{"id":124487,"date":"2025-06-27T10:03:38","date_gmt":"2025-06-27T18:03:38","guid":{"rendered":"https:\/\/xira.com\/p\/2025\/06\/27\/two-judges-same-district-opposite-conclusions-the-messy-reality-of-ai-training-copyright-cases\/"},"modified":"2025-06-27T10:03:38","modified_gmt":"2025-06-27T18:03:38","slug":"two-judges-same-district-opposite-conclusions-the-messy-reality-of-ai-training-copyright-cases","status":"publish","type":"post","link":"https:\/\/xira.com\/p\/2025\/06\/27\/two-judges-same-district-opposite-conclusions-the-messy-reality-of-ai-training-copyright-cases\/","title":{"rendered":"Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases"},"content":{"rendered":"
\"\"
<\/figcaption><\/figure>\n

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\u2019s likely fair use as transformative. Judge Vince Chhabria said it\u2019s 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\u2019t be right.<\/p>\n

The disconnect reveals something important: we\u2019re 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\u2019s a classic case of blind men and an elephant, with each judge touching one part of the problem and declaring that\u2019s the whole animal.<\/p>\n

I\u00a0just wrote about Judge Alsup\u2019s careful analysis<\/a>, 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\u00a0came out with a ruling that disagrees<\/a>.<\/p>\n

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\u2019s ruling was probably a win for AI innovation but a loss for Anthropic. Chhabria\u2019s is the opposite: a clear win for Meta, but potentially devastating for AI innovation generally.<\/p>\n

Chhabria\u2019s Flawed Market Harm Analysis<\/strong><\/p>\n

Chhabria\u2019s ruling seems to overweight (and, I think incorrectly predict) the \u201ceffect on the market\u201d aspect of the fair use analysis:<\/p>\n

\n

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\u2014without 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.<\/em><\/p>\n

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 \u201cfair use,\u201d which provides a defense to certain claims of copyright infringement, typically doesn\u2019t 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<\/em><\/p>\n<\/blockquote>\n

I find this entire reasoning extremely problematic, and it\u2019s why I mentioned in the Alsup piece that I don\u2019t think the \u201ceffect of the use upon the market\u201d should really be a part of the fair use calculation. Because\u00a0any<\/em>\u00a0type of competition can lead fewer people to buy a different work. Or it can inspire people to actually buy\u00a0more works<\/em>\u00a0because of more interest. Chhabria\u2019s example here seems particularly\u2026 weird:<\/p>\n

\n

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\u2019s Master of the Senate, because that book is at the top of so many people\u2019s 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.<\/em><\/p>\n<\/blockquote>\n

This is where Chhabria\u2019s reasoning completely falls apart. He admits in his own example that Robert Caro\u2019s biography would be fine because \u201cthat book is at the top of so many people\u2019s lists.\u201d But that admission destroys his entire argument: people recognize that a good biography is a good biography, and AI slop\u2014even AI slop generated from reading other good biographies\u2014is not a credible substitute.<\/p>\n

More fundamentally, his logic would make any learning from existing works potentially infringing.<\/p>\n

If you go to Ford\u2019s Theatre in DC, where Lincoln was shot and killed, you can actually see\u00a0a very cool tower of every book<\/a>\u00a0they could find written about Lincoln. Under Chhabria\u2019s reasoning, this abundance should have killed the market for Lincoln biographies decades ago. Instead, new ones keep getting published and finding audiences.<\/p>\n

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\u2019s analysis seems to argue that it would likely be so.<\/p>\n

\n

Or take magazine articles. If a company uses copyrighted magazine articles to train a model capable of generating similar articles, it\u2019s 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?<\/em><\/p>\n<\/blockquote>\n

This argument would be more compelling if the internet hadn\u2019t 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\u2019t destroyed the market for original journalism\u2014it\u2019s just competition. And, indeed, some of that competition can actually\u00a0increase<\/em>\u00a0the 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.<\/p>\n

So I don\u2019t find either of these examples particularly compelling, and am a bit surprised that Chhabria does. He does admit that other kinds of works are \u201cmurkier\u201d:<\/p>\n

\n

With some types of works, the picture is a bit murkier. For example, it\u2019s 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.<\/em><\/p>\n<\/blockquote>\n

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\u2019t seem to diminish the demand for new ones.<\/p>\n

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\u2026 and monetized in some form.<\/p>\n

So the entire premise of Chhabria\u2019s argument just seems\u2026 wrong.<\/p>\n

The Alsup vs. Chhabria Split<\/strong><\/p>\n

Chhabria also takes a fairly dismissive tone on the question of transformativeness. And even though he likely wrote most of this opinion before Alsup\u2019s became public, he adds in a short paragraph addressing Alsup\u2019s ruling:<\/p>\n

\n

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 \u201ctraining schoolchildren to write well,\u201d which could \u201cresult in an explosion of competing works.\u201d 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 \u201cis not the kind of competitive or creative displacement that concerns the Copyright Act.\u201d 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.<\/em><\/p>\n<\/blockquote>\n

Here we see the fundamental disagreement: Alsup thinks transformativeness is the key factor; Chhabria thinks market impact trumps everything else. Both can\u2019t be right, and the fair use four-factor test gives judges enough wiggle room to justify either conclusion.<\/p>\n

Chhabria does agree that training LLMs is transformative:<\/p>\n

\n

This factor favors Meta. There is no serious question that Meta\u2019s use of the plaintiffs\u2019 books had a \u201cfurther purpose\u201d and \u201cdifferent character\u201d than the books\u2014that it was highly transformative. The purpose of Meta\u2019s 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 \u201cto create a new platform that could be readily used by programmers\u201d). 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\u2019 books, by contrast, is to be read for entertainment or education.<\/em><\/p>\n<\/blockquote>\n

But he thinks market harm is more important\u2014a conclusion that would gut much of fair use doctrine if applied consistently.<\/p>\n

Also, while Alsup focused heavily on the unauthorized works that Anthropic downloaded and then stored in an internal \u201clibrary\u201d 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.<\/p>\n

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.<\/p>\n

The Plaintiffs\u2019 Spectacular Failure<\/strong><\/p>\n

Given all this, you might think that Chhabria ruled against Meta, but he did not, mainly because the crux of\u00a0his<\/em>\u00a0opinion\u2014that these AI tools will flood the market and diminish the incentives for new authors\u2014is so ludicrous that the plaintiffs in this case\u00a0barely even raised it as an issue<\/em>\u00a0and presented no evidence in support.<\/p>\n

\n

In connection with these fair use arguments, the plaintiffs offer two primary theories for how the markets for their works are affected by Meta\u2019s 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\u2019 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\u2019 books to matter, and the plaintiffs are not entitled to the market for licensing their works as AI training data.<\/em>\u00a0As for the potentially winning argument\u2014that Meta has copied their works to create a product that will likely flood the market with similar works, causing market dilution\u2014the plaintiffs barely give this issue lip service, and they present no evidence about how the current or expected outputs from Meta\u2019s models would dilute the market for their own works.<\/em><\/strong><\/p>\n

Given the state of the record, the Court has no choice but to grant summary judgment to Meta on the plaintiffs\u2019 claim that the company violated copyright law by training its models with their books.<\/em><\/p>\n<\/blockquote>\n

In short, the court\u2019s 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.<\/p>\n

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\u2019s a clear win for Meta, but potentially devastating precedent for AI development generally.<\/p>\n

What we\u2019re 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.<\/p>\n

This case isn\u2019t settled. Neither is the broader question of AI training and copyright. We\u2019re 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.<\/p>\n

Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases<\/a><\/p>\n

More Law-Related Stories From Techdirt:<\/strong><\/p>\n

Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases<\/a>We Have All Become Too Comfortable With Corruption<\/a>
More Than 90 Percent Of ICE Detainees Have Never Been Convicted Of Violent Crimes<\/a>
Trump NHTSA \u2018Investigates\u2019 Tesla Robotaxis Failing To Adhere To Basic Austin Traffic Laws<\/a><\/p>\n

The post Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases<\/a> appeared first on Above the Law<\/a>.<\/p>\n

\"\"
<\/figcaption><\/figure>\n

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\u2019s likely fair use as transformative. Judge Vince Chhabria said it\u2019s 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\u2019t be right.<\/p>\n

The disconnect reveals something important: we\u2019re 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\u2019s a classic case of blind men and an elephant, with each judge touching one part of the problem and declaring that\u2019s the whole animal.<\/p>\n

I\u00a0just wrote about Judge Alsup\u2019s careful analysis<\/a>, 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\u00a0came out with a ruling that disagrees<\/a>.<\/p>\n

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\u2019s ruling was probably a win for AI innovation but a loss for Anthropic. Chhabria\u2019s is the opposite: a clear win for Meta, but potentially devastating for AI innovation generally.<\/p>\n

Chhabria\u2019s Flawed Market Harm Analysis<\/strong><\/p>\n

Chhabria\u2019s ruling seems to overweight (and, I think incorrectly predict) the \u201ceffect on the market\u201d aspect of the fair use analysis:<\/p>\n

\n

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\u2014without 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.<\/em><\/p>\n

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 \u201cfair use,\u201d which provides a defense to certain claims of copyright infringement, typically doesn\u2019t 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<\/em><\/p>\n<\/blockquote>\n

I find this entire reasoning extremely problematic, and it\u2019s why I mentioned in the Alsup piece that I don\u2019t think the \u201ceffect of the use upon the market\u201d should really be a part of the fair use calculation. Because\u00a0any<\/em>\u00a0type of competition can lead fewer people to buy a different work. Or it can inspire people to actually buy\u00a0more works<\/em>\u00a0because of more interest. Chhabria\u2019s example here seems particularly\u2026 weird:<\/p>\n

\n

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\u2019s Master of the Senate, because that book is at the top of so many people\u2019s 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.<\/em><\/p>\n<\/blockquote>\n

This is where Chhabria\u2019s reasoning completely falls apart. He admits in his own example that Robert Caro\u2019s biography would be fine because \u201cthat book is at the top of so many people\u2019s lists.\u201d But that admission destroys his entire argument: people recognize that a good biography is a good biography, and AI slop\u2014even AI slop generated from reading other good biographies\u2014is not a credible substitute.<\/p>\n

More fundamentally, his logic would make any learning from existing works potentially infringing.<\/p>\n

If you go to Ford\u2019s Theatre in DC, where Lincoln was shot and killed, you can actually see\u00a0a very cool tower of every book<\/a>\u00a0they could find written about Lincoln. Under Chhabria\u2019s reasoning, this abundance should have killed the market for Lincoln biographies decades ago. Instead, new ones keep getting published and finding audiences.<\/p>\n

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\u2019s analysis seems to argue that it would likely be so.<\/p>\n

\n

Or take magazine articles. If a company uses copyrighted magazine articles to train a model capable of generating similar articles, it\u2019s 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?<\/em><\/p>\n<\/blockquote>\n

This argument would be more compelling if the internet hadn\u2019t 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\u2019t destroyed the market for original journalism\u2014it\u2019s just competition. And, indeed, some of that competition can actually\u00a0increase<\/em>\u00a0the 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.<\/p>\n

So I don\u2019t find either of these examples particularly compelling, and am a bit surprised that Chhabria does. He does admit that other kinds of works are \u201cmurkier\u201d:<\/p>\n

\n

With some types of works, the picture is a bit murkier. For example, it\u2019s 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.<\/em><\/p>\n<\/blockquote>\n

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\u2019t seem to diminish the demand for new ones.<\/p>\n

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\u2026 and monetized in some form.<\/p>\n

So the entire premise of Chhabria\u2019s argument just seems\u2026 wrong.<\/p>\n

The Alsup vs. Chhabria Split<\/strong><\/p>\n

Chhabria also takes a fairly dismissive tone on the question of transformativeness. And even though he likely wrote most of this opinion before Alsup\u2019s became public, he adds in a short paragraph addressing Alsup\u2019s ruling:<\/p>\n

\n

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 \u201ctraining schoolchildren to write well,\u201d which could \u201cresult in an explosion of competing works.\u201d 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 \u201cis not the kind of competitive or creative displacement that concerns the Copyright Act.\u201d 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.<\/em><\/p>\n<\/blockquote>\n

Here we see the fundamental disagreement: Alsup thinks transformativeness is the key factor; Chhabria thinks market impact trumps everything else. Both can\u2019t be right, and the fair use four-factor test gives judges enough wiggle room to justify either conclusion.<\/p>\n

Chhabria does agree that training LLMs is transformative:<\/p>\n

\n

This factor favors Meta. There is no serious question that Meta\u2019s use of the plaintiffs\u2019 books had a \u201cfurther purpose\u201d and \u201cdifferent character\u201d than the books\u2014that it was highly transformative. The purpose of Meta\u2019s 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 \u201cto create a new platform that could be readily used by programmers\u201d). 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\u2019 books, by contrast, is to be read for entertainment or education.<\/em><\/p>\n<\/blockquote>\n

But he thinks market harm is more important\u2014a conclusion that would gut much of fair use doctrine if applied consistently.<\/p>\n

Also, while Alsup focused heavily on the unauthorized works that Anthropic downloaded and then stored in an internal \u201clibrary\u201d 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.<\/p>\n

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.<\/p>\n

The Plaintiffs\u2019 Spectacular Failure<\/strong><\/p>\n

Given all this, you might think that Chhabria ruled against Meta, but he did not, mainly because the crux of\u00a0his<\/em>\u00a0opinion\u2014that these AI tools will flood the market and diminish the incentives for new authors\u2014is so ludicrous that the plaintiffs in this case\u00a0barely even raised it as an issue<\/em>\u00a0and presented no evidence in support.<\/p>\n

\n

In connection with these fair use arguments, the plaintiffs offer two primary theories for how the markets for their works are affected by Meta\u2019s 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\u2019 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\u2019 books to matter, and the plaintiffs are not entitled to the market for licensing their works as AI training data.<\/em>\u00a0As for the potentially winning argument\u2014that Meta has copied their works to create a product that will likely flood the market with similar works, causing market dilution\u2014the plaintiffs barely give this issue lip service, and they present no evidence about how the current or expected outputs from Meta\u2019s models would dilute the market for their own works.<\/em><\/strong><\/p>\n

Given the state of the record, the Court has no choice but to grant summary judgment to Meta on the plaintiffs\u2019 claim that the company violated copyright law by training its models with their books.<\/em><\/p>\n<\/blockquote>\n

In short, the court\u2019s 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.<\/p>\n

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\u2019s a clear win for Meta, but potentially devastating precedent for AI development generally.<\/p>\n

What we\u2019re 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.<\/p>\n

This case isn\u2019t settled. Neither is the broader question of AI training and copyright. We\u2019re 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.<\/p>\n

Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases<\/a><\/p>\n

More Law-Related Stories From Techdirt:<\/strong><\/p>\n

Two Judges, Same District, Opposite Conclusions: The Messy Reality Of AI Training Copyright Cases<\/a>We Have All Become Too Comfortable With Corruption<\/a>
More Than 90 Percent Of ICE Detainees Have Never Been Convicted Of Violent Crimes<\/a>
Trump NHTSA \u2018Investigates\u2019 Tesla Robotaxis Failing To Adhere To Basic Austin Traffic Laws<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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\u2019s likely fair use as transformative. Judge Vince Chhabria said it\u2019s likely infringing because of the supposed impact on the market. Both rulings came out of the Northern District […]<\/p>\n","protected":false},"author":3,"featured_media":124444,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-124487","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-above_the_law"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/xira.com\/p\/wp-content\/uploads\/2025\/06\/artificial-intelligence-6767502_1280-620x413-YVMefw.jpeg?fit=620%2C413&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/124487","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/comments?post=124487"}],"version-history":[{"count":0,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/124487\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media\/124444"}],"wp:attachment":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media?parent=124487"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/categories?post=124487"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/tags?post=124487"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}