A group of authors has filed a complaint against Nvidia. Nvidia is a company that makes good computer parts and artificial intelligence software. The authors say that Nvidia wanted to get into a library of books that people did not pay for, which is called Annas Archive. They claim Nvidia did this so they could use these books to make their language models better. Nvidias language models are, like computers that can understand and talk like people. The authors are upset because Nvidia used Annas Archive, which has a lot of books that were taken without permission. The complaint says that people who work at Nvidia found out about Annas Archive and something called Books3. They thought these were sources to get information from. They talked about getting fast access to a lot of material about 500 terabytes of it. The people in charge at Nvidia said it was okay to go with this plan even though some people warned them that the material might have been gotten illegally. The people who are suing say that Nvidia copied and used things that are copyrighted without getting permission to do so. They say Nvidia did this with Annas Archive and the copyrighted works, in it.

2) Who is. What datasets are named in this project. We need to know the people and the datasets that are part of this. The people. The datasets named are important to understand what is going on. We have to look at the people and the datasets named to get a picture. The datasets named and the people involved are crucial, in this.

Important. Information that show up in the reports and documents:

Annas Archive is like a library. It is known as a shadow library or shadow archive. This is because it collects books that people did not pay for and makes them easy to get.

The complaint says that Nvidia got in touch with Annas Archive. They wanted to be able to look at millions of books fast. Annas Archive has a lot of books that people can read for free which’s why Nvidia was interested, in it.

Books3 is a collection of books that people already knew about. It has a lot of scanned and copied ebooks from places like bibliotik. Books3 has been a part of lawsuits about copyrights for artificial intelligence before. There was a lawsuit earlier in 2024 where authors said that Nvidia used Books3 to train their NeMo system. Books3 is still important, in these kinds of cases.

The plaintiffs are groups of authors. These authors are writers. Some of these writers have sued intelligence companies before. Now they are adding things to their complaint. They are including allegations and datasets in the complaint, about the artificial intelligence companies.

A lot of news outlets found out about the story. Then added more details, from court papers and secret information that was leaked. The court papers and leaked information are what most people know about the allegation.

3) What the plaintiffs say took place in detail

According to the amended complaint and reporting:

The team at Nvidia that looks at data strategies took a look at Annas Archive. They thought about getting access to a collection of books. We are talking about a lot of books around 500 terabytes. They even talked about paying for fast access to these books. It seems that the people on the team knew that using this dataset could get them into trouble with the law. They still wanted to see how useful it could be for them. Nvidias Data Strategy Team looked at Annas Archive as a dataset. They discussed getting access, to this big collection of books.

The people who are complaining say that Nvidia did this on purpose. They think Nvidia knew what they were doing. It was not a mistake. Nvidia took some text that people had written and used it without asking. The people who wrote this text are saying that Nvidia did this to save money and to be better than companies. They say Nvidia wanted to use the things about the authors work to help themselves. The complaint is about Nvidia using the authors work in a way that’s not fair. Nvidia is a company that makes computer parts. They are in trouble because of what they did, with the authors work.

The plaintiffs want the court to help them because of copyright infringement. They want the people who did this to pay them money for the damages they caused. The plaintiffs also want the court to stop the people from doing this which is called injunctive relief. They might also want the people to give back any money they made from doing this. They might want other things to happen.

The lawsuit also says that Nvidia used the Books3 dataset, which they had said before. Now they are saying it again with more details.

4) Nvidia’s likely or reported responses (and prior positions)

Nvidia and a lot of Artificial Intelligence companies have said that the way they use training data is okay. They say this is because training is a way to look at information and statistics. So they think it is use. When there were lawsuits about Books3 and other datasets in 2024 Nvidia said that when they train models they are just finding patterns in the numbers. They are not copying anything that is copyrighted. Nvidia says that what they are doing is changing the way the information is used so it is okay. A lot of companies in the Artificial Intelligence industry are using this argument to defend themselves. Nvidia and these other Artificial Intelligence companies are all saying the thing, about training data.

So the question is can a company get pirated content on purpose or pay to get it. This is different from using copyrighted works for training. The new issue is about where the company got the content, from and if they knew it was illegal. This can affect what the courts think about how bad the companys behavior was and what kind of punishment they should get. It can also affect if the company can say they were just using the content for a reason. The news says Nvidia does not say they paid money or made a deal with Annas Archive.

5) The legal framework — copyright + fair use in the U.S. (concise primer)

So you want to figure out this lawsuit. There are two things you need to know:

first what copyright actually protects and second how they decide if fair use is okay.

A) Copyright is a deal, for authors. They have the right to make copies of their work and share it with others. They can also make things based on what they wrote. If someone else wants to copy what an author wrote they usually need to ask for permission. If they do not ask they might be doing something unless there is a good reason they do not need to ask. Authors want to control what happens to their work so they have these rights to help them do that. Copyright helps authors keep control of their writings.

B) Fair use, which is found in the law at 17 U.S.C. §107 Is a defense that is based on the facts of a situation.

It is decided by looking at four factors that are stated in the law and these factors are used to evaluate fair use.

Fair use is evaluated on these factors to determine if it applies in a case.

The law looks at these four factors to decide if fair use is a valid defense and fair use is a concept, in copyright law.

Purpose and character of the use (commercial vs. noncommercial; transformative or not).

The kind of work that is copyrighted is important. If the work is fictional and really creative it gets protection. This means that things like stories and art that are very imaginative will have copyright laws to help keep them safe. The nature of the copyrighted work like if it is a story is a big factor, in how much protection it gets.

Amount and substantiality of the portion used.

What happens to the market for the work when someone uses it. Does it hurt the market for the work or does it make the original work less valuable. The effect on the market, for the work is important to consider.

Courts look at these things when they make a decision. No one thing is the important. When it comes to training intelligence companies say that using the information is okay because the machines are learning from it not just copying it. The machines are finding patterns in the information they are not just replacing what the person wrote. On the hand authors say that the machines are really just taking the good parts of what they wrote and using them for themselves. The authors think that the artificial intelligence models can do the job as them, which is not fair. Courts have to think about intelligence and how it uses the work of authors. Artificial intelligence is an issue and authors are worried, about their work being used by artificial intelligence models.

6) Things that have happened before in court and important decisions made by judges far which are precedents and relevant judicial decisions, like precedents and relevant judicial decisions that help guide future cases.

AI copyright litigation has given us decisions so far which means we do not have a clear AI copyright law that everyone agrees on yet:

Meta (2025). A federal judge made a decision in one case that copying books to train an intelligence system like Meta could be considered fair use. The court agreed with Meta on some parts of the fair use defense in this case. However the outcome of these cases can be different depending on where they’re heard and the specifics of each case. Meta is still dealing with these types of cases. The results are not always the same, for Meta.

Anthropic. A judge said that using pirated content might be okay in some situations but only if it is really specific. However copying pirated stuff to make the main dataset, for Anthropic is probably not allowed. The courts are now trying to figure out how and why the data was used for Anthropic. They have to look at each case because the facts are different each time.

There are cases where the authors are taking OpenAI, Meta, Anthropic and now Nvidia to court. These cases are happening in different federal courts. The results of these cases are not the same. It is possible that higher courts, like appellate courts will have to get involved before we know what the rules are, for OpenAI, Meta, Anthropic and Nvidia.

The main thing to consider is that there are examples on both sides of this issue. Judges really look at the facts of each case carefully. They want to know how the data was collected and how it was used. They also consider whether the use of the data was transformative meaning it was used in an different way. Additionally judges think about whether the person who is being accused did anything, on purpose, which is called acting. The judges examine all these things when they are looking at the facts of the case especially when it comes to the data and how it was used.

7) The Annas Archive allegation is important, for a couple of reasons. It matters because of the law. Also because of the facts. The Annas Archive allegation is something that people should pay attention to. The Annas Archive allegation has a lot to do with what’s legal and what is true. So the Annas Archive allegation is really important when we talk about the law and the facts of the case. The Annas Archive allegation is something that affects the situation.

There are two reasons that are connected to each other. These two reasons are linked. The first reason is related to the reason. The two linked reasons are important to consider.

Sourcing and intent. If the complaint says that Nvidia looked for a website that they knew was illegal and paid for access even after they were told it was not allowed that could mean Nvidia did something wrong on purpose. This could make them have to pay money and might make a judge want to stop them from doing something. It makes a difference if some information gets into a model by accident when it is looking at lots of websites or if Nvidia specifically goes to a website that has things and gets the information, from there. Nvidia is the one that did this. It is important to look at what Nvidia did.

Scale and content. There are hundreds of terabytes of books including books that were published recently and these books are very valuable. They also have a lot of content that courts really care about protecting. If we use this content from the books to pre-train models and it can be proven that we directly copied the books instead of just finding patterns in them then this could be a problem, for a fair use argument. The books are what is important here and using the books in this way could hurt our case. The expressive content of the books is what the courts want to protect and using the books to -train models could be seen as not being fair use of the books.

8) Let us talk about the realities of this thing. What does it really mean when we say that something is trained on books.

The term “training on books” is used a lot. What does it actually mean for the technical side of things.

We need to understand what training on books means for the realities of this thing.

This is important because training on books is a part of how some things work, like machines that can understand human language and training on books is what makes them able to do that.

So when we talk about training on books we are talking about the realities of how machines can learn from the information that is, in books.

It is useful to think about how modelsre trained and what the models actually produce. This means we should look at how the training of models happens and then what kind of output the models give us. The training of models and the output of models are two things so it helps to separate the training of models, from the output of the models.

The training process for a Large Language Model is really about reading an amount of text. This helps the model to learn and understand how words and ideas are connected. The model does not store books in its memory. Instead it learns to recognize patterns in language like how words sound and what they mean. This is important, for companies that make these models because they can say they are not copying books they are just learning from them. The model is trying to capture the way people write and speak so it can do things like answer questions and generate text. This is the point that companies use to say they are not doing anything wrong when they use these models.

The risk of reproduction is a deal. Models can copy things word for word especially if they are not common or if they have seen them times before. This is a problem if a model can remember things that people have written and repeat them back. For example if a model can recall parts of something that is copyrighted that is not okay. Courts will pay attention to see if the model can reproduce substantial parts of things that people have written and own the rights to, like copyrighted works.

Sourcing matters. This is because where the material comes from is important. If the material was part of a collection like Books3 or Annas Archive that is one thing.. If the material was just found on public web pages that is something else. If a company gets material from a source that’s not legal like a pirated source, that looks very bad for the company. It looks worse than if the material was just found by accident. This is because getting material from a pirated source makes it seem like the company did it on purpose. The company looks like it intentionally went out and found the copyrighted material. Sourcing from sources is a big problem, for defendants. It is a problem because it shows that the company made a choice to use the copyrighted material.

9) Some possible legal theories that the plaintiffs will probably try to use include

* The plaintiffs will try to say that something was not done correctly

* The plaintiffs will argue that they were treated unfairly

* The plaintiffs will claim that their rights were not respected by the people involved

The plaintiffs will use these legal theories to try to make their case stronger, in court. The plaintiffs will press these theories to get what they want.

People who are suing the plaintiffs will probably go after claims that have some things in common. The plaintiffs will try to make different arguments and some of these arguments will overlap with each other the plaintiffs will make these overlapping claims.

The main issue here is that someone is copying the work of the plaintiffs without permission. This is called copyright infringement. It means they are taking something that does not belong to them and using it or sharing it with others. If the complaint says that the copying was done to build training data then this is the problem that needs to be addressed. The plaintiffs works are being used in a way that they did not agree to which’s the core of the issue.

So if Nvidia helped spread or let people get to stolen content, the people who are suing can say that Nvidia is also responsible for what happened with the pirated content. They may try to hold Nvidia liable for the actions of people, like the ones who actually stole the content. This is because Nvidia may have played a role in helping those people get the stolen content to others.

If someone does something on purpose that’s against the law, like willful infringement and messages or communications show that they knew they were getting something from an illegal source then the people who were hurt by this the plaintiffs may try to get more money because of this willful infringement. They may also try to get remedies because of the willful infringement.

The person who is suing wants some things to happen. They want money because of the damages. They also want the court to stop the people from using their work to train. The person who is suing might also want the other people to tell them about the datasets or model checkpoints that they made using their work. This will depend on what they find out during the discovery process. The person who is suing wants to know about the datasets or model checkpoints that were made using their work and they might want the other people to hand them over.

10) What Nvidia will probably say to defend themselves the defenses that Nvidia will use

Anticipated defenses:

Fair use is when something is used in a way like, for training and it does not take the place of the original thing. This is because the models are just learning from patterns they are not meant to replace books. A lot of companies that make technology have used this idea to defend themselves in court. Fair use is a concept and it says that the training is transformative meaning it changes the way we use something and it is non-expressive meaning the models learn patterns and that is all they do not replace books.

Nvidia might say they never paid for or never got the Annas Archive materials. Now people are saying that there is no proof that they paid. The company could say they never really talked about using Annas Archive or they could say that the talks were just to see what was possible. Nvidia could deny that anything actually happened with Annas Archive.

Nvidia says it did not directly copy the plaintiffs work in a way that’s against the law. The company may claim that it did not make copies of the plaintiffs work without permission, which would be considered wrong. Nvidia may argue that it did not do anything to hurt the plaintiffs rights by copying their work word for word. The issue here is, about Nvidia and the plaintiffs work and Nvidia says it did not copy the plaintiffs work in a way.

The people involved in the case will also argue about discovery and evidentiary issues. The plaintiffs have to prove that specific copyrighted works were used to train the system. They also have to show how the use of these copyrighted works is connected to things that were produced or, to the harm that was caused by the discovery of copyrighted works. The plaintiffs must link the use of copyrighted works to specific outputs or harms that resulted from the use of these copyrighted works.

11) What earlier court decisions will mean for this case of earlier rulings and how these earlier rulings could affect the outcome of this case of earlier rulings. The thing is, earlier rulings can really shape what happens in this case of rulings.

The situation, with AI and copyright law is not really clear. So this case will be affected by what courts have said but it will not be decided by those things alone. The AI and copyright law situation is still being figured out.

If courts keep saying it is okay to use a lot of things for free because it’s transformative then companies like Meta will have a better chance in court. On the hand if judges say that companies cannot use things that they know are pirated then the people who are suing will have a better chance.

Recent decisions from judges and opinions from district courts and also appeals that might happen show that judges do not all agree on the facts of use and pretraining for companies, like Meta. Judges are looking at the facts of use and pretraining very closely.

Judges take a look at what policies mean for people. Some courts think that using intelligence to train computers is good for the public and helps us come up with new ideas. At the time they have to think about the rights of the people who created the original work. The judges have to weigh the importance of intelligence training, against the rights of authors. This balance is very important. It will be a key issue here. Artificial intelligence training is a deal and the judges will have to consider it carefully.

12) What will happen to the industry if the people who are suing win or if the evidence they present is really convincing and people believe it. The industry will likely see some changes if the plaintiffs are successful or if the evidence is persuasive. This could affect a lot of companies and people who work in the industry. The broader industry implications are important to think about because they can have an impact on the future of the industry. If the plaintiffs prevail or if the evidence is persuasive it will be interesting to see how the industry responds and what changes are made as a result. The broader industry implications will probably be significant. Could lead to some new rules or regulations for companies, in the industry.

If the people who are suing can prove that Nvidia knew it was getting stolen information and used that stolen information to train its models there are some things that could happen to Nvidia. The company could get in trouble for using the stolen information. Nvidia could face problems because it used the stolen information to train its models. This is a deal, for Nvidia.

The artificial intelligence industry needs to be more careful, about where it gets its data from. This means companies have to keep an eye on where their data is coming from. They need to check the people they buy data from carefully. Companies should also use data that they have paid for.

Companies may start doing checks to make sure they know where their artificial intelligence data is coming from. They will have to be careful when they get data to make sure it is good.

Publishers and authors might want money for licensing. This means they could ask for fees to use their work in training datasets.. They could create special rules for people to follow when they use their work. This would make it more expensive to develop models. It would also help companies with a lot of money because they would be the only ones who could afford to pay these fees. The cost of model development would go up. Smaller companies would have a harder time paying for it. Publishers and authors would get money from licensing and this would change the way companies make and use models.

Model limitations or content filtering is something companies do to avoid getting in trouble with the law. They take out types of content from the things the model learns from. They also try to stop the model from memorizing things and make rules, about how it can be used. This way firms can reduce the risk of something going wrong. Model limitations or content filtering helps firms to be safe.

People who make rules might want rules about where companies get their training data from. They might also want companies to be more open, about where their data comes from. This could even lead to laws that say what companies can and cannot do with their data.

Litigation chill versus clarity. This is a deal. In the term companies might stop or slow down training on big collections of data until they know what the law says.

They do not want to get in trouble.

In the term the decisions made by courts, especially the higher courts will decide what happens with fair use for pretraining.

These decisions could make it clear that fair use is okay for pretraining or they could make it so that companies cannot use it much.

This will affect companies that use litigation and clarity and fair use for pretraining of data like the litigation chill, versus clarity issue.

13) Things that companies can do now to reduce problems and make things safer for themselves which is called risk mitigation and some practical remedies that companies might adopt now for risk mitigation include being prepared and having a plan, in place to deal with any issues that might come up and these practical remedies that companies can use for risk mitigation are very important.

No matter what happens with the lawsuit companies can take some steps:

Data provenance systems are really good at keeping track of where the data comes from. They make logs of dataset sources and all the other information that comes with it like when it was brought in and what rules it has to follow. This includes things like contracts and licences, for the data. Data provenance systems do a job of keeping all this information straight so we can always look back and see where the data came from and what we are allowed to do with it.

Licensing programs are an idea. We can work with publishers to get licenses. This means we can buy a lot of licenses at the time and it will be cheaper.. We can make a special fund to pay authors for their work. The licensing programs will help us deal with publishers and authors in a way. We will negotiate with publishers to get the deal, for the licensing programs. The licensing programs will also make sure that authors get paid for what they do.

Filtering & deduplication — scrub known pirated sources and aggressively filter or remove copyrighted books unless licensed.

When it comes to training methods that’re different people use made up or licensed collections of things. They also try to give importance to things that are copyrighted and very valuable. This is what differential training approaches are all, about. They use synthetic or licensed corpora. They reinforce weighting less on high-value copyrighted content.

We need to have transparency and ways for people to opt out. This means that people who own the rights to something should be able to say what they do not want to be included. We should also have a way to fix any problems that come up. This is what transparency and opt-out mechanisms are about giving rightsholders the power to identify works they want to exclude and then giving them a way to make things right through a remediation process, for rightsholders.

Taking these steps can help reduce the risk and the backlash from the public. The legal risk and public backlash are things that companies want to avoid. If companies keep getting sued then these steps may become what everyone, in the industry does. This is because companies will want to follow the steps to reduce the risk and public backlash.

14) What to watch next — immediate legal and news milestones

Watch for:

When people take someone to court they have to share information with each other. The people who are suing called the plaintiffs will show their evidence. The people being sued, called the defendants will then. Try to get the case thrown out. Sometimes important information comes out during this process like emails, messages, from Slack and records of things that were bought.

The judge makes decisions on motions to dismiss or summary judgment. These decisions will help figure out what the legal issues are and give us an idea of what the court thinks about the fair-use defense. The fair-use defense is a part of the case so the judges rulings on these motions will be very important. They will show us how the court feels about the fair-use defense and what the court’s likely to do with it. The fair-use defense and the judges rulings, on it will help us understand how the case is going to go.

When other court cases, about intelligence and copyright law go to a higher court the decisions made in those cases will affect what happens in the trials that follow. This is because the higher court decisions will set a standard for the lower courts to follow in artificial intelligence and copyright law cases.

What Nvidia says to the public is going to be very interesting. If Nvidia says they are doing something or if Nvidia says they are not doing something it will tell us a lot. We will also learn a lot from Nvidia if Nvidia decides to change the way they do things. The things Nvidia says and the things Nvidia does will show us what is really going on with Nvidia.

15) Balanced assessment — strengths and weaknesses of plaintiffs’ case

Strengths for plaintiffs:

The fact that Nvidia allegedly bought things directly from a known pirate library, which is called Annas Archive is a really important fact. It shows that Nvidia did things on purpose not by accident. This can influence what judges decide about whether Nvidia did things, on purpose or not and if they should get treatment. Nvidias behavior looks deliberate when they deal with Annas Archive, a known pirate library, which can affect the judges decisions on these issues.

When the discovery process finds communications that clearly show approval and intent the plaintiffs get an advantage in settlement talks or when they ask for a summary judgment. This means the plaintiffs have a chance of getting what they want because they have the internal communications from the other side to back them up. The internal communications are like evidence that the plaintiffs can use to prove their point. This can be very helpful in the settlement talks or when they ask for a summary judgment. The plaintiffs are looking for communications that clearly show what the other side intended to do and if they find this it can be very useful, for them.

Weaknesses for plaintiffs:

People take things to court. Sometimes the court says it is okay to use something for model training because it is fair use. The person who is suing has to prove that the use was not about numbers and that it actually hurt the market. The law is not really clear. It depends on the situation with the model training. The courts look at each case, with model training. Decide if it is fair use or not.

When people sue someone for using their copyrighted work they need to show that the other person actually used their work in a way that hurt them. They have to prove that the person who used their work did something like copying it or selling it and that it caused them real harm. This can be really hard to do especially when the case is, about someone using their work to train a computer model. The people who are suing have to show that the persons actions caused them to lose money or hurt their business in some way. This is not easy to prove. It can be very complicated.

16) Policy and ethical considerations beyond the courtroom

People who create things think that others are making money from their work without giving them anything in return. We need to think about what’s good for everyone when it comes to new ideas but we also have to consider how creators of moral economy will make a living. The moral economy is important because it is about being fair, to the people who come up with things.

When we talk about transparency we have to think about trade secrets. Companies are saying that if they show everyone their training data it could give away the methods they use.. On the other hand transparency is really helpful for people who own the rights to something because it lets them make sure their rights are being respected. We need to find a way to make policies that balance these two things, transparency and trade secrets so that companies can keep their methods safe and rightsholders can still enforce their rights. Transparency is important, for rightsholders. Companies also need to protect their trade secrets.

Access and inequality is a problem. Smaller companies that make Artificial Intelligence or AI for short may have a time getting the data they need because it is too expensive to buy. This means that big companies that already have a lot of money like the ones that are already doing well will get even stronger if the cost of getting this data goes up. These big companies or incumbents will have a bigger advantage, over the smaller AI developers.

17) Likely short- and medium-term outcomes (reasonable scenarios)

Settlement is very likely. Lots of copyright cases get settled so people can avoid a long and difficult process and also avoid damaging their reputation. A settlement could mean that the companies involved make a deal to license something and they also have to change how they do things.

The court can make rulings. This means the court may let some claims go forward while stopping others. For example judges will often look really closely at the issues. They might say it is okay to look into where something came from. They will not let you look into the bigger picture of damages. The court does this by looking at each issue one by one like sourcing and deciding if that can go forward. Judges do the thing with damages and they decide if that can go forward too. Partial rulings are when the court makes decisions, on the claims of the Partial rulings and this is what the judges do.

The people who lose the case can ask for clarification later. This means that whichever side does not win on the legal points can appeal. The appellate decisions that are made on will have a big impact, on the industry for a long time.

18) Takeaway. This is important for people who read this and, for the Artificial Intelligence field because it affects what the Artificial Intelligence field is doing and what people who read this are learning about the Artificial Intelligence field. The Artificial Intelligence field is a deal and people who read this should care about what is happening in the Artificial Intelligence field. The Artificial Intelligence field is. The Artificial Intelligence field is going to keep changing so people who read this should pay attention to the Artificial Intelligence field.

This allegation is important for three reasons:

This changes the discussion from the question of whether training used copyrighted words to the specific question of where companies get their training information. The intention behind getting this data and how it is obtained are very important, from an moral standpoint. Companies sourcing training data is what really matters now.

This thing could really change the way things are done in the industry. If it is proven that big developers are looking for pirated corpora then companies will have to do things. They will have to get better at licensing and checking where things come from. Companies will also have to be more open and honest, about what they’re doing with corpora. The fact that major developers are seeking out pirated corpora could really push the industry to make some changes. Corpora will have to be handled in a careful and transparent way.

The law is going to help us understand what is allowed when we train Artificial Intelligence models. The decisions that courts make about Artificial Intelligence training will influence the rules that govern how copyright laws apply to Artificial Intelligence. This will affect how we use Artificial Intelligence for projects that change the original work. Over the year or two the court decisions and agreements that are made will likely change the way Artificial Intelligence model training and content creation work and how much they cost. This will have an impact on Artificial Intelligence model training and the people who create content, for Artificial Intelligence models.

The claim that Nvidia tried to buy a lot of pirated books, which’s hundreds of terabytes of Nvidia data is a big deal if it is true. This would be a step up in the fight over Nvidia and how Nvidia trains its generative AI.. Nvidia will have to go through the legal process. This means Nvidia will have to deal with facts and discovery and legal tests like fair use analysis to figure out what happens to Nvidia. We should expect Nvidia to do a lot of work and maybe Nvidia will settle and the court will give guidance a little at a time rather than making one big decision, about Nvidia tomorrow. Meanwhile, the industry is likely to tighten sourcing practices, and policymakers may accelerate efforts to clarify rules for data use in AI.