Posts

Posted on

The Mouse and the Model: the Disney-OpenAI Deal

The other shoe has finally dropped.

Today, December 11, 2025, OpenAI and Disney announced a partnership that essentially signals a marriage between generative AI and legacy media. Although some kind of deal was inevitable, the range and scope of this one are striking. Disney is sinking $1 billion into OpenAI for an equity stake and warrants, while simultaneously inking a three-year licensing deal.

The immediate result? OpenAI’s Sora and ChatGPT will legally ingest over 200 marquee characters from the Disney, Marvel, Pixar, and Star Wars vaults. We’ll see AI-generated Disney content on Disney+, and Disney employees will get enterprise-grade access to OpenAI’s tools. Notably, actor likenesses are off the table—a nod to the sensitivities of the recent labor strikes—but the direction of travel is clear. For more reporting, see the Verge.

Why it matters

Addressing the “Snoopy Problem“

AI companies and copyright industries are beginning to understand, and become reconciled to, the fact that neither side is going to score an absolute victory when it comes to the fair use issue for AI training. AI training that results in a model that learns from, but does not reproduce, the training data looks very likely to be upheld as fair use. Two recent cases held as much on summary judgement and this aligns with a line of precedent “nonexpressive use” cases that predate generative AI.

However, it’s becoming increasingly clear that it’s hard to train generative AI models to be really useful without some degree of memorization of the training data along the way. This is particularly problematic when it comes to copyrightable characters, because copyright protects characters more abstractly than most things. This is the well-known Snoopy problem (a term I coined in 2023).

Faced with this increasingly clear reality, it makes sense for consumer facing AI companies and entertainment Giants like Disney to think about licensing arrangements.

This deal signals a retreat from the fair use absolutism of early AI development. OpenAI and Disney have effectively priced the risk of memorization. Instead of spending the next decade in discovery arguing over pixel similarities, they are moving to a licensing regime. Disney gets paid and retains control; OpenAI gets legal certainty and the ability to serve the entertainment industry without looking over its shoulder.

Capital Crunch?

With competitors like Anthropic eyeing public listings, OpenAI’s decision to take strategic capital from a corporate giant like Disney may be telling. It suggests we are hitting a saturation point for traditional venture capital at the scale these foundation models require. It also hints that OpenAI sees more value in “smart money,” than in the volatility of the public markets. Disney isn’t just a piggy bank; it’s a hedge. By entangling itself with the world’s premier IP holder, OpenAI makes itself indispensable to the very industry that threatened to sue it out of existence. Or, I’m sure that’s the theory, whether it pans out that way remains to be seen.

The End of the Scaling Era?

Finally, this move also adds to the “Data Scarcity” thesis. The era of simply scraping the open web to make models smarter (2017–2025) might be over. The low-hanging fruit of the public internet has been picked, processed, recycled into synthetic data, and processed again, every which way you can imagine. To get better, and to stay ahead of open source rivals, companies like OpenAI are going to need access to data that no one else has. Google has YouTube; OpenAI now has the Magic Kingdom.

The Bottom Line

This is the template for the future. We are moving away from total war between AI and Content, toward a negotiated partition of the world. The tech companies provide the engine; the media giants provide the fuel. And for now, at least, both sides seem to think that’s a better outcome than leaving it up to a judge.

I wrote this blog post the morning the deal was announced, because it fits surprisingly well with a Law Review article I am writing, “The Snoopy Solution: How Fair Use and Licensing for Generative AI Can Coexist” based on a talk I gave at Yale last month.

Posted on

A handful of cherries does not make a sundae

Why content licensing cannot solve AI’s training-data problem

I have just published an article as part of the ProMarket symposium for the University of Chicago, the Booth School of Business, “The False Hope of Content Licensing at Internet Scale”

Although the article is not very long, I thought I would summarize my point even more briefly here.

AI developers have been on a shopping spree. Since mid-2023, OpenAI, Google, Anthropic and Meta have collectively spent hundreds of millions of dollars striking deals with publishers. OpenAI alone has inked agreements with everyone from the Associated Press to Condé Nast, gaining access to archives from The New Yorker, Vogue, The Wall Street Journal and dozens of other publications.

To many watching from the sidelines, these deals offer tantalizing proof that AI companies can—and should—pay for the content they consume.

However, the agreements grabbing headlines represent a tiny fraction of the data needed to train cutting-edge language models. Modern AI systems require trillions of diverse tokens scraped from across the internet—a scale and diversity that traditional licensing simply cannot reach.

To see why, read the full article: The False Hope of Content Licensing at Internet Scale

Posted on

Copyright Winter is Coming (to Wikipedia?)

Judge Stein’s Order Denying OpenAI’s Motion to Dismiss in Authors Guild v. OpenAI, Inc., No. 25-md-3143 (SHS) (OTW) (S.D.N.Y. Oct. 27, 2025)

A new ruling in Authors Guild v. OpenAI has major implications for copyright law, well beyond artificial intelligence. On October 27, 2025, Judge Sidney Stein of the Southern District of New York denied OpenAI’s motion to dismiss claims that ChatGPT outputs infringed the rights of authors such as George R.R. Martin and David Baldacci. The opinion suggests that short summaries of popular works of fiction are very likely infringing (unless fair use comes to the rescue).

This is a fundamental assault on the idea, expression, distinction as applied to works of fiction. It places thousands of Wikipedia entries in the copyright crosshairs and suggests that any kind of summary or analysis of a work of fiction is presumptively infringing.

A white walker in a desolate field reading Wikipedia (an AI Image by Gemini)

Copyright and derivative works

In Penguin Random House LLC v. Colting, the Southern District of New York found that defendant’s “The Kinderguide” series, which condensed classic works of literature into children’s books, infringed the copyrights in the original works despite being marketed as educational tools for parents to introduce literature to young children.

Every year, I ask students in my copyright class why the children’s versions of classic novels in Colting were found to be infringing but a Wikipedia summary of the plots of those same books probably wouldn’t be. A recent ruling in the consolidated copyright cases against OpenAI means I might have to reconsider.

The ruling

On October 27, 2025, Judge Stein of the Southern District of New York denied OpenAI’s motion to dismiss the output-based copyright infringement claims brought by a class of authors including David Baldacci, George R.R. Martin, and others.

OpenAI had argued, reasonably enough, that the authors’ complaint failed to plausibly allege substantial similarity between any of their works and any of ChatGPT’s outputs. It is standard practice in copyright litigation to attach a copy of the plaintiff’s work and the allegedly infringing work, but the court held that “the outputs plaintiffs submitted along with their opposition to OpenAI’s motion were incorporated into the Consolidated Class Action Complaint by reference” and that it was enough that their Complaint repeatedly made “clear, definite and substantial references” to the outputs. Losing that civil procedure skirmish was probably a bad sign for OpenAI—a bit like the menacing prologue in A Game of Thrones, you sense that Copyright Winter is Coming .  

Judge Stein then went on to evaluate one of the more detailed chat-GPT generated summaries relating to A Game of Thrones, the 694 page novel by George R. R. Martin which eventually became the famous HBO series of the same name. Even though this was only a motion to dismiss, where the cards are stacked against the defendant, I was surprised by how easily the judge could conclude that:

“A more discerning observer could easily conclude that this detailed summary is substantially similar to Martin’s original work, including because the summary conveys the overall tone and feel of the original work by parroting the plot, characters, and themes of the original.”

The judge described the ChatGPT summaries as:

“most certainly attempts at abridgment or condensation of some of the central copyrightable elements of the original works such as setting, plot, and characters”

He saw them as:

“conceptually similar to—although admittedly less detailed than—the plot summaries in Twin Peaks and in Penguin Random House LLC v. Colting, where the district court found that works that summarized in detail the plot, characters, and themes of original works were substantially similar to the original works.” (emphasis added).

To say that the less than 580-word GPT summary of A Game of Thrones is “less detailed” than the 128-page Welcome to Twin Peaks Guide in the Twin Peaks case, or the various children’s books based on famous works of literature in the Colting case, is a bit of an understatement.

The Wikipedia comparison

To see why the latest OpenAI ruling is so surprising, it helps to compare the ChatGPT summary of A Game of Thrones to the equivalent Wikipedia plot summary. I read them both so you don’t have to.

The ChatGPT summary of a Game of Thrones is about 580 words long and captures the essential narrative arc of the novel. It covers all three major storylines: the political intrigue in King’s Landing culminating in Ned Stark’s execution (spoiler alert), Jon Snow’s journey with the Night’s Watch at the Wall, and Daenerys Targaryen’s transformation from fearful bride (more on this shortly) to dragon mother across the Narrow Sea. In this regard, it is very much like the 800 word Wikipedia plot summary. Each summary presents the central conflict between the Starks and Lannisters, the revelation of Cersei and Jaime’s incestuous relationship, and the key plot points that set the larger series in motion.

I could say more about their similarities, but I’m concerned that if I explored the summaries in any greater detail, the Authors Guild might think that I am also infringing George R. R. Martin’s copyright, so I’ll move on to the minor differences.

The key difference between the Wikipedia summary and the GPT summary is structural. The Wikipedia summary takes a geographic approach, dividing the narrative into three distinct sections based on location: “In the Seven Kingdoms,” “On the Wall,” and “Across the Narrow Sea.” This structure mirrors the way the novel follows different characters in different locations, to the point where you begin to wonder whether these characters will ever meet. In contrast, the GPT summary follows a more analytical structure, beginning with contextual information about the setting and the series as a whole, then proceeding through sections that follow a roughly chronological progression through the major plot points.

There are some minor differences. The Wikipedia summary provides more granular plot details and clearer causal chains between events. It explains, for instance, how Catelyn’s arrest of Tyrion leads to Tywin’s retaliatory raids on the Riverlands, which in turn necessitates Robb’s strategic alliance with House Frey to secure a crucial bridge crossing. The Wikipedia summary also includes more secondary characters and subplots, such as Tyrion’s recruitment of Bronn as his champion in trial by combat, and Jon’s protection of Samwell Tarly.

The Wikipedia summary probably assumes a greater familiarity with the fantasy genre, whereas the GPT summary might be more helpful to the uninitiated. The GPT summary explains the significance of the long summer and impending winter and explicitly sets out the novel’s major themes.

In broad strokes, however, there is very little daylight between these two summaries. They are remarkably similar in what they include and in what they leave out. Most notably, both summaries sanitize Daenerys’s storyline by omitting the sexual violence that is fundamental to her character arc. This is particularly striking because sexual violence is central to Martin’s narrative in so many places and to the narrative arc of several of the main characters.

If GPT is substantially similar, so is Wikipedia

I don’t see how the ChatGPT summary could infringe the copyright in George R. R. Martin’s novel, if the Wikipedia summary doesn’t. A chilling prospect indeed, but I don’t think that either one is infringing.

It’s absolutely true that you can infringe the copyright in a novel by merely borrowing some of the key characters, plot points and settings, and spinning out a sequel or a prequel. In copyright, we call this a derivative work. But just because sequels and children’s versions of novels are often infringing, doesn’t mean that a dry and concise analytical summary of a novel is infringing.

Why not? It’s actually the act of taking those key structural elements, the skeleton of the novel if you like, and adding new flesh to them to create a new fully realized work that makes an unauthorized sequel infringing.

What’s at stake

Judge Stein’s order doesn’t resolve the authors’ claims, not by a long shot. And he was careful to point out that he was only considering the plausibility of the infringement allegation and not any potential fair use defenses. Nonetheless, I think this is a troubling decision that sets the bar on substantial similarity far too low.

The fact that “[w]hen prompted, ChatGPT can generate accurate summaries of books authored by plaintiffs and generate outlines for potential sequels to plaintiffs’ books” falls well short of demonstrating that such outputs by themselves would be regarded by the ordinary observer as substantially similar to a fully realized novel.

Posted on

Do law schools need Harvey.AI?

Harvey.AI is following the playbook of Westlaw and Lexis by trying to establish itself as the go-to AI tool of choice for lawyers before they even become lawyers. I asked my university library to organize a Harvey demo so that we could think about joining the ranks of Stanford, UCLA, NYU, Notre Dame, WashU, Penn, UChicago, Boston University, Fordham, BYU, UGA, Villanova, Baylor, SMU, and Vanderbilt. (As reported by Above The Law)  (https://abovethelaw.com/2025/10/harvey-snags-even-more-seats-in-the-t14).

This post is primarily based on a one-hour product demonstration given to us by a Harvey representative. To have a really well informed view on the product, I would want more hands-eye experience but there is surprisingly little information about what Harvey is actually offering online beyond the company’s own press releases. So, I thought my colleagues at other universities might find this assessment interesting.

TLDR

Meh, it’s OK, but law schools probably don’t need it and are probably only jumping on the bandwagon so that they can be part of the press release.

What is Harvey?

Harvey.AI is a legal-tech and professional services AI company whose flagship product is a generative AI assistant designed specifically for legal workflows used by law firms, in-house legal teams, and other professional services organizations. On its website, Harvey characterizes itself as “Professional Class AI” for leading professional service firms, emphasizing that its technology is domain-specific. In other words, it’s an AI system fine-tuned and optimized for legal and related professional work.

Use Cases and Contraindications

The first thing to understand about Harvey is that it is categorically not a legal research tool. Harvey essentially offers its clients a way of integrating generative AI into some routine drafting and analytical tasks that are quite common in legal practice.

Here are some common use scenarios:

If you have already identified the relevant case law and have a memo template to hand, Harvey AI can help you draft a legal research memo in double-quick time.

Alternatively, Harvey can help you review the key terms of a lengthy contract or almost any other synthesis or summarization task you could imagine.

Another good use case for the Harvey AI platform would be drafting an agreement or marking up the other side’s agreement in light of your own preferred templates. Harvey’s process for drafting from scratch seems directly analogous to vibe coding in software, but with a nice Microsoft Word integration.

You can also use Harvey for analysis and ideation (i.e., brainstorming). I can imagine coming to the end of a 3-month trial, throwing all the relevant documents into Harvey, and then launching into a discussion about closing argument strategy. Or, uploading a motion for summary judgment and the other side’s response, and then trying to anticipate the kinds of questions you might get from the bench.

The Harvey’s Value Proposition

You can already do almost all of this with ChatGPT, Gemini, Claude, and the like, subject to volume limitations on how many documents you upload. So, the natural question is, what value add does Harvey AI offer?

Fine tuning and model switching

One of the advantages claimed by Harvey is that rather than using foundation models like GPT directly, you would be engaging with custom versions of those model, fine-tuned on training data relevant to law and legal analysis. I could imagine that in some fields this would be a significant advantage, but I wonder how much of an advantage it is in the legal field given that most of that fine-tuning data is going to be public domain legal texts that are already well represented in the foundation models.

Another thing Harvey sees as a benefit is that they are not tied to any one model. They currently use three different fine-tuned foundation models, GPT, Gemini, and Claude, and they allocate tasks according to comparative advantage.

Security and confidentiality

By default, prompts and documents transmitted to a company like OpenAI may be used in training, will definitely be stored on OpenAI’s servers (at least for a while), and thus might be subject to discovery through appropriate legal processes. OpenAI has a setting where users can opt out of training that specifies that their data will only be retained for 30 days. This is probably good enough for many casual uses and even some mildly sensitive uses, but it’s obviously not enough for material that is subject to attorney-client privilege.

Accordingly, one of the key differentiators offered by Harvey AI is that the documents you upload and the prompts you write will not be accessible to Harvey or any third party, and that all of the information processing takes place in a secure Microsoft Azure environment with end-to-end encryption. This is probably the absolute minimum necessary to use LLMs for legal work. A large law firm could go one step further and actually host its own model in-house rather than relying on Microsoft. That extra layer of security might be required by some especially restrictive protective orders in litigation or by some especially sensitive clients. That sounds great, but I’m pretty sure I already get all that from Microsoft Copilot (although I would have to do a deep dive into the terms and conditions, Microsoft offers my university, to be sure).

Another nice feature of Harvey is that the client administrator can set permissions for individual users and for particular teams of users. This is critical in a corporate law environment where access to sensitive documents needs to be compartmentalized. It’s also critical if Harvey is being made available to students in a law school environment because students taking courses such as foundational Legal Writing and Research classes should probably not have access to Harvey AI.

Document Review (Retrieval-Augmented Generation)

Harvey AI has a good user interface for analyzing large volumes of documents. That is essentially an implementation of retrieval-augmented generation (RAG).

What’s RAG?

In very simple terms, RAG is an alternative to just answering a question through next-token prediction, relying on bulk context and whatever knowledge and understanding is latent in a foundation model. In a RAG process, the user query is translated into a document query. The document query identifies sections of documents that seem relevant to the query. Those sections are then collated and fed back into a general model which attempts to answer the question based on the specifically retrieved chunks of text. Platforms like ChatGPT are using a process like this any time you see them searching the web and providing links back to particular documents.

Harvey does RAG pretty well

RAG sounds like a great idea in theory. But whether it works in practice depends on how good the matching method is, which can vary a lot from context to context. In any RAG process, you will never know what relevant chunks of text were overlooked, and you won’t know whether the interpretive part of the model has drawn the appropriate inferences from the chunks it has retrieved unless you go back and check the original sources. One of the things I liked about the Harvey UX is that it made it easy to inspect the original document fragments and it had a clear process for checking off that these had actually been interrogated.

Example use cases would be looking for a change of control provisions in licensing agreements, as part of merger due diligence, or in document review for litigation. The Harvey representative we spoke to candidly admitted that the system performed really well in establishing a chronology, except in relation to emails. This makes sense, because an email thread contains lots of different dates all jumbled in together, but it is clearly a major limitation.

Prompting and training

Another value-add our representative stressed was prompting. Our representatives seem to be saying not only that Harvey would be running some thoughtfully-crafted prompts in the background, essentially running interference between user instructions and the models, but also that individual clients could do this for themselves. I can see why this might be an appealing feature to some people, but I’m not entirely convinced that making the steps in an analytical process obscure from the user is a good idea.

My Assessment

Generative AI as legal technology

Before we get into the specific pros and cons of Harvey, we need to consider the appropriate uses of generative AI as a legal technology more generally.

Many key deliverables in the legal field are in the form of text. But it’s relatively rare that the value of that text is entirely contained within the document itself. When a lawyer explains something to a client, they aren’t just helping their client understand something. They are also making a set of representations about the thought, diligence, and analysis that has gone into formulating that advice. Clients don’t just want text for its own sake, they want text you stand behind.

Accordingly, the most significant uses of generative AI in the legal field will be ones that accelerate a drafting-review or document-analysis process, as opposed to merely substituting for the underlying analysis.

Responsible use of generative AI in the legal field must be accompanied by either:

  • strong validation mechanisms (such as a process for clicking through the footnotes to confirm that the document in question really says what the model represented),
  • a knowing and well-informed acceptance of certain risks, or
  • the kind of external validation that a lawyer who is already familiar with the underlying materials intrinsically provides.

The validity questions that need to be answered before deploying generative AI as a legal technology are not limited to the problem of hallucinations in the narrow sense of invented cases, citations, and quotations.

Harvey claims to do very well in dealing with hallucinations, but it’s important to situate this in the context that Harvey is not a legal research tool. The kinds of tasks that Harvey says that its product should be used for are exactly the kind of tasks where one would expect a much lower instance of hallucinations. Why? Because they are mostly summary or translation tasks where the model has specific documents or templates to draw from. Even so, I’m a bit skeptical that the rate of hallucinations is really as low as Harvey claims.

The value proposition for law firms

Depending on the cost, I can see that Harvey would be a very attractive proposition for law firms of all sizes. Most of what Harvey offers can be replicated through an enterprise agreement with one of the main AI providers. Harvey offers a turnkey solution and a good user interface. You can think of it as ChatGPT in a black turtleneck, but that’s no bad thing.

Is it worth it? That depends on the cost, and the cost of the alternatives.

The value proposition for law schools

There is no doubt that most of our students are already using generative AI. It seems appropriate that we begin training them to do so properly and responsibly at the earliest opportunity. That said, the availability of generative AI to students taking specific skills courses could easily undermine the development of those skills. Rather than simply making Harvey available to all students, it makes sense to exclude first-year students and perhaps some upper-level skills courses. But obviously, we would want students in our Advanced Legal Writing course (where we are teaching AI skills) to have access to this tool.

If we decide that we don’t want students in our clinics using generative AI, then one of the major selling points of Harvey disappears. Our students don’t need the robust confidentiality protection that Harvey offers.

If Harvey is offering commercially reasonable terms, I still think it is an attractive proposition. But its value in legal education seems to me to be really quite limited. Our students are not conducting massive document review exercises or working with in-house templates. Most of the things students would find compelling about using Harvey, they can already do with Microsoft Co-Pilot, ChatGPT, Gemini, and Claude.

Posted on

Legal Scholars Roundtable on Artificial Intelligence 2026 (save the date)

Emory Law is proud to host the 5th annual Legal Scholars Roundtable on Artificial Intelligence on April 9-10, 2026, at Emory University in Atlanta, Georgia. The Legal Scholars Roundtable on Artificial Intelligence is a forum for the discussion of current legal scholarship on AI, covering a range of methodologies, topics, perspectives, and legal intersections.

We will make a formal call for papers in January with submission deadline some time in February.

The AI Roundtable is conveyed by Prof. Matthew Sag (Emory Law) and Prof. Charlotte Tschider (Loyola Law Chicago)

Posted on

Competition from AI music, Country Girls Make Do

As of October 2025, Suno and Udio are two text-to-music AI platforms that let users create full songs—including lyrics, vocals, and artwork—simply by entering text prompts. Some of this music is unappealing, even to its creators (protagonists?), but music scene insiders have assured me that some of the music emanating from these platforms is good enough to provoke a wistful, “I wish I had written that.”

AI music is also becoming more popular. A recent article in The Economist (of all places) recounts the viral success of “Country Girls Make Do,” a raunchy parody country song generated by artificial intelligence under the pseudonym Beats By AI. The song apparently features on TikTok where users prank the unsuspecting by playing it under false pretenses.

This is more than a one off. Acts such as Aventhis and The Velvet Sundown, also AI-based, have attracted hundreds of thousands of monthly listeners on Spotify. These tools allow for rapid and prolific production: Beats By AI reportedly releases a new song every day. This is not simply a case of streaming fraud where AI slop steals music plays from real artists by adopting confusing names—Spotify recently removed 75 million such tracks, citing “bad actors” flooding the platform with low-quality content. Some people at least, like some AI music. The Economist reports a Luminate survey finding that, one-third of Americans accept AI-written instrumentals, nearly 30% are fine with AI lyrics, and over a quarter do not mind AI vocals.

No music stands alone, but AI music arguably even less so

The appeal of these tracks lies partly in their echos of established genres and tropes, with a dash of irony and experimentation thrown in. It’s to be seen whether this portends a consumer-driven revolution in content creation where listeners generate their own entertainment rather than relying on record labels.

What does this mean for copyright law?

Although the Copyright Office would not regard works of The Velvet Sundown or Beats By AI as copyrightable, Spotify seems happy to royalties for AI music, provided the works themselves (as opposed to the copying that fed the AI process that created the works) don’t infringe on other artists songs.

AI music may destabilize entrenched business models at the fringes, but it might also foster broader participation and new forms of cultural expression. Does AI pose the same threat to the economic and cultural standing of musicians as it does to stock photography and digital art? Or will AI-generated music remain a hybrid layer within popular culture that feeds off and refers back to mainstream music without replacing the central role of human creation? If so, perhaps at least some country girls will make do.

Posted on

Skater Beagle and the Puzzle of AI Creativity

Generative AI poses a puzzle for copyright lawyers, and many others besides. How can a soulless mechanical process lead to the creation of new expression, seemingly out of nothing, or if not nothing, very little?

This essay will help you understanding where the apparent creativity in generative AI outputs comes from, why a lot of AI works are not copyrightable, and why the outputs of generative AI are mostly very different to the works those AIs were trained on.

Who is the author of Skater Beagle?

The image below was created by one LLM (Google Gemini) using a long prompt written by another LLM (Anthropic’s Claude) following the instruction “draft a prompt for an arresting image of a beagle on skateboard.”

AI generated “arresting image of a beagle on skateboard.” From a low angle, a joyful beagle with ears flying expertly rides a skateboard down a steep urban hill during a cinematic, “golden hour” sunset. A city skyline is backlit by the setting sun.

If I took this photo in real life, I would be recognized as the author. Likewise, if I painted it as a picture. But because the image was created by a process that involved very little direct human contribution, it is uncopyrightable. For many people, this seems odd. How can an image that looks creative not be recognized as copyrightable, just because it was created with AI rather than an iPhone camera, or a set of water-based paints? After all, artists use tools to make art all the time?

No copyright for the AI

The first question to address is whether Google’s image generation model is the author of Skater Beagle. The answer is no, for many reasons, but let’s focus on the copyright issues, because they are the most interesting.

The AI can’t get copyright protection because the AI itself is not creative in any of the ways we generally understand that term (at least if you are a copyright lawyer) because it lacks any desire or intention to express. In Burrow-Giles Lithographic Co. v. Sarony (1884) the U.S. Supreme Court recognized that a photograph could be copyrighted, but only because the photographer’s creative choices made the image an “original intellectual conception[] of the author” rather than a mere mechanical capture. LLMs are impressive, but they don’t have any intentions separate from the math that makes them predict one thing and not another. LLMs don’t have original intellectual conception they are trying to express.

No copyright for the simple prompt engineer

If not the AI, then maybe the person who writes the prompts should be credited with the resulting expression? After all, isn’t choosing the right words in the prompt a creative act?

That doesn’t work either. Sure, choosing the right words in the prompt might be creative in some senses, but copyright law doesn’t protect creativity in the sense of “hey, that’s a good idea,”— it protects creativity that manifests in original expression. This idea-expression distinction is one of the foundations of copyright law. Copyright attaches to the final expression, not the upstream idea or instruction that triggered it. Even if you think my idea to get one LLM to write a prompt for another LLM “for an arresting image of a beagle on skateboard” is creative, its really just a simple idea and nothing copyrightable.

Surely, it must be one or the other?

But still, many would say, if Skater Beagle exhibits all the tell-tale signs of subjective creative authorship, that creativity must come from somewhere. So it’s either the AI or the person who wrote the prompt?

This line of thinking is half right, the generative AI is doing something important, it’s creating something from nothing, but its not “creativity” in the relevant sense. If you want to think of all of the details of the skater-beagle picture as expression, that expression does not magically appear from the ether, it comes from the latent space implied by the training data as processed by the model during training. In some ways it’s fair to say it comes from the collective efforts of all of the authors of all of the works in the training data. But not in the sense of a simple remix or cut-and-paste job.

Not from nothing, but not a remix

Generative AI systems come in different kinds, GANs, diffusion models, multimodal large language models, and more. The common feature of all these systems is that they trained on a large volume of prior works, and through a mathematical process, they are able to produce new works, often with very limited additional human input. But that doesn’t mean Skater Beagle belongs to the millions (10s of millions, 100s of millions?) of authors of the works in the training data. This beagle is not simple remix or collage. Although generative AI models are data dependent, they don’t just remix the training data, they produce genuinely new outputs.

AI Creativity comes from latent space

Generative AI models learn an abstract model of the training data, a model that is in many ways more than the sum of its parts. When you prompt a generative AI model, you are not querying a database, you are navigating a latent space implied by the training data.

What do I mean by “navigating a latent space implied by the training data”? Let’s start with a simple analogy. When you fit a linear regression to a handful of data points you generate a line of best fit implied by the data as seen in the figure below. Think of the dots as the training data and the line as the model implied by the training data.

Illustration of fitting a line to scattered data. Two side-by-side scatter plots on a beige background. Left: Five orange data points scattered in an upward trend without a line. Right: The same points with a straight diagonal line drawn from bottom left to top right, representing a best-fit line. Both axes are labeled X and Y, ranging from 0 to 10.

The line illustrated above is simple, it is in fact an equation that you can use to answer the question, “if y is 6, what is x?” The point 6,6 is not in the data, but it is implied by the data and the model we used to fit the data. When you plug y=6 into the model you are navigating to a point implied by the data that tells you x=6, as seen in the figure below. That is what I mean by navigating the latent space.

Illustration of navigating to point implied by linear regression. A scatter plot with five orange data points, a green dashed diagonal line representing a trend, and red dashed lines intersecting at the point (6,6). Axes are labeled X and Y, ranging from 0 to 10, on a beige background.

But of course, if we used a different model, the data would imply a slightly different latent space, as illustrated in the figure below. Here the model is not linear its quadratic and just changing that starting assumption gives us a different line of best fit.

Illustration of fitting a different model to the data. A scatter plot with five orange data points on a textured blue-and-beige background. A green dashed curve rises steeply before leveling off, intersecting red dashed lines at the point (4,6). Axes are labeled X and Y, ranging from 0 to 10.

The difference between the straight line and the curved line here is analogous to the difference between different LLMs. Obviously, generative AI models are much more complicated than a two-dimensional regression model. Generative AI models have thousands of dimensions, and so they constructs a much richer latent space, but the analogy holds. Any number of dimensions above 3 is hard to conceptualize, don’t bother trying to imagine thousands of dimensions, your brain might melt.

Does Latent Space Solve the Creativity Puzzle?

Understanding latent space helps resolve the creativity puzzle. The image of Skater Beagle looks original because the model has generated a point in a vast space of possible images implied by its training data — not because a human author made free and creative choices about the details. The model navigates to a statistically plausible combination of features, but no person decides where the beagle’s ears should fly, how steep the hill should be, or what the sunset should look like. Understanding latent space helps explain why the output of a model can feel creative but still lacks the human authorship copyright law requires.

But wait, …

But in practice, it seems like almost any photo you send to the Copyright Office will be deemed creative enough to meet the requirements for registration. If I can get copyright for just pointing my iPhone at a beagle on skateboard and pressing a button, why can’t I get copyright in an image of a beagle on skateboard that I created using generative AI?

This seems inconsistent at first blush, but only because the question overlooks the difference between the “thin” copyright that attaches to photos based in reality and the thick copyright that typically attaches to illustrations drawn from imagination.

Small jumps versus big jumps

When you take a photo, you are making a copyrightable selection and arrangement from reality. You get no rights in the underlying reality, just a specific photographic representation therein. In most copyrightable photos there is only a small jump between idea and expression and so the resulting copyright is limited to that jump. Taking a photo does not give you exclusive rights on the underlying ideas, subjects, locations, etc.

There are two critical differences between the typical iPhone snap and an image generated with AI.

The first difference is that there is a much more significant jump between idea and expression in the transition from text prompt to final image, compared to the jump from a real life scene to photo capturing the scene. The second difference is that in photography, a human still makes some minimal creative decisions (framing, timing, composition) that manifest in the look of the resulting image. The human makes the jump, even if it’s only a small jump. In AI generation, the algorithm fills in the details that transform the prompt into a specific visual expression. The AI makes the jump between your idea for a photo and the details of the photo itself.

There is no copyright in the Skater Beagle image Gemini made for me. The work of bridging the gap from abstract concept to concrete image was done entirely by algorithms trained on trillions of words and millions photos. The details that we might think of as expression in the image didn’t come from nothing, they didn’t come directly from any particular photo featuring low angle action shots, beagles, dogs with ears flying, skateboard riders, steep hills, urban settings, “golden hour” sunsets, city skylines, etc. The details that we might think of as expression don’t reflect the free and creative choices of any human mind. They are details implied by a model trained on millions of photos, but those details don’t come from those photos either. The come from the universe of possibilities those photos imply, they come from latent space.

Skater Beagle is an extreme example

Generative AI lets us navigate a latent space implied by works too numerous to count so that we can create genuinely new digital artifacts. I began this essay with the promise that understanding this would shed light on how copyright applies to AI-generated works, but Skater Beagle is an extreme example drawn from one end of the continuum. Understanding why Skater Beagle is not a copy of beagles in the training data, but is also not my creative expression tells us that the Copyright Office is right to deny copyright to some generative AI creations. But it does not tell us at what point a user would cross the line from commissioning editor to guiding hand or creative mastermind. It’s hard to imagine crossing that line with a single text prompt, but it’s easy to see how you would leap over it in an iterative process as in A Single Piece of American Cheese. Iterative interactive use of generative AI will often be an act of authorship, so long as it is more than just choosing a winner in a beauty pageant of AI creations.

[This essay was adapted from Matthew Sag, Copyright Law in the Age of AI (2025)]

Posted on

Drafting Law School AI Policies

There are a lot of poorly thought through AI policies out there

Law schools are realizing that they need student conduct policies that address generative AI. But after reviewing many of their policies (and some undergrad policies as well), I feel they often miss the mark. Here are five problems that crop up again and again.

First, many conflate using AI with plagiarism.

Plagiarism, properly defined, is the unacknowledged appropriation of another’s words or ideas. Violations of prohibitions on AI use, by contrast, are often better conceptualized as breaches of disclosure obligations, misrepresentation, or general academic integrity violations. While AI misuse can sometimes constitute plagiarism it is not necessarily so. Rules that lump these activities together are too blunt. There are sound reasons, sometimes to prohibit both, but they should not be conflated. Taring a wide set of AI uses with the brush of plagiarism. Is unlikely to win acceptance from students, who will reasonably see such policies as overreach.

Second, definitions are a muddle.

Many policies leave key operative terms—such as “compose,” “proofread,” “substantially edit,” or “small part”—undefined. Absent bright-line rules or illustrative examples, students and faculty are left to infer the policy’s scope, producing inconsistent enforcement and potential due process concerns.

Sweeping prohibitions on “AI use” may unintentionally extend to widely accepted tools, including spellcheckers, grammar correction software, and dictation systems. Such breadth is rarely the drafters’ intent and risks chilling legitimate academic practice. Blanket prohibitions, especially without accommodation mechanisms, may disproportionately disadvantage non-native speakers and students with disabilities who rely on technological assistance, even as comparable human support (e.g., writing centers) remains permissible. If that kind of restriction is intended, it should be express.

Third, some schools are leaning on unreliable technology to police AI use.

Recommendations to use AI detectors or plagiarism software to identify AI-generated work are problematic given their poor reliability. Without cautionary limits, such tools risk false positives and undermine due process.

It is important to understand three key limitations here:

(1) Anti-plagiarism software does not detect novel generative AI outputs;

(2) AI detectors are not reliable in the way anti-plagiarism software are reliable;

(3) AI detectors generate a large percentage of false positives. They are especially prone to do so in cases involving neurodivergent authorship or use of standard proofreading programs such as Grammarly.

Honestly, you would be better off tossing a coin, at least then you would have a realistic assessment of how far you should trust the answer.

Fourth, few schools offer clear ways for students to disclose their use of AI.

Standardized disclosure mechanisms would enhance transparency and promote consistent expectations across courses and instructors.

Fifth, the policies themselves are often inconsistent.

One policy I read takes a categorical approach on prohibiting AI use but then in a latter part of the document it suggests allowing AI “for parts of assignments” and asks instructors to clarify expectations. What?

A template for a better Law School AI policy

So, what should your AI policy look like? It should be clear, specific, comprehensive, and custom tailored for each course you teach. You can do that with the template I suggest below, just by changing the “mays” to “may nots”

I’m sure this is not perfect, but I think it’s a useful place to begin. Your use of this template is not plagiarism, I am posting it here because I think you should copy it.

Generic Law School Syllabus AI Use Policy

(1) The use of generative AI in this course is restricted but not entirely prohibited. The restrictions serve multiple, sometimes overlapping, purposes: preserving pedagogical integrity, preserving the integrity of assessment, helping you avoid plagiarism, misrepresentation, and shoddy work. These restrictions are tailored to this course, so you need to review them carefully.

(2) Key Prohibitions:

(a) In this course you are prohibited from presenting text generated by generative AI as your own in any assessable work product. This means that you may not copy-paste more than 8 consecutive words from any source without specific attribution (superficial changes designed to evade the substance of this rule will be disregarded); you may not present specific insights and ideas from external sources without specific attribution to an appropriate source. In addition, you may not include factual information or citations from generative AI that you have not verified. Work containing obvious AI “hallucinations” of citations or quotations will merit a failing grade.

(b) In addition, you may not use generative AI to develop insights and strategies for specific assigned class activities or assessable work product without specific authorization from your professor. For example, in that context you may not use generative AI

  • to review legal documents (real and simulated) for potential issues where learning to spot relevant issues is part of the skillset being taught;
  • to suggest negotiation strategies for a simulated deal where learning to develop negotiation strategies is part of the skillset being taught;
  • to practice role-playing as opposing counsel for such a simulated deal or negotiation; and
  • to identify ethical issues in a fact pattern where identifying such issues is part of the skillset being taught.

(c) You may not use generative AI to assist with answering questions presented in class in real time: if you are on-call that does not mean ChatGPT is on-call.

(3) You may use generative AI for research and source discovery provided you do so responsibly and in compliance with (2) above. Examples of acceptable uses include asking a generative AI tool for caselaw, statutes, and regulations relating to a particular topic, or to review a draft of your work product and ask for suggested additional sources or authorities.

(4) You may use generative AI to improve your work product, provided you do so responsibly and in compliance with (2) above. For example, you may use generative AI for brainstorming/ideation for essay topics, or to suggest a more logical structure for a paper; you may use generative AI to identify weaknesses in argument, counter-arguments you may have overlooked, and otherwise critically evaluate your written work. Likewise, you may use generative AI to improve your understanding of complex legal doctrines, including by asking for different types of explanations thereof, but again, provided you do so responsibly and in compliance with (2) above.

(5) You may use generative AI for detailed assistance with drafting, editing and style, provided you do so responsibly and in compliance with (2) above and with an appropriate disclosure. For example, you may draft a passage and then ask generative AI to rewrite it a particular style (law review, client email, opening argument), or to maintain a particular style but reduce the word count; you may draft a passage in a language other than English and then ask generative AI for an English translation; you may use generative AI to suggest more effective transitions and topic sentences, introductions and conclusions; you may use generative AI for suggestions as to how to more effectively integrate quotations into your main text.

The disclosure for the editorial assistance described above should be in the following form: “Approximately [10-25 |25-50 ] % of this [essay] was redrafted with the assistance of generative AI (list all), however all of the ideas and analysis are either my own or are appropriately cited.”

(6) You may use generative AI to generate images and charts in assessable work product with specific disclosure, such as a visible note in the caption or figure description: “Chart produced with [name of tool] based on [general description of prompt or underlying data]”.

(7) You may use spell check, and dictation software without any disclosure.

(8) You may use generative AI to support your learning and comprehension of course materials, provided you do so responsibly and in compliance with (2) above. For example, you may use generative AI as a tutor or a study partner, or to create flashcards, hypotheticals, explanations, quiz questions, etc.; you may use generative AI to summarize and outline course materials; you may use generative AI to suggest answers to non-assessable problem questions, or to evaluate your answers to non-assessable problem questions.

(9) Permitted uses are not necessarily recommended. Direct engagement with primary sources and your own analysis will yield the deepest learning and the most reliable work product. AI may serve as a useful complement—helping to clarify, organize, or refine ideas—but it should be employed thoughtfully and never as a substitute for the skills this course is designed to develop.

For term papers, you need a bit more

I suggest the following additional instructions.

Write in your own voice:

To avoid the impression that your work was written by a chatbot or is just a superficial rephrasing of a few original sources you must ensure that it reflects your own original analysis, voice, and understanding. Submissions that exhibit unusually advanced legal knowledge, overly polished or professional tone, highly structured policy-style formatting, or extensive use of comparative law without appropriate scaffolding may raise concerns about authorship. Likewise, papers that rely heavily on secondary authority without clear personal engagement can suggest inappropriate use of generative AI or outside assistance.

A good way to demonstrate the originality of your contribution is to explore a narrowly defined, non-obvious topic, rather than a broad or generalized theme arising from the course. A greater level of specificity usually indicates that a student has chosen a unique angle shaped by personal interest or experience.

Research, sources, and citation practices:

Good research and appropriate citation practices go hand in hand.

For most law research papers, you should prioritize primary sources and academic sources. However, for many topics in this course, you will be discussing recent trends and developments, so it will often be appropriate to cite journalistic reports and even blog posts as well. Here are some guidelines for citing propositions relating to Law, Opinion, Facts as summarized by someone else, and Specific facts.

(1) Law: If you are making an assertion about what the law is, you should generally cite case law, statutes, or academic treatises.

(2) Opinion: If you are discussing academic commentary or opinion, cite the relevant source directly.

(3)  Key arguments: If you are making an academic argument that already exists in the literature, you should identify who made that argument first. What if you can’t say for sure? If the argument is central to your thesis, put in the effort to be sure! If it is not, sometimes it will suffice to note others who have made the same point in a form such as “For arguments that …, see, for example, …”

(4) Facts as summarized by someone else: If you are referencing facts that have been summarized in academic commentary, you have some discretion as to whether to cite the academic source or go directly to primary sources for the underlying facts. Government reports and think tank publications are also useful for consolidated discussions of facts, as well as insightful commentary and analysis. In general, citing primary sources is preferable unless you are relying on an author’s summary or synthesis of multiple sources.

(5) Specific facts: Background information often comes from blogs, news articles, magazine articles, or even Wikipedia. That is fine. When using these as secondary sources, ask yourself: Is this the most direct source? Is this a reliable source? Whenever possible, prioritize more direct, reliable and authoritative sources to ensure accuracy and credibility. For example, do not cite to a blog post that summarizes an article in the NY Times, if you can read the underlying article and cite it directly.

Caution: AI summaries and dialogs with chatbots are not a reliable source of any external fact. Obviously, you can cite a ChatGPT session for a proposal like, “ChatGPT (version 4o) often recommends Kyoto when asked to suggest a random city.” But you can’t use ChatGPT as authority for the proposition that Kyoto was Japan’s capital from 794 to 1868.

Concluding thoughts

“Law schools are uniquely positioned to model thoughtful, principled engagement with new technology. A well-crafted AI policy can uphold academic integrity without stifling innovation or disadvantaging students. The goal is not to ban the future, but to teach students how to use it responsibly.”

Or that’s what ChatGPT said when I asked for suggestions on how conclude this post. I use LLMs in lots of different ways and this post benefited from long discussions with ChatGPT and with my Emory Law colleagues, but this post does not reflect the views of Emory Law, or ChatGPT for that matter.

Posted on

Piracy, Proxies, and Performance: Rethinking Books3’s Reported Gains

A new NBER working paper by Stella Jia and Abhishek Nagaraj makes some stunning claims about the effects of pirated book corpora on large-language-model (LLM) performance. In Cloze Encounters: The Impact of Pirated Data Access on LLM Performance (May 19, 2025) (working paper)( https://www.nber.org/papers/w33598), the authors contend that access to Books3—a pirated collection of  full-text books—raises measured performance by roughly 21–23 percent in some LLMs.  

This astonishing finding is an artifact of the paper’s methodology and the very narrow definition of “performance” that it adopts, as such it should not be taken at face value. 

Cloze Encounters’ methodology and claims

Jia and Nagaraj assemble a 12,916-book evaluation set and apply a “name cloze” task: mask a named entity in a short passage and ask the model to supply it.

For instance, given a sentence like “Because you’re wrong. I don’t care what he thinks. [MASK] pulled his feet up onto the branch” from The Lightning Thief, the model should identify “Grover” as the missing name.

The main results of Cloze Encounters are estimates of “performance” showing large, statistically significant gains for GPT-class models (about 21–23 percent relative to baseline), smaller gains for Claude/Gemini/Llama-70B (about 7–9 percent), and no detectable effect for Llama-8B. The effects are stronger for less-popular book titles, consistent with fewer substitutes (Internet reviews or summaries) in other training data.

This is all well and good, but the way authors explicitly link these findings to current controversies relating to copyright policy, licensing markets, and training-data attribution is troubling.

Cloze Encounters is not measuring “performance” any way that people should care about

The first thing that raised my suspicion about this paper is that I had already seen this exact methodology used as a clever way to illustrate memorization and to show how some books are memorized more than others. See, Kent Chang et al., “Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4” (https://arxiv.org/abs/2305.00118). Cloze Encounters scales and repurposes that approach for a causal analysis of how access to pirated books in the Books3 lead to improved LLM “performance.” But it doesn’t make sense to me that what counted as a memorization probe in one paper could just be relabeled as a general “performance” metric in another.

Why is memorization so different to performance?

This is a question of construct validity. The method in Cloze Encounters tests recall of a masked name from a short passage, scored as a binary hit. This kind of lexical recall is a narrow slice of linguistic ability that is highly sensitive to direct exposure to the source text. It’s a proxy for memorization rather than the broad competencies that make LLMs interesting and useful.

The capabilities that matter in practice—long-context understanding, abstraction and synthesis, factual grounding outside narrative domains, reliable instruction following—are largely orthogonal to masked-name recall. Calling the cloze score “LLM performance” is a massive over-generalization from a task that measures a thin, exposure-sensitive facet of behavior. As an evaluation device, name-cloze is sharp for detecting whether models learned from—or memorized—a specific source; it is blunt for assessing overall performance. There is no reason to think that evidence of snippets of memorization from particular works in the books3 dataset has any necessary relationship with being a better translator, drafter, summarizer, brainstorming partner, etc.

This paper is begging to be misread and misapplied in policy and legal debates

I wouldn’t go so far as to say that success on the cloze score tells us “literally nothing” about LLM performance: “almost nothing” is a fairer estimate. To see why, think about the process of pre-training. Pre-training optimizes next-token prediction over trillions of tokens; the cloze outcome is, by construction, and basically the same as that objective. So it is not surprising that it is unusually sensitive to direct exposure to given pieces of training data. There probably is a broad correlation between next-token  accuracy  and perceived usefulness (we certainly saw this in the transition from GPT-3.5 to GPT-4), but the relationship is not lockstep, and it’s easy to imagine a model that excels  at memorization alone but generalizes poorly.

The authors nod to these limitations at various points in the manuscript but they still frame it as a measure a  of “LLM performance” in a way that is just begging to be misread and misapplied in policy and legal debates. Abstract-level claims travel further than caveats; many readers will see the former and miss the latter.

Nor does the identification strategy employed in the paper do anything to rescue the limits of the construct. The instrumental variable—publication-year share in Books3—may isolate an exogenous shock to exposure. Even granting the exclusion restriction, the estimate remains the effect of Books3 on a name-cloze score. It tells us little about summarization, reasoning, instruction following, safety behavior, or cross-domain generalization.

Bottom line

Cloze Encounters usefully documents that access to Books3 leaves a measurable imprint on exposure-sensitive recall. But its central metric does not justify broad the claims it makes about “LLM performance.” The study measures whether models can fill in masked strings drawn from particular books; it does not show that such access improves the flexible, user-tailored generation that makes these systems valuable.

Posted on

Emory Law AI Roundtable 2025

The Fourth Annual Legal Scholars Roundtable on Artificial Intelligence 2025 will be held next week at Emory Law and I am very excited by the amazing line-up of speakers and commentators we have.

AI Roundtable Papers

Neel Guha, Information in AI Regulation
Michael Goodyear, Dignity and Deepfakes
Kat Geddes, AI’s Attribution Problem
Deven Desai & Mark Riedl, Responsible AI Agents
Nikola Datzov, AI Jurisprudence: Toward Automated Justice
Yiyang Mei & Matthew Sag, The Illusion of Rights-Based AI Regulation
David Rubenstein, Federalism & Algorithms
Oren Bracha, Generative AI Two Information Goods

Some of these papers are available in draft on SSRN.com or arXiv.com, others are still in development.

AI Roundtable Keynote

We also have a special keynote from Prof. Barton Beebe, presenting his new book manuscript “Technological Change and the Beautiful Deaths of Law: A Recurring History.” The Roundtable is invitation only, Emory faculty and students who are interested in attending should contact me for details.

History of the Legal Scholars Roundtable on Artificial Intelligence

The Roundtable was founded by Professor Matthew Sag and Professor Charlotte Tschider in March 2022 as an online event (due to the Covid-19 Pandemic) and has been conducted as an annual event at Emory Law School ever since. The Roundtable is supported by Emory University School of Law and by Emory’s AI.Humanity initiative.

The following were recognized as the Roundtable’s Best Paper in their respective years: Rebecca Crootof, Margot Kaminski, & Nicholson Price, Humans in the Loop, 76 Vanderbilt Law Review 429 (2023) (Best paper of 2022); Matthew T. Wansley, Regulating Driving Automation Safety, 73 Emory Law Journal 505 (2024) (Best paper of 2023); Mark Bartholomew, A Right to Be Left Dead, 112 California Law Review 1591 (2024) (Best paper of 2024)