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The New York Times sued OpenAI and Microsoft on December 27, 2023, alleging that they copied millions of its works to build and commercialize AI products and that those products can reproduce its journalism and compete with its business. The case raises questions about copying during training, what chatbots generate, and the effect on publishers’ traffic and revenue. A judge narrowed some claims in April 2025, but did not decide whether the use was lawful. The latest court-calendar entry identified here is a February 11, 2026 status conference; no final merits ruling, settlement, or trial verdict is established by the sources cited below.
What The New York Times alleges
The Times filed its lawsuit in Manhattan federal court on December 27, 2023. The defendants named in the case caption include Microsoft and multiple OpenAI entities. The complaint followed unsuccessful discussions about use of Times content and possible licensing arrangements. The filing and case record are available through the case docket.
The Times alleges that the companies copied its journalism without authorization and used it to develop commercial AI products, including ChatGPT and Microsoft AI services. It says chatbot responses can reproduce substantial or near-verbatim portions of its articles, and argues that AI answers may compete with its news products by giving readers information without a visit to the Times’ website.
The complaint also alleges harm to subscriptions, advertising, licensing, referral traffic, and the Times’ relationship with its audience. It raises claims concerning copyright-management information and seeks monetary and injunctive relief, including destruction or remediation of models and datasets said to contain Times works. These are demands and allegations—not findings that the defendants copied unlawfully, caused the claimed losses, or must destroy a model.
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OpenAI and Microsoft have different roles in the dispute. The Times challenges OpenAI’s development and operation of models and Microsoft’s alleged role as a partner, investor, infrastructure provider, and distributor. Liability for each company depends on the evidence and legal theories; Microsoft’s participation should not be treated as identical to OpenAI’s.
What it means to train a chatbot on copyrighted work
“Training on articles” can refer to several separate stages. A dispute about one stage does not automatically settle the copyright questions at the others.
- Collection: text may be obtained from websites or other sources. Whether access was authorized and what was copied are factual questions.
- Dataset preparation: text may be copied, extracted, cleaned, or stored to prepare material for training. Copyright questions can arise from those copies even if a model does not later display an article.
- Training: a model processes examples to adjust its parameters. The legal question includes whether this copying is infringement or fair use; describing the model as having “read” an article does not answer it.
- Inference: the trained system generates an answer in response to a user’s prompt. A generated answer can raise its own questions if it reproduces protected expression.
- Memorization or regurgitation: a model may sometimes produce text closely resembling training material. Technical research treats memorization and reproduction as distinct from the claim that every model stores or outputs complete source documents: one study and another study examine these issues.
Copyright generally does not protect bare facts, but it can protect a journalist’s wording and other original expression. A concise factual answer or summary is different from a long reproduction of an article; the details of the text, its amount, and its use matter.
Why fair use is central
Under U.S. copyright law, fair use is assessed through four factors. No single factor automatically decides the question, and calling a use “transformative” does not by itself make it fair.
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- Purpose and character: courts consider how the material is used, including whether the use is commercial and whether it has a different purpose or character from the original.
- Nature of the work: creative and expressive works may receive different consideration from factual material. Journalism contains facts as well as protected expression.
- Amount and substantiality: the analysis considers both how much was used and whether the portion taken was qualitatively important.
- Market effect: courts consider harm to the market for the work and potential markets, including licensing markets.
OpenAI’s public position is that training can be transformative fair use, that models generally do not reproduce source articles, and that the Times’ examples relied on prompts designed to elicit passages. The Times’ competing theory emphasizes commercial use at scale, expressive journalism, alleged close reproductions, and possible substitution for reading or licensing its work. Those are opposing legal positions, not a settled statement of the law in this case. OpenAI’s account appears in its statement on journalism and case-information page.
The existence of licensing discussions or agreements in the broader market does not, by itself, prove that unlicensed training is unlawful. Nor does a fair-use defense mean that every use or output is protected.
Training, chatbot answers, and publisher competition are separate issues
Copies made to collect and train
The first question is what material was obtained and copied, by whom, and for what purpose. Publicly viewable content is not necessarily licensed for commercial copying or training. Allegations involving paywalled material also present different factual questions from material available on an open webpage.
Text produced in responses
A model can be trained on a work without routinely displaying it. Conversely, a particular output may raise an infringement question even while the legality of training remains contested. The Times cited examples it says show substantial reproduction under particular prompts. Such an example may show a capability, but does not on its own establish how ordinary users typically interact with a product or prove that a complete article is stored like a file in a database.
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Possible effects on news businesses
The Times argues that AI answers can compete with publisher products and weaken traffic, subscriptions, advertising, or licensing. Possible mechanisms include a reader asking for an article’s conclusions instead of clicking through, an answer reproducing enough reporting to reduce the value of a subscription, or a publisher losing referral visits and audience data. These are theories about market harm; their factual and legal significance must be established in the case.
What the court decided on April 4, 2025
Judge Sidney H. Stein’s April 4, 2025 decision ruled on motions to dismiss, not on evidence presented at trial. The court dismissed or narrowed some theories, including parts of the claims concerning removal of copyright-management information under the Digital Millennium Copyright Act. The core copyright claims were not all dismissed, so the litigation could proceed. Read the court’s opinion.
The ruling narrowed the case but did not answer the ultimate question of whether OpenAI and Microsoft’s use of Times journalism was lawful. A claim surviving dismissal is not a finding that the allegation is true, and the decision was not a final fair-use ruling.
Why the parties are fighting over discovery
Discovery is the evidence-gathering phase of a lawsuit. Here, disputes have included the relevance and preservation of ChatGPT-related logs, the Times’ use of generative-AI tools and development of its own AI products, and the parties’ positions on AI. The court record includes an order addressing discovery issues involving the Times’ AI use and positions: the order. Another order addresses ChatExplorer logs and other evidence: the Times-case order.
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OpenAI says the Times sought access to millions of private ChatGPT conversations, including conversations it says could reveal attempts to evade the Times’ paywall. OpenAI describes the demand as an extraordinary privacy invasion and says it challenged the request on privacy and relevance grounds. Its account of the dispute and response is available in its response to the data demands and privacy litigation update. These are a defendant’s descriptions of a contested discovery dispute.
The Times’ rationale, as reflected in the dispute over evidence, is that logs and related materials may help establish access, copying, output behavior, or damages. The court must weigh evidentiary relevance against the privacy interests of people whose conversations may have little connection to the claims. Discovery orders do not decide whether the alleged infringement occurred.
A Southern District of New York proceedings calendar listed a status conference involving the case for February 11, 2026. A calendar listing establishes a scheduled event, not what occurred at it or whether the merits were decided: the calendar.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a win could—and could not—mean
The Times case is one part of wider litigation over AI systems trained on books, journalism, images, music, and other works. Other cases can raise related questions about training copies, fair use, generated outputs, copyright-management information, licensing, and remedies, but their facts and rulings do not automatically decide this lawsuit. OpenAI’s case page discusses its view of related litigation; that account is the company’s advocacy, not a binding ruling in the Times case.
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A court could consider monetary relief or an injunction if liability is established. The Times’ requested remedies include substantial damages and destruction or remediation of models and training datasets, but a request is not a prediction of what a court will award. Any remedy would depend on proof, the legal claims, causation, proportionality, and feasibility.
- Identifying a particular work’s influence in trained parameters may be technically difficult.
- Retraining can be expensive and may not remove every learned association.
- Rules aimed at particular outputs or safeguards may be easier to administer than deleting a model, though their feasibility and scope would need to be assessed.
- Licensing or a commercial settlement could address some uses without establishing a general rule for all AI training.
These are possible remedial considerations, not outcomes already ordered in this case. A Times victory would not automatically prohibit every use of copyrighted material to train AI; a defense victory would not automatically make every generated answer lawful.
Is the lawsuit resolved?
No final merits judgment, definitive fair-use decision, settlement, or trial verdict is established by the sources cited here. The April 2025 ruling left core copyright claims alive, and the later calendar and discovery materials show procedural activity rather than a final answer. Anyone relying on the case’s current status should check the live docket, because a calendar entry or party update is not a substitute for a subsequent court order.
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