Publishers Behind Nearly 400 Newspapers Sue OpenAI, Microsoft Over AI Training
Updated
Updated · Bloomberg Law · Jun 24
Publishers Behind Nearly 400 Newspapers Sue OpenAI, Microsoft Over AI Training
3 articles · Updated · Bloomberg Law · Jun 24
Summary
A coalition representing nearly 400 newspapers sued OpenAI and Microsoft in federal court in New York, calling it the largest legal effort yet led by local and regional publishers over AI training.
The complaint alleges the companies secretly crawled publishers’ websites, copied articles onto their servers, stripped copyright management information, and reproduced the works through user prompts without paying publishers.
Publishers say they spent billions of dollars creating and protecting that content, including behind paywalls, and are seeking statutory damages plus injunctive relief for copyright and DMCA violations.
OpenAI said its models are trained on publicly available data and are grounded in fair use; Microsoft did not immediately respond.
The case adds local news outlets to a widening wave of AI copyright litigation, with plaintiffs arguing unchecked scraping could become a death knell for local journalism.
Is AI training on news articles a protected fair use, or is it the largest copyright theft in history?
Can local news, with its paywalls bypassed, survive the AI era without new legal protections?
30+ Local News Outlets Sue OpenAI and Microsoft: Copyright Clash Puts Journalism’s Survival at Stake
Overview
On June 24, 2026, a coalition of local news publishers filed a major federal lawsuit against OpenAI and Microsoft, accusing them of systematically copying and using copyrighted news content without permission to train AI models like ChatGPT and Copilot. This unauthorized use has helped these tech giants generate billions in market value, yet none of that value has gone to the publishers. The lawsuit argues that AI chatbots, built on this uncompensated work, threaten the financial stability and survival of local journalism, and put the public’s access to reliable local information at risk.