Evidence at a glance
A Collection of Briefs, Not a Court Ruling
On October 30, 2026, OpenAI published a post titled 'Fair use benefits everyone' on its website, compiling excerpts from court briefs submitted by authors, journalists, creators, academics, accessibility advocates, library groups, business coalitions, and the U.S. government. All these briefs support a core claim: training AI models on copyrighted works without permission constitutes fair use under U.S. copyright law. The post itself is a presentation of litigation materials, not a court ruling, and does not indicate that any judicial conclusion has been reached.
The direct opponent in this dispute is The New York Times. According to OpenAI's case timeline, The New York Times sued OpenAI and Microsoft on December 27, 2023, alleging that training models on news articles without permission constitutes infringement. On September 4, 2026, OpenAI filed a motion for summary judgment, asking the court to rule directly that its training conduct is fair use. On October 23, 2026, the news publishers filed their opposition. OpenAI's decision to publish this collection of briefs just days later is no coincidence.
The Four-Factor Test: Fair Use Is Never an Automatic Exemption
The U.S. Copyright Office makes clear that fair use requires weighing four factors together: the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality of the portion used, and the effect on the potential market or value of the work. These are not a checklist to pass sequentially but a framework for holistic balancing. The core legal strategy of OpenAI and its supporters is to argue that AI training should be deemed fair use under all four factors.
On purpose and character, they argue that training's purpose is fundamentally different from the expressive purpose of news or literature—the model learns from content to become a general-purpose tool, rather than providing substitute access to the original works, making it transformative. On market effect, the brief from Engine and Developers Alliance advances a more aggressive view: even if AI outputs compete with and affect the sales or visibility of original works, this should not automatically constitute market harm under copyright law as long as it does not substitute for specific protected expression. If courts adopt this 'market dilution' theory, it would significantly narrow the space for copyright holders to claim market harm.
Who Supports: The Coalition Behind the Briefs
The OpenAI post lists 11 brief excerpt headings, covering both joint institutional briefs and individual submissions. The jointly signed organizations include the American Council of the Blind, the Association of Research Libraries, the American Library Association, the Consumer Technology Association, and the Business Software Alliance; additionally, groups such as Authors Alliance, Engine and Developers Alliance, Public Knowledge, and the Foundation for American Innovation, as well as the U.S. government and individual independent creators. The signatories form a carefully curated cross-sector coalition.
Notably, the inclusion of accessibility advocacy organizations and library groups expands the fair use argument from 'tech companies seeking freedom to train' to 'restricted training would harm vulnerable populations who rely on AI for access to information.' The Authors Alliance brief goes further, arguing that even if AI outputs compete with original works, this should not automatically be deemed market harm, because copyright law should protect against 'substitution of protected expression,' not 'successful competition by new expression.' This argument pushes the protective boundary of copyright law toward a narrower definition, while the addition of the government brief adds a public policy dimension to the debate.
The Market Dilution Debate: Redrawing the Line Between Copyright and Competition
The most technically governance-relevant issue in this litigation is how to distinguish 'copyright infringement' from 'market competition.' The brief from Engine and Developers Alliance explicitly states that the plaintiffs seek to have the court recognize a 'market dilution' theory—that AI outputs, even without copying protected expression, constitute actionable market harm if they compete for the same audience or occupy the same subject matter as the original. The alliance asks the court to hold that such aggregate competitive pressure is not cognizable market harm under Section 107(4) of the U.S. Copyright Act.
If adopted, this request would mean copyright holders face greater difficulty using litigation to block competitive outputs generated by AI models trained on their content. For tech leads, this directly affects the legal space for model capability boundaries and content strategy: as long as the training process does not cause model outputs to substitute specific expression from original works, the generation of competitive content could be legalized. Conversely, if the court rejects this theory, then any competitive relationship between model outputs and original works in subject matter, audience, or style could become an entry point for copyright holders to claim harm. This would significantly raise compliance costs for training data selection and output filtering.
Practical Implications for Tech Teams: From Compliance to Argumentation Strategy
For tech teams building or using large models, the value of these briefs lies not in legal conclusions but in signaling an emerging argumentation framework. When your training data sources or model outputs face copyright challenges, explaining 'how the model works' from a purely technical angle may not be enough; you also need a public interest narrative about 'who gets hurt if training is restricted.' OpenAI's strategy shifts the debate from model technical details to social impact, offering a defensible path other AI companies can reference.
Another actionable point is the linkage between output filtering and market harm arguments. If the 'market dilution' theory is ultimately not adopted by the court, tech teams will need to more carefully assess the degree of competition between model outputs and original works in subject matter and audience at the output level, to avoid being deemed market substitution. Conversely, if the theory is accepted, output filtering can focus more on preventing copying of specific expression rather than preventing competition in style or subject matter. This requires tech leads to incorporate copyright argumentation strategy into product design from the start, rather than remedying it after litigation arises.
Boundaries and Costs: What the Coalition Narrative Obscures
This coalition narrative has a clear blind spot: it does not address the right to know and compensation mechanisms in the data scraping process. The briefs emphasize public interest and transformative use after training, but barely touch on how training data sources are acquired, whether creators are informed when their data is included in training sets, and whether compensation paths independent of litigation exist. For tech leads, this means that even if the fair use defense succeeds, compliance disputes over data sourcing will not automatically disappear.
Moreover, the timing and curation of these briefs are themselves a display of litigation strategy. They attempt to create an impression before judges and the public that 'restricting AI training would harm society as a whole,' but the essence of copyright litigation remains a commercial negotiation between the traditional content industry and AI companies over the pricing power of data value. When referencing this argumentation, tech teams need to distinguish which points are legally reliable and which are public opinion campaigning. The most pragmatic judgment is: the legal boundaries of fair use are still moving, and training data compliance strategies should retain sufficient flexibility to cope with uncertainties arising from different jurisdictions and different rulings.