The AI Copyright Hype: Legal Claims That Didn’t Hold Up

Over the past year, two dozen AI-related lawsuits and their myriad infringement claims have been winding their way through the court system. None have yet reached a jury trial. While we all anxiously await court rulings that can inform our future interaction with generative AI models, in the past few weeks, we are suddenly flooded by news reports with titles such as “US Artists Score Victory in Landmark AI Copyright Case,” “Artists Land a Win in Class Action Lawsuit Against A.I. Companies,” “Artists Score Major Win in Copyright Case Against AI Art Generators”—and the list goes on. The exuberant mood in these headlines mirror the enthusiasm of people actually involved in this particular case (Andersen v. Stability AI). The plaintiffs’ lawyer calls the court’s decision “a significant step forward for the case.” "We won BIG," writes the plaintiff on X. 

In this blog post, we’ll explore the reality behind these headlines and statements. The “BIG” win in fact describes a portion of the plaintiffs' claims surviving a pretrial motion to dismiss. If you are already familiar with the motion to dismiss per Federal Rules of Civil Procedure Rule 12(b)(6), please refer to Part II to find out what types of claims have been dismissed early on in the AI lawsuits. 

Part I: What is a motion to dismiss?

In the AI lawsuits filed over the last year, the majority of the plaintiffs’ claims have struggled to survive pretrial motions to dismiss. That may lead one to believe that claims made by plaintiffs are scrutinized harshly at this stage. But that is far from the truth. In fact, when looking at the broader legal landscape beyond the AI lawsuits, Rule 12(b)(6) motions are rarely successful.

In order to survive a Rule 12(b)(6) motion to dismiss filed by AI companies, plaintiffs in these lawsuits must make “plausible” claims in their complaint. At this stage, the court will assume that all of the factual allegations made by the plaintiffs are true and interpret everything in a way most favorable to plaintiffs. This allows the court to focus on the key legal questions without getting caught up in disputes about facts. When courts look at plaintiffs’ factual claims in the best possible light, if the defendant AI companies’ liability can plausibly be inferred based on facts stated by plaintiffs, then the claims will survive a motion to dismiss. Notably, the most important issues at the core of these AI lawsuits—namely, whether there has been direct copyright infringement and what may count as a fair use—are rarely decided at this stage, because these claims raise questions about facts as well as the law. 

On the other hand, if the AI companies will prevail as a matter of law even when the plaintiffs’ well-pleaded claims are taken as entirely true, then the plaintiffs’ claims will be dismissed by court. Merely stating that it is possible that the AI companies have done something unlawful, for instance, will not survive a motion to dismiss; there must be some reasonable expectation that evidence can be found later during discovery to support the plaintiffs’ claims. 

Procedurally, when a claim is dismissed, the court will often allow the plaintiffs to amend their complaint. That is exactly what happened with Andersen v. Stability AI (the case mentioned at the beginning of this blog post): the plaintiffs’ claims were first dismissed in October last year, and the court allowed the plaintiffs to amend their complaint to address the deficiencies in their allegations. The newly amended complaint contains infringement claims that survived new motions to dismiss, as well as other breach of contract, unjust enrichment, and DMCA claims that again were dismissed.

As you may have guessed, including something like the “motion to dismiss” in our court system can help save time and money, so parties don’t waste precious resources on meritless claims at trial. One judge dismissed a case against OpenAI earlier this year, stating that “the plaintiffs need to understand that they are in a court of law, not a town hall meeting.” The takeaway: plaintiffs need to bring claims that can plausibly entitle them to relief.

Part II: What claims are dismissed so far?

Most of the AI lawsuits are still at an early stage, and most of the court rulings we have seen so far are in response to the defendants’ motions to dismiss. From these rulings, we have learned which claims are viewed as meritless by courts. 

The removal of copyright management information (“CMI,” which includes information such as the title, the copyright holder, and other identifying information in a copyright notice) is a claim included in almost all plaintiffs’ complaints in the AI lawsuits, and this claim has failed to survive motions to dismiss without exception. DMCA Section 1202(b) restricts the intentional, unauthorized removal of CMI. Experts initially considered DMCA 1202(b) one of the biggest hurdles for non-licensed AI training. But courts so far have dismissed all DMCA 1202(b) claims, including in J. Doe 1 v. GitHub, Tremblay v. OpenAI, Andersen v. Stability AI, Kadrey v. Meta Platforms, and Silverman v. OpenAI. The plaintiffs’ DMCA Section 1202(b)(1) claims have failed because plaintiffs were not able to offer any evidence showing their CMI has been intentionally removed by the AI companies. For example, in Tremblay v. OpenAI and Silverman v. OpenAI, the courts held that the plaintiffs did not argue plausibly that OpenAI has intentionally removed CMI when ingesting plaintiffs’ works for training. Additionally, plaintiffs’ DMCA Section 1202(b)(3) have failed thus far because the plaintiffs’ claims did not fulfill the identicality requirement. For example, in J. Doe 1 v. GitHub, the court pointed out that Copilot’s output did not tend to represent verbatim copies of the original ingested code. We now see plaintiffs voluntarily dropping the DMCA claims in their amended complaints, such as in Leovy v Google (formerly J.L. vs Alphabet). 

Another claim that has been consistently dismissed by courts is that AI models are infringing derivative works of the training materials. The law defines a derivative work as “a work based upon one or more preexisting works, such as a translation, musical arrangement, … art reproduction, abridgment, condensation, or any other form in which a work may be recast, transformed, or adapted.” To most of us, the idea that the model itself (as opposed to, say, outputs generated by the model) can be considered a derivative work seems to be a stretch. The courts have so far agreed. On November 20, 2023, the court in Kadrey v. Meta Platforms said it is “nonsensical” to consider an AI model a derivative work of a book just because the book is used for training. 

Similarly, claims that all AI outputs should be automatically considered infringing derivative works have been dismissed by courts, because the claims cannot point to specific evidence that an instance of output is substantially similar to an ingested work. In Andersen v. Stability AI, plaintiffs tried to argue “that all elements of [] Anderson’s copyrighted works [] were copied wholesale as Training Images and therefore the Output Images are necessarily derivative;” the court dismissed the argument because—besides the fact that plaintiffs are unlikely able to show substantial similarity—“it is simply not plausible that every Training Image used to train Stable Diffusion was copyrighted [] or that all [] Output Images rely upon (theoretically) copyrighted Training Images and therefore all Output images are derivative images. … [The argument for dismissing these claims is strong] especially in light of plaintiffs’ admission that Output Images are unlikely to look like the Training Images.”

Several of these AI cases have raised claims of vicarious liability—that is, liability for the service provider based on the actions of others, such as users of the AI models. Because a vicarious infringement claim must be based on a showing of direct infringement, the vicarious infringement claims are also dismissed in Tremblay v. OpenAI and Silverman v. OpenAI, when plaintiffs cannot point to any infringing similarity between AI output and the ingested books.

Many plaintiffs have also raised a number of non-copyright, state law claims (such as negligence or unfair competition) that have largely been dismissed based on copyright preemption. Copyright preemption prevents duplicitous state law claims when those state law claims are based on an exercise of rights that are equivalent to those provided for under the federal Copyright Act. In Andersen v. Stability AI, for example, the court dismissed the plaintiffs' unjust enrichment claim because the plaintiffs failed to add any new elements that would distinguish their claim based on California’s Unfair Competition Law or common law from rights under the Copyright Act.

It is interesting to note that many of the dismissed claims in different AI lawsuits closely mimic one another, such as in Kadrey v. Meta Platforms, Andersen v. Stability AI, Tremblay v. OpenAI, and Silverman v. OpenAI. It turns out that the similarities are no coincidence—all these lawsuits are filed by the same law firm. These mass-produced complaints not only contain overbroad claims that are prone to dismissal, they also have overbroad class designations. In the next blog post, we will delve deeper into the class action aspect of the AI lawsuits.