Wikimedia Foundation Seeks to File Amicus Brief in OpenAI Copyright Case

The Wikimedia Foundation announced on October 30, 2026, that it has asked the U.S. District Court for the Southern District of New York for leave to file an amicus, or friend-of-the-court, brief in In re: OpenAI, Inc. Copyright Infringement Litigation, the consolidation of 12 lawsuits against OpenAI whose best-known member is The New York Times’ case against OpenAI and Microsoft.

The Foundation’s filing, dated October 29, 2026, and addressed to Judge Sidney H. Stein, is a letter motion for leave to file, with the proposed brief attached as an exhibit and styled as supporting neither party. It was submitted under the court’s local rules and an August 7, 2026 order governing amicus participation, as modified on September 24, 2026; because that order directs prospective amici to move for leave, Wikimedia did not seek the parties’ consent. The proposed brief contains 5,831 words, within the 6,000-word limit the court set for amicus briefs.

Rhett O. Millsaps II of Lex Lumina LLP is counsel of record, alongside the Foundation’s General Counsel Stephen LaPorte, Associate General Counsel Jacob Rogers, and Legal Counsel Aly Marino.

In the consolidated litigation, the court will rule on whether artificial intelligence (AI) companies’ use of copyrighted content to develop large language models (LLMs) constitutes fair use. The Foundation said the decision will be highly influential because of the jurisdiction, the parties involved, and the small number of rulings that have addressed the question so far.

The New York Times filed its lawsuit in 2023, alleging that OpenAI and Microsoft’s use of Times-owned content for any purpose, including AI model training, directly violates copyright law. OpenAI and Microsoft contend that training and deploying models based on publicly available data is technological development that falls within fair use.

The Foundation argues that a too-restrictive version of fair use could inhibit technological innovation and limit the availability of tools that benefit open-source projects like Wikipedia, while blanket fair use could produce a more closed web in which online knowledge is weakened by a lack of available secondary source material.

In a companion post on Diff, the Wikimedia movement’s blog, the Foundation said that if the training step is not considered fair use, AI development would likely remain possible only for the largest corporations or the best-funded developers, leaving open-source developers unable to afford or even access the technology. A blanket ruling, it said, would instead encourage publishers to lock more information behind barriers, reducing the visibility that Wikipedia and many publishers depend on for readership and for recruiting new volunteers.

CEO Bernadette Meehan said the Foundation is taking a position “to defend the free and open human knowledge that Wikipedia depends on,” describing expanded access to knowledge through AI as a powerful goal that must respect the people, contributors, and communities that make reliable knowledge possible.

A Use-by-Use Fair-Use Framework

The proposed brief grounds its approach in the Supreme Court’s 2023 decision in Andy Warhol Foundation for the Visual Arts v. Goldsmith, arguing that each distinct use of copied works across the stages of developing and operating a chatbot requires its own four-factor fair-use analysis. It cites the 2025 Bartz v. Anthropic ruling as instructive for assessing separate uses of the same copies at different stages rather than blending them into a single analysis.

The brief also contends that scraping — making an automated technical request to a website for a copy of a work — is acquisition rather than a copyright use, and that only what is done with the copies afterward should undergo fair-use analysis. It notes that some forms of scraping can still violate site terms of use, state contract or tort law, the Digital Millennium Copyright Act, or the Computer Fraud and Abuse Act.

Training Likely Fair, Retrieval-Augmented Generation Likely Not

On pre-training, the brief argues the use is likely fair: transformative because the copies supply linguistic patterns rather than serving the works’ informational purpose; neutral on the second factor given the factual nature of encyclopedic works; justified on the third because developers need billions of words to train a model, making the use of entire works reasonably necessary; and fair on the fourth because pre-training itself does not substitute for the originals in the market. The Foundation’s announcement summarizes the position as holding that using copyrighted content for model training should be considered fair when it trains models that are something new and different from the original sources, with no requirement to meet copyright licensing terms.

The brief describes retrieval-augmented generation, or RAG, as a process in which a chatbot searches the web or another database, then identifies, copies, ranks, and pastes relevant chunks of content into the model’s context window alongside the user’s query. Because such a system uses the works for their original purpose of informing readers, takes the most important parts of those works, and competes with the sources for human viewership, the brief argues, the use likely fails the four-factor test.

The Foundation maintains that RAG and similar grounding techniques should not be treated as fair use and that companies offering them should respect each platform or publisher’s licensing terms. In Wikipedia’s case, that is the Creative Commons CC BY-SA 4.0 license, which requires credit to original creators and requires that adaptations remain under the same license.

The brief’s conclusion asks the court to rule that the pre-training of ChatGPT’s general-purpose LLM was fair use, while OpenAI’s use of copies in ChatGPT’s retrieval-augmented generation feature is not fair and must abide by licenses such as the Creative Commons license for Wikimedia Project content.

Wikipedia’s Stake and Other Amicus Filings

Wikipedia holds more than 67 million articles across 300 languages, with nearly 250,000 volunteers contributing each month, and the Foundation describes it as one of the highest-quality datasets used to train LLMs. The brief states that more than 260,000 volunteer contributors per month, as of January 2025, create and moderate content across the Foundation’s thirteen free-knowledge platforms, and that Wikipedia is viewed billions of times per month.

The brief states that non-human traffic from scraping and crawling strains Wikimedia’s servers, pushing it to adopt technical safeguards that bad actors often bypass, and that both the strain and the safeguards threaten the accessibility of its sites to human readers. The Foundation said scraping of its projects must respect its public API rate limits or go through its Wikimedia Enterprise API offerings. It also told the court that machine-learning tools already help volunteers fight vandalism, detect copyright violations and inappropriate personal information, and prioritize articles most in need of improvement, arguing that it would be contrary to the purposes of copyright law to label LLM development categorically unfair.

General Counsel Stephen LaPorte said the fair-use conversation “cannot be limited to for-profit organizations,” adding that a ruling that does not apply fair use principles carefully could threaten the sustainability of free knowledge and stifle the development of open-source technologies.

Wikimedia’s motion joined a wave of amicus activity: docket entries show that on October 28 and 29, 2026, Engine Advocacy, the Authors Alliance, Public Knowledge, Professor Matthew Sag, Brian L. Frye, Digital Content Next, and a group of publishing organizations including the Association of American Publishers moved to file their own amicus briefs, while summary-judgment briefing between the news publishers, OpenAI, and Microsoft continued. The court has broad discretion to decide whether to accept an amicus brief, the Foundation’s filing notes, and Judge Stein must grant the Foundation leave before its proposed brief is formally accepted.