A federal judge in Manhattan now faces 56 amicus briefs in the consolidated OpenAI copyright case, the largest single filing wave in an AI-related lawsuit to date, setting the stage for a precedent-setting ruling on whether training models on copyrighted material is fair use.
TL;DR
- 56 amicus briefs were filed in OpenAI's Manhattan copyright case, the largest wave in any AI-related lawsuit, with plaintiffs backed by publishers and defendants by tech groups.
- The core question is whether ingesting copyrighted works to train AI models counts as fair use, a ruling that could redefine licensing markets and legal exposure for the entire AI industry.
- Industry heavyweights including the Authors Guild, News/Media Alliance, RIAA, and major tech groups like the EFF and CCIA have staked out positions in the filings.
What happened
In September 2023, the Authors Guild sued OpenAI, alleging the company used authors' copyrighted works to train its large language models without permission. In December 2023, The New York Times filed a separate suit against OpenAI and Microsoft, accusing them of unlawfully using its journalism to build AI products.
By April 2025, a dozen similar cases were consolidated into In re: OpenAI, Inc. Copyright Infringement Litigation before U.S. District Judge Sidney Stein in Manhattan. The central legal question: whether copying books, news articles, and other copyrighted material to train models qualifies as fair use under U.S. copyright law.
On November 6, 2026, Judge Stein approved 56 amicus briefs—more than in any prior AI-related lawsuit—submitted by industry groups, advocacy organizations, and individuals. The briefs are split between those supporting the plaintiffs (including publishers and creators) and those backing OpenAI and Microsoft (including tech coalitions and AI startups).
Several briefs neither endorsed a side nor declared a position, while others included notable signatories such as actor Cate Blanchett, director Steven Soderbergh, and economist Joseph Stiglitz, who supported the plaintiffs, and writer Kevin Kelly and former Sen. Rick Santorum, who backed the defendants.
Why it matters
For AI startups, the ruling could determine whether training on publicly available data remains legal without explicit licensing, potentially reshaping data acquisition strategies and model development costs. A finding against fair use could force companies to negotiate licenses for vast troves of copyrighted material, increasing operational expenses and slowing innovation.
Publishers and content creators argue that unlicensed training undermines their markets and devalues original work, while tech groups and AI developers contend that training is transformative and aligns with established fair-use principles. The outcome will set a legal baseline that influences future AI model training across industries.
The sheer volume of briefs reflects the high stakes: more than 700 case citations were analyzed across the filings, with the most cited precedents including *Authors Guild v. Google* and *Campbell v. Acuff-Rose Music*, both central to the fair-use debate. The case also highlights tensions between rapid AI advancement and existing legal frameworks, particularly around licensing and compensation for creators.
Investors and founders should monitor this case closely, as a precedent could either accelerate capital flows into AI startups (if fair use is upheld) or trigger a wave of licensing negotiations and legal challenges that increase barriers to entry and reduce competitive advantages tied to proprietary datasets.
Key facts
- The consolidated case, In re: OpenAI, Inc. Copyright Infringement Litigation, is before U.S. District Judge Sidney Stein in Manhattan.
- 56 amicus briefs were filed on November 6, 2026—more than in any prior AI-related lawsuit, according to Tech Policy Press analysis.
- The plaintiffs include the Authors Guild (representing authors), The New York Times, and other publishers and creators, while defendants are OpenAI and Microsoft.
- Plaintiff-backed briefs were filed by organizations including the News/Media Alliance, the Copyright Alliance, the Association of American Publishers, the RIAA, SAG-AFTRA, and the Writers Guilds of America West and East.
- Defendant-backed briefs were filed by groups including the Electronic Frontier Foundation, Public Knowledge, the Computer & Communications Industry Association, NetChoice, TechNet, and AI startups such as Midjourney and Runway.
- Notable individual signatories supporting plaintiffs include filmmaker Ken Burns, actor Ashley Judd, and economist Joseph Stiglitz; those supporting defendants include writer Kevin Kelly, musician Trevor Lawrence Jr., and former Sen. Rick Santorum.
- The case hinges on whether training AI models on copyrighted materials constitutes fair use under 17 U.S.C. § 107, a question central to the future of AI model development.
- The filings cite over 700 court decisions, with the most frequently referenced precedents including *Authors Guild v. Google* (2013) and *Campbell v. Acuff-Rose Music* (1994).
Context
The OpenAI copyright case is part of a broader wave of legal challenges facing AI companies over data sourcing and model training practices, following similar lawsuits against Stability AI, Adobe, and others. These cases collectively test the boundaries of fair use in the digital age, where training data often includes copyrighted works scraped from the web.
The fair-use doctrine, established in U.S. copyright law, allows limited use of copyrighted material without permission for purposes such as criticism, commentary, or education. Courts have historically grappled with applying this doctrine to new technologies, from photocopiers to search engines, and the AI industry now sits at the center of this evolving legal landscape.
For AI startups, the outcome of this case could influence how they structure data pipelines, negotiate licensing agreements, and design compliance frameworks. A ruling against fair use could accelerate the adoption of opt-in data licensing models, while a ruling in favor could reinforce the current practice of training on publicly available data—both scenarios carrying significant strategic implications for the industry.
