The lawsuits against tech companies could shape what copyright means for AI, or simply serve as leverage for plaintiffs to secure more favorable licensing deals
The bar for fair use is typically that the new work doesn't compete with the original. … X: Sar Haribhakti / @sarthakgh : “If the NY Times successfully argues that reading a third party article to help its reporters “learn” about the news before reporting their own version of it is copyright infringement, it might not like how that is turned around by tons of other news organizations against the NY... [image] Cecilia Ziniti / @ceciliazin : Nice thread by @martyswant of DallE making cartoon characters. I tested it by asking my 3-year-old to guess them. She got only 7 of the 12. Not Winnie the Pooh and not Mickey. The fair use art generation cases will be fascinating! Julia Alexander / @loudmouthjulia : It certainly feels somewhat inevitable, based on what I've read, that OpenAI will institute some form of ContentID. YouTube's big answer to similar concerns was to institute one of the most important but controversial tools. And it's benefitted the studios/labels enormously. Marty Swant / @martyswant : I tried making images of popular cartoon characters via DALL-E 3 and it generated some that looked very similar to the actual characters and others with less resemblance. Here's a few from the other day. Prompts in the ALT text. [image] Walter Isaacson / @walterisaacson : These will be the most important cases for journalism and publishing in our lifetime. If AI companies have to cut deals with news organizations and publishers to license their content feeds for use as AI training data, that could save local journalism as well as magazines and... See also Mediagazer
Context & Ripple Effects
The dispute follows the NYT's lawsuit alleging use of millions of its articles to train OpenAI systems, putting publisher material at the center of the AI-training copyright debate. The core tension is whether litigation produces a judicial fair-use boundary or improves publishers' bargaining position for licenses.
The issue extends beyond text: earlier coverage flagged potential claims tied to AI-generated images that resemble protected characters, suggesting that training and output-based infringement may develop along separate legal tracks.
First-order effects
- Publishers and AI companies face immediate pressure to negotiate licensing terms while the scope of fair use for training remains unsettled.
- The NYT case makes the treatment of training data and model outputs a central legal risk for OpenAI and Microsoft, rather than solely a product or reputation issue.
Second-order effects
- A licensing-focused outcome could give other publishers a template to seek compensation, while AI companies may need to weigh deal costs against continued litigation exposure.
- Disputes over generated images, including concerns about DALL-E outputs resembling copyrighted material, could force separate safeguards for output handling even if training is defended as fair use.
Third-order effects
- If courts or settlements establish repeatable licensing terms, copyrighted material could become a more formalized input market for AI development rather than an assumed training resource.
- If broad fair-use arguments prevail, the durable battleground may shift toward whether systems reproduce protected expression and what technical controls are expected; the available coverage does not establish which path will win.
The trend: AI copyright disputes are turning control of training data and generated outputs into a negotiation over both legal precedent and the commercial value of content licenses.