst obscures better content. It might not collapse the market, but it definitely makes it worse for everyone to the benefit of few.
Feeling compromised
I’m no fan of copyright (certainly in its current condition) and using a law designed to promote progress to try and reign in technological development seems like using a hammer to drive a screw…
But…
I do wonder if these cases are happening at the wrong level. In the same way that we say liability for online content lies with the creator not the platform, should we be saying something similar about use of AI in creative fields.
So rather than having a go a ChatGPT at a corporate level, should we be having a go a the SUNO user who is flooding Spotify with hundreds of tracks in the style of [insert your favourite artist name here] and actually diluting the payment pool?
I dunno, it just feels like the wrong arguments are being presented which is might be why we’re getting conflicting steers.
But I have not given this argument a lot of thought so I am open to be persuaded otherwise.
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Generative AI is just another fancy tool. You should almost always go after the humans who abuse tools rather than the tool itself. Unless it’s an actual weapon.
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@MrWilson Why can’t the creators persue tool makers that encourage people to “abuse” then?
Not all generative AIs are neutral. Some of them do encourage abuse, like Midjourney and Suno. So it becomes close to Napster case where creators didn’t persue every pirate using Napster but aimed at Napster itself in order to break that pirate chain.
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Tools can’t encourage people to abuse. Tools have no agency or volition. People can do that. And even how a tool is configured doesn’t necessarily determine how a user will utilize it. This just speaks to your bias that you can’t imagine that an LLM is useful for anything other than “abuse.”
Define “encourage abuse.” Prove that software is intelligent, personified, and capable of expressing its will.
Napster was a tool. You could use it to share legal downloads. People just didn’t choose to do so. Again, blame the people, not the tool.
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MGM v. Grokster. When a tool maker advertises the illegal use of the tool, that tool maker is liable. It’s the ruling.
I’m not answering your unfounded assumption about I can’t “imagine that an LLM is useful for anything other than ‘abuse.'” Because that’s irrelevant. You are arguing like marijuana, which does have good medical uses, but many average people would just buy it for bad uses.
It’s the maker of the software that encourage illegal uses, not the software itself, dammit!
Do you have reading disabilities?
Re: Re: Re:3
Marijuana is legal where I’m at. So are alcohol and cigarettes and high fructose corn syrup and firearms and motor vehicles and computers and other products that can be abused. It’s highly relevant that you’re arguing from a perspective that assumes something should be banned because you personally can’t imagine a good use for it.
Do you have a reading comprehension and a memory problem? I quoted what you literally wrote. Look, I’ll quote it again with emphasis.
“Not all generative AIs are neutral. Some of them do encourage abuse, like Midjourney and Suno.”
You didn’t say, “some creators of generative AIs.” You said it was the “generative AIs” themselves that encourage abuse. Don’t lash out because of how you chose to phrase it.
Wrong!
The fact that the fourth factor counts more is argued by Chhabria very solidly by quoting a sentence of the Supreme Court: “(the fourth factor) is the single most important element of fair use”, Harper & Row. That’s also the reading of the US Copyrighted Office.
Human analogies
Back in the day, everyone was trying to patent common business processes as they were automated onto computers, and the general opinion ended up being that you can’t patent common practice just because it’s being done on a computer.
The AI transformation argument seems to be the reverse of that situation for copyright. Copyright holders are arguing that just because the transformation is done by a computer, it’s somehow copyright infringement, where a hypothetical human that absorbed the same copyrighted works and was somehow able to create new works by the thousands or millions would not be infringing. The volume of material generated shouldn’t be a consideration.
Plagiarism is not copyright infringement. If the expression of ideas is new and transformative, it may compete with the original works, but it is not necessarily copying from them.
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Correct, but nothing in this case is plagiarism, either. You see, plagiarism is specifically the representation of another person’s language, thoughts, ideas, or expressions as one’s own original work (thank you, Wikipedia), meaning work that would be copyright infringement if there were copyright protections in place, yet you or I could never hope to credit all of the various authors that have inspired our own school essays, etc., so failure to give credit to Stephen King, for example, is not plagiarism.
Do others find it disturbing when judges write opinions that read like their own amicus briefs? Chhabria makes it clear that he has decided the issue regardless of the case he is presented and the arguments of the litigants. Justice Thomas has done the same a couple of times over the years on the issue of Sec. 230. It’s awful, and more so for a Federal District Court Judge who will make initial decisions on his own.
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@BeerOnTap
Many pro-AI people would want to sway your opinion by presenting incomplete view of facts, even after the lawsuit. When people have a broader view on the social impacts of AI it could become clear that training AI with copyrighted material is unethical at the start (with a few exceptions).
News media will very likely just make the title “AI Training is Fair Use” without letting to grab onto the details of the judgement. You might be thankful that Judges wrote detailed opinion for you to read so that the issues of AI can be further debated in the public. I just don’t think that’s bad, as many people misunderstood the whole picture of AI and the copyright issue involved.
blow me off
It’s pretty nervy for Chhabria to accuse Alsup of “blowing off the most important factor” when they see each other in the courthouse lunchroom every day.
I agree that Chhabria appears to assume a lot of facts that are only in evidence in the minds of certain people on the S.F. peninsula, not so much in the real world.
I actually find this discrepancy super interesting. What I am inferring from this split is the philosophical perspective of whether non-human competition with human works of authorship is the type of “market harm” that would affect a finding of fair use. Alsup brushes off the market harm analysis as just being competition, while Chhabria takes the perspective that AI-originated competition on authorship is more than simple competition.
I don’t know what the correct view should be, but I do think there is something different about automated mass produced content than other people creating content. People should be expected to compete with people, but can we expect people to be able to compete with AI systems? And is that even a question relevant to copyright? Alsup seems to say no to the latter while Chhabria says yes.
The constitution says “To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries;” and this question seems to turn on whether one would seek to protect human-originated progress, or just progress regardless of the origin.
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Well, the question is about whether the competition is fair, not whether copyright should protect authors from competition. (The latter is the wrong simplification to the problem that would lead to wrong conclusion.)
The original copyright law was enacted to stop printing companies from blatantly copy books published by someone else. There, it assume authors have exclusive rights over reproduction of their books (although in practice it’s the book publishers that exercise those rights). It did kill competition, and yet the aim was to kill the unfair competition, rather than those that present no harm to the authors’ market.
And by following the spirit of this, it’s not hard to understand that the “fair use” statute considers the market factor being the most important of the four factors.
Claiming “fair use” by saying “it’s just market competition” won’t cut it for the purpose of copyright law. We need to address whether that “competition” was fair at all for the book authors. That’s why Judge Alsup made a flawed reasoning about the market factor while Judge Chhabria make a correct one.
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If we’re actually concerned about what’s fair for the book authors, then we need to examine and limit the abuses of the publishing industry and of other media companies. Again, you don’t have to side with a big corporation.