Mike Masnick (via Techdirt)
Last week Cathy wrote about the amicus brief we filed in the big multi-district litigation (MDL) that consolidated a bunch of different lawsuits claiming that OpenAI and Microsoft infringed the plaintiffs’ copyrights by training AI models on their works. A lot of people and groups offered proposed amicus briefs. For boring procedural reasons, unlike in most cases, you couldn’t just file the briefs, but rather everyone had to ask permission from the court, which meant that each attempted filing involved multiple filings, and many potential amici used different processes to try to file their briefs. Oh, and the docket is already at almost 2,500 entries. It’s a mess.
So, if you want to review all of the proposed amicus briefs, I’ve embedded below a tool to look at all of the briefs. They are sorted by which side they support (defendants, plaintiffs, neither), and then if you open up each section you can see who the amici are, a link to their brief, and a one sentence summary of their argument. In case you are wondering, yes, this was created using Claude Code and the CourtListener MCP, asking it to go in and find the actual proposed briefs from the docket, to organize them, and to summarize them. And yes, not surprisingly, it didn’t get everything right on a first pass. The version below is the third version, human reviewed, after it had trouble reading some of the files and got confused on who some of the amici were actually supporting.
Anyway, we already have a whole post on our own brief, but a few others I’ll call out quickly. While it’s no surprise that the usual copyright maximalists filed briefs supporting the plaintiffs, what’s interesting is how many other copyright holders filed in support of the defendants like OpenAI. My favorite one is from The Authors Alliance, a group representing authors, but who recognize that copyright maximalism and hating on fair use is bad for authors as well. Their argument is to call bullshit on the whole idea of “market dilution” being a harm implicated by copyright, noting: “Market Dilution is just a fancy term for competition, and copyright law supports competition.”
Other copyright holders who weighed in on behalf of innovation were independent creators Sarah Rose Siskind and Michael Wall. They argued that contrary to the claims of plaintiffs, AI is helping creators as a tool that assists them in being creative. Indeed, they highlight how it has helped them compete with big-name creators backed by major industry players. The brief from the Chamber of Progress and the Abundance Institute also included the incredible multi-Grammy winning drummer Trevor Lawrence Jr., also talking about how important AI has been in helping musicians like him create new music.
There were a number of other industry groups and civil society orgs, along with a bunch of academics. EFF details how a ruling for the plaintiffs would do real damage to research. Library groups and the American Council of the Blind teamed up with tech trade groups CTA and BSA to highlight the many important benefits of AI tools. And then there are the academics. Copyright experts Pam Samuelson and Chris Sprigman (both former Techdirt podcast guests) also call out the nonsense “market dilution theory.” Brian Frye teamed up with journalist Ron Suskind to explain how training is transformative and how AI is useful for journalists. Professors Rebecca Tushnet and Edward Lee — both well known copyright experts as well — also explain transformative use and how silly the market dilution theory is.
There are some briefs that support “neither” party (technically the Frye one supports neither, but the argument seems to strongly support the defendants) including the Wikimedia Foundation’s brief. It focuses on applying the (problematic) precedent from the weird Andy Warhol fair use case a few years back, arguing that under Warhol, courts need to analyze each use separately, rather than making a blanket assertion that [waves hands around vaguely] “all this is infringing.” It does say that regular AI training should be fair use, but argues that RAG training (basically, AI on top of a corpus of data) probably isn’t fair use.
Not surprisingly, I’m less impressed by those siding with the plaintiffs. To me they trot out the well worn and debunked tropes of copyright maximalists of past fights. Claims that “if this isn’t infringing no one will ever create again” are rampant and just silly. The oddest one may be actor Ashley Judd’s. It basically argues that AI will kill journalism (citation desperately needed) and that if that had happened a decade ago, there wouldn’t have been journalists left to blow the whistle on Harvey Weinstein and the entire #metoo movement might not have happened. I honestly had trouble following how any of those things connect, especially since journalists use AI tools all the time, and it hasn’t made them disappear.
There’s a similar one from everyone’s favorite documentarian, Ken Burns, who argues that AI will kill the market for historical documentaries. Again, I don’t see how anyone gets there, though. First of all, the market for documentaries has grown massively over the past decade, and they’re now often treated as equals of feature films, something that would have been unthinkable not long ago. And literally this amicus brief is filed the same week people are debating if the new Elizabeth Holmes documentary has a shot at winning the Oscar for Best Picture (rather than best documentary). And I know (personally!) documentary filmmakers who are saying that AI tools are often making it easier to create stronger documentary films (and no, not by generating AI slop, but to help with some of the more annoying production tasks). Then there are all the expected ones from the RIAA, Copyright Alliance, newspapers, the publishers… who say the same tired “oh no the sky is falling, creators will never create again” bullshit.
The one other surprising (and disappointing) filing on behalf of the plaintiffs was the brief from PEN America, the Protect Democracy Project, and Reporters Without Borders. I am a fan of all three organizations and find it incredibly awkward and odd that those organizations in particular would strike an anti-free speech stance by siding with copyright maximalists seeking to lock up knowledge. They give a highly misleading history lesson on the Progress Clause, suggesting that the First Amendment’s Press Clause and the Progress Clause work together in a way that justifies locking up knowledge for the benefit of the press. They’re right that the two clauses fit together, but they get the relationship exactly backwards: both exist to expand the spread of knowledge, not to restrict it. It’s just… odd.
Anyway, you can find the links and summaries to all those and more below… This case still has a long way to go and, no matter how it’s decided, will almost certainly involve many years of appeals. But the copyright battle lines are being drawn, once again.
Proposed amicus briefs on the RECAP docket through ECF 2462, by the side each supports. Click a section to expand it.
■ 22 for defendants · ■ 26 for plaintiffs · ■ 4 neither/mixed · ■ 1 brief not yet located · 53 total
Supports defendants (22)
Kevin Kelly
AI speeds up the creation of new works without harming plaintiffs, and its public benefits are vast. Copyright isn't a shield against disruptive technology; training is fair use.
Public Knowledge
Copying for training is fair use. Fair use also allows reasonably necessary copying to reach unprotectable facts, including facts about creative works.
Supports defendants “in part.”
Engine Advocacy et al. (startups)
Startups build products on fine-tuned foundation models, which shows training is a general-purpose fair use. Licensing costs would lock startups out of AI.
Floor64 / Copia Institute
Copyright can't and shouldn't be used to throttle AI training. Doing so would conflict with the Progress Clause and the First Amendment, and copyright grants no right to foreclose training.
A.I. Progress, Inc. (AI developer coalition)
Generative AI is now everyday infrastructure. Training is “quintessentially transformative,” and a ruling against fair use would hurt the broader economy.
Profs. Pamela Samuelson & Christopher Jon Sprigman
Reject the market dilution theory: the empirical studies behind it are flawed, and any collective harm is for Congress to address. At minimum, require proof.
Prof. Joshua Gans
Economic framing aimed at the news cases: lost sales aren't by themselves copyright harm, dependence on any single work in a large dataset is unproven, and later licenses don't prove a market. Asks the court to reject the News Plaintiffs' arguments.
Consumer Technology Ass'n, American Council of the Blind, American Library Ass'n, Ass'n of Research Libraries, BSA
Catalogs public benefits of LLMs across accessibility, agriculture, law, transit, manufacturing and independent creators. Grant summary judgment to defendants.
Electronic Frontier Foundation
Fair use turns on the ultimate use, not how copies were acquired. Good faith isn't required, and competition isn't market harm. Plaintiffs' theories would threaten web scraping, research datasets and search.
CCIA, NetChoice, TechNet
Training is non-expressive intermediate copying to build a new tool (Google Books, Oracle). Market dilution isn't cognizable harm, and owners can't create a market by demanding licenses for a transformative use.
Khosla Ventures
VC perspective: a ruling against fair use would entrench incumbents, chill investment and cede AI leadership abroad. Grant OpenAI's motions.
Gregory Leppert
Libraries rely on general-purpose models for preservation and access at scale. Anyone who wants Game of Thrones goes to the library rather than rebuilding it from AI output.
Former Sen. Rick Santorum & former Rep. Rick Boucher
Congress made fair use flexible. AI training continues the tradition of non-expressive technological uses, and fair use underpins US tech leadership.
Midjourney, Runway AI
Training is designed to enable new expression. Detailed examples of AI tools expanding creativity for amateurs and professionals make it a “quintessential transformative fair use.”
Trevor Lawrence Jr., Chamber of Progress, Abundance Institute
Extracting non-expressive patterns is transformative, and working musicians' experience shows generative AI enables human creativity.
Takes no position on how the works were acquired.
Authors Alliance
Market dilution, as framed in Kadrey, is just competition. Factor four covers only substitution by uses that take protected expression.
Single issue; takes no position on anything else.
Prof. Brian L. Frye
Training is transformative, and requiring licenses would entrench the biggest incumbents.
Styled “neither party” but argues fair use. The court dismissed his earlier filing (2282) for not requesting leave (Order, Dkt. 2412); this one may have the same defect.
Prof. Matthew Sag
Training is highly transformative under Bartz, Kadrey and the copy-reliant technology cases. Transformativeness is judged by realistic outputs, and questionable sourcing belongs in factor four.
Says it “supports neither party” but argues fair use.
Foundation for American Innovation, David Shedd, Gen. Michael Groen, George Barnes
Under Oracle, the court should weigh the national-security benefits the record ties to the challenged training as part of factor four.
Narrow ask that in practice helps defendants.
Profs. Rebecca Tushnet & Edward Lee
Scaling training data with intermediate copies is highly transformative under factor one. Plaintiffs' market dilution theory rests on non-infringing outputs that share only genre or facts, which are uncopyrightable, and the Copyright Clause forecloses it.
Sarah Rose Siskind & Michael Wall (independent creators)
A comedy writer and a composer who license their own work describe using AI for business operations and ideation. They argue it lets independent creators compete with better-resourced rivals and helps them make more work, not less.
Computer scientists Mike Knoop, Prof. Daniel Lowd, Prof. Douglas C. Schmidt
Technical brief: training fits numerical parameters and is not a system for storing and retrieving works. Memorization is limited, model-specific and reducible, and research shows OpenAI's safeguards block substantial extraction, which supports a different purpose under fair use.
Styled in support of OpenAI but says it takes no position on the ultimate merits. About 130 of the 160 pages are an appendix of CVs.
Supports plaintiffs (26)
Association of American Publishers, Copyright Clearance Center, Independent Book Publishers Ass'n, STM
Training uses expressive content for the same purpose as the originals and produces substitutes, so it isn't transformative. Unlicensed training harms book markets and the emerging collective AI-licensing market.
Center for American Rights
OpenAI's broad fair use theory would undercut the administration's AI/IP framework. Protecting IP is a national-security issue, and a pro-fair-use ruling would give foreign adversaries legal cover for IP theft.
Digital Content Next
AI tools that summarize publishers' content without sending users to the source cut off traffic, subscription, ad and licensing revenue. That is an existential threat to digital publishers.
AFM, SAG-AFTRA, WGA East, WGA West
A flood of AI-generated works is the “substantially adverse impact” Campbell anticipated. The unions already bargain licensing for new technologies, so the court should reject a categorical fair use exemption.
Copyright Alliance
After Warhol, the older transformativeness cases defendants rely on carry little weight, and Kadrey and Bartz are distinguishable. Knowingly using pirated sources, and an existing licensing market, weigh against fair use.
Open Markets Institute
Mass copying is unfair competition against publishers. It also undercuts AI rivals that pay for licenses and could kill the emerging licensing market.
Also misfiled under ECF 2372.
News/Media Alliance, National Newspaper Ass'n, National Press Photographers Ass'n, ProPublica
Copying news to build products that supersede it isn't fair use, relying on the Third Circuit's Thomson Reuters v. ROSS ruling (Sept. 2026). Factor four: harm to online news and AI-licensing markets.
Minderoo Centre for Technology & Democracy, University of Cambridge
Opposes defendants' summary judgment motions. What the models retain from books is a disputed material fact. Training is cumulative, so the copies aren't “intermediate” under Sega and Connectix.
AI Coalition for Data Integrity, WGA East & West, Nat'l Ass'n of Voice Actors, Digital Media Licensing Ass'n, Transparency Coalition AI, Ass'n of Medical Illustrators, Ass'n of Independent Music Publishers, Music Workers Alliance, Microstocksolutions
Answers the DOJ's national-security argument: security agencies want documented training-data provenance, which unlicensed scraping can't provide. Foreign rulings such as GEMA v. OpenAI undercut the claimed US advantage.
First Amendment scholars: RonNell Andersen Jones, Jack Balkin, Erin Carroll, Alan Chen, Heidi Kitrosser, Jane Kirtley, Burt Neuborne, Jonathan Peters, Victor Pickard, Francesca Procaccini, Sonja West
The press serves constitutional and democratic functions that depend on a viable business model. AI chatbots free-ride on reporting and erode that model without doing what the press does.
RIAA, NMPA, A2IM, ASCAP, BMI, Recording Academy, Songwriters of North America, Artist Rights Alliance, Black Music Action Coalition
Music industry brief in support of plaintiffs' summary judgment motions.
The argument summary couldn't be pulled from the text. Open the PDF for details.
Ashley Judd
Draws on her experience as a source for the NYT's Weinstein reporting: work like that needs well-resourced human journalists and trusted relationships, which AI can't replace and threatens to defund.
ASJA, ASMP, American Literary Translators Ass'n, National Writers Union, Novelists Inc., Romance Writers of America, Sisters in Crime, Dramatists Guild, SFWA, Nat'l Ass'n of Science Writers, NPPA, Garden Communications
Supports the class plaintiffs. Downloading from LibGen and Books3, training, and OpenAI–Microsoft distribution are not fair use. AI output floods the market with substitutes, and defendants acted in bad faith.
Reps. Jamie Raskin, Jerrold Nadler, Hank Johnson
DOJ's national-security argument amounts to an unauthorized blanket exemption for all AI training. It deserves no deference, and national security isn't a fair use factor.
Styled as a response to the DOJ Statement of Interest.
Ken Burns
Training on his films is destroying the market for quality historical documentaries, and the models regurgitate exact copies of his material.
PEN America, Protect Democracy Project, Reporters Without Borders (RSF USA)
Defendants' arguments invert the historical relationship between the Press Clause and the Progress Clause. Their business practices threaten a free, independent press.
Cate Blanchett, Steven Soderbergh, Nick Hexum, CAA, Creators Coalition on AI, Music Artists Coalition, Nat'l Ass'n of Voice Actors, Human Consent Foundation, Writer, Inc.
A working licensing market for training data exists. Collective licensing and machine-readable consent tools are in use, and that should drive the factor four analysis.
No explicit ask; the argument favors plaintiffs.
Copyright law professors (incl. Julia Powles, Trevor Reed, Marketa Trimble, Bhamati Viswanathan; full list in the brief's appendix)
Training and RAG copying is direct infringement. In these works expression is inseparable from facts and ideas, and copyright has never allowed wholesale copying of expression to reach the underlying knowledge.
Progressive Democrats of America, RadicalxChange, Tech Oversight Project, Young People's Alliance, Prof. Walter Scheirer (lead author David Atkinson)
Defendants want “GenAI exceptionalism.” If their use is fair, almost any human's unlicensed use would be too. Public availability doesn't create an implied license.
Prof. Joseph Stiglitz
Economic argument: unlicensed AI use erodes incentives and degrades the information ecosystem, and the split of value is grossly inequitable. Licenses should divide the gains.
Attaches an economics paper (51 pp.).
America's Newspapers
Loss of local news makes government costlier and less accountable, communities less safe and democracy weaker. Claimed public benefits can't outweigh that.
Jim Albrecht
Focuses on RAG: the copying is the violation and the output shows a substitutive purpose. Harm to news is ongoing, and spreading the copying across sessions doesn't excuse it.
AAN Publishers, Inst. for Rural Journalism, LION Publishers, Nat'l Ass'n of Hispanic Publications, Rebuild Local News Coalition, Solutions Journalism Network, NewsGuild-CWA, SPJ
Strong copyright gives small outlets leverage for compensation. Today only big outlets get AI deals, so a ruling for the NYT would push AI companies toward licensing that pays local news.
AI startups: BandLab, Boomy, Created by Humans, Credtent, Splice, Newstex, ProRata.ai, SourceAudio, Cashmere (Vandal AI), Voice-Swap
Licensed training hasn't priced developers out. Licensing creates markets, pays creators and reduces legal risk, so requiring permission won't slow innovation.
The AI Rights Project
Defendants haven't shown that machine-scale training is equivalent to human creative reuse, which is the premise of transformativeness. Rebalancing the IP Clause is for Congress.
The motion says it supports plaintiffs, but the brief “urges no outcome.”
William Morris Endeavor (WME)
Factor four: AI companies already license copyrighted works, voices and likenesses, and WME negotiates those deals. Unauthorized training and AI replicas harm that market, and lost licensing fees count as market harm.
Supports the news plaintiffs. Filed only in the NYT case (23-cv-11195).
Neither party / mixed (4)
Wikimedia Foundation
Analyze each use separately under Warhol. Scraping itself isn't a “use.” Pre-training is likely fair use; feeding copied works into a chatbot's RAG system likely isn't.
Center for AI and Digital Policy, plus AI experts and legal scholars
Training-data provenance is central to accountability. Identify each use separately, and don't let poor recordkeeping become an evidentiary advantage.
Leans plaintiffs.
Institute for Intellectual Property & Social Justice (Lateef Mtima)
Asks the court to weigh social-justice values such as equitable access, inclusion and empowerment, beyond the usual economic framing.
The specific ask is unclear from the excerpt.
Michael Moritz
LLMs and independent journalism both matter, and LLMs depend on continued quality reporting. For the two to coexist, a workable licensing market has to develop, and that will happen only if the law makes clear developers can't copy publishers' content without paying.
Filed in the NYT case (23-cv-11195) and supports neither party, though its argument leans toward the news plaintiffs. Discloses that a co-author was formerly seconded as counsel to the Times.
Brief not yet located (1)
Profs. Jon Garon, Adam Mossoff, Loren Mulraine, Chris Newman, Sean Pager, Zvi Rosen, Mark Schultz
Likely supports plaintiffs; this group usually files pro-rightsholder briefs.
Only notices of appearance (2432, 2450) are in RECAP. As of Oct 31, no brief appears on the RECAP docket after 2462; check PACER.
Sides are assigned by what each brief says it supports; where a brief claims neutrality but clearly lands on one side, it is placed there with a note. Summaries are drawn from each brief’s Summary of Argument, Introduction or Conclusion. RECAP’s copy of the docket stops at ECF 2462 (Oct. 30, 2026).