The New York Times lawsuit against OpenAI and Microsoft has become one of the clearest tests yet of how generative AI collides with journalism, copyright, and search. The case raises two related questions: whether training on copyrighted material can be defended as fair use, and whether AI products can lawfully present publisher content inside a chatbot experience.
The source article argues that the case is difficult for OpenAI and Microsoft because the NYT has shown more than 100 examples in which GPT-4 reproduced New York Times text almost verbatim. At the same time, the facts described in the source also show why the legal and technical questions are not simple.
What the NYT says GPT-4 reproduced
The lawsuit cites more than 100 instances where GPT-4 allegedly produced New York Times text with very close similarity to the original. That makes the output issue central to the dispute: if a model can be prompted into generating protected material, courts may have to decide how much that matters for the legality of the broader system.
But the examples described in the source were not ordinary ChatGPT conversations. The NYT used excerpts from articles in its prompts, including material such as an article teaser, and worked through API/Playground as a text completion model rather than normal chat mode.
That distinction matters because the source says normal ChatGPT chat mode is less likely to provide a copy of an NYT article in response to a regular prompt, partly because stricter safety rules apply. Still, the source also notes that copying could happen, and that a prompt designed to elicit a verbatim passage may still be relevant in a copyright case.
OpenAI and Microsoft may argue that such outputs are not the intended behavior of ChatGPT. The source describes the likely explanation as overfitting, meaning the model may have been trained so intensively on high-quality material that parts of that material can reappear. Under that view, the copied text would be a technical defect rather than the purpose of the system.
The fair use argument remains unresolved
The source makes clear that the NYT examples do not automatically defeat the core argument from major AI companies: that training models on data can be a transformative use and therefore fair use. The legal issue is not only whether a model can reproduce content, but whether the training process itself is protected.
That is why the case has broader stakes than a single set of outputs. If courts accept that AI training is fair use, OpenAI and Microsoft would have a stronger defense for foundational model development. If courts reject that position, the impact could reach far beyond this lawsuit.
The source describes several possible consequences if the NYT prevails. Models like GPT-4 could face demands to be destroyed, retrained, or supported by licensed training data. Any of those outcomes would create a major shift for an AI industry that has relied heavily on data from the Internet without paying for each item individually.
The cost question is also central. The source says the development and operation of AI systems is currently a loss-making business, even before adding potential licensing costs. It also cites Meta’s submission to the US Copyright Office, in which Meta described licensing training data at the required scale as unaffordable and stated: "Indeed, it would be impossible for any market to develop that could enable AI developers to license all of the data their models need."
Browsing chatbots create a different publisher problem
The dispute is not only about training data. The source highlights web search-enabled chatbots as a separate and potentially more serious issue for publishers. These systems can crawl news sites and reproduce article text inside a chat window, reducing the need for a reader to visit the publisher’s site.
Traditional search engines also rely on publisher content, but the source draws a distinction: search results usually show a short snippet and place the link to the publisher’s site at the top. That arrangement can send traffic back to the source and create benefits for both sides.
Chatbots change that balance. If the answer appears directly in the chat interface, the chatbot provider may capture more of the value while the publisher loses traffic. The source says major chatbot providers recognize this dilemma but have not yet offered solutions.
OpenAI acknowledged the issue at the launch of the browser plugin in March 2023, saying: "We appreciate that this is a new method of interacting with the web, and welcome feedback on additional ways to drive traffic back to sources and add to the overall health of the ecosystem."
The same concern applies to Microsoft’s Bing Chat, which the source says also copied entire NYT articles according to the case file, and to Google’s Search Generative Experience. OpenAI later limited web page summaries to about 100 words when it redesigned ChatGPT’s browsing feature, a limit the source says was presumably meant to respond to the copyright debate.
Hallucinations add a brand risk
The NYT also alleges that Microsoft’s Copilot, formerly Bing Chat, has attributed information to the New York Times that the newspaper never published. This is a different harm from copying. Instead of reproducing real work, the chatbot may attach the NYT name to material that is not in the source article.
The source gives several examples. In one, a prompt asked for 15 foods that are good for your heart while referencing an NYT article, and the system generated a list supposedly drawn from that article. The article did not contain that list.
In another example, the NYT asked for a specific paragraph in an article, and Copilot cited a paragraph that was not there. The source explains this as consistent with a broader limitation: large language models are not built for precise information retrieval and may not be reliable substitutes for search engines.
The source also describes a GPT-3.5-turbo prompt asking for an article about a study linking orange juice and non-Hodgkin's lymphoma. The model produced fictitious New York Times statements about the study, but the study did not exist and the NYT had never reported it.
These examples strengthen the publisher-side concern. A chatbot can harm a news organization not only by using its work, but also by creating the impression that the organization published claims it did not make.
Why licensing deals matter to the case
The source points to OpenAI’s cooperation with AP and Axel Springer as an important part of the broader picture. The Axel Springer cooperation involves OpenAI distributing licensed news from Axel Springer media via ChatGPT.
That matters because it suggests news content can be treated as licensed material inside AI products. The source argues this may support the NYT’s claim that OpenAI is competing with newspapers, or at least trying to become a platform that captures part of the value created by news publishers.
The NYT did not reach such an agreement with OpenAI and Microsoft. According to the source, the lawsuit says the NYT demanded "fair value," but negotiations failed. The Axel Springer deal reportedly cost tens of millions of euros, plus ongoing licensing fees, and the source suggests the NYT may have wanted more.
The outcome of the case could therefore influence both law and business strategy. If AI companies can rely on fair use, licensing may remain selective. If they cannot, the economics of generative AI may have to change, especially for models and chatbot products that depend on publisher material.