Policy · The Verge ·

Microsoft: Almost No One Read NYT Articles via Chatbot

Microsoft told the court that virtually no users were accessing full New York Times articles through its chatbot, undermining a key claim in the NYT's copyright lawsuit against Microsoft and OpenAI.

Based on reporting by The Verge — analysis by dalili

Microsoft has told the court that virtually nobody was actually grabbing full New York Times articles through its chatbot, a revelation that undermines a central claim in the newspaper's copyright lawsuit against Microsoft and OpenAI.

The New York Times sued Microsoft and OpenAI in December 2023, alleging that the companies used millions of its articles to train AI models without permission and that chatbot outputs reproducing its content harmed its business. A key argument was that users were accessing full articles through the chatbot rather than visiting the Times website.

Microsoft's filing suggests that the actual usage data doesn't support that claim. The company stated that virtually no users were obtaining full Times articles through the chatbot interface. This data point could significantly weaken the NYT's damages argument, though the broader question of whether training on copyrighted material without permission constitutes infringement remains unresolved.

The case continues to be watched closely as a bellwether for how copyright law applies to AI training data. Similar lawsuits from other publishers and authors are pending, and the outcome could set precedent for the entire industry.

Key takeaways

  • Microsoft says virtually no users accessed full NYT articles via chatbot
  • Undermines key damages claim in NYT copyright lawsuit
  • NYT sued Microsoft and OpenAI in December 2023 over AI training
  • Case is bellwether for how copyright law applies to AI training data

Why it matters

The NYT v. Microsoft/OpenAI case is the bellwether for AI copyright law. If the damages argument weakens because actual chatbot usage was minimal, it could reshape how courts evaluate harm in AI training cases — focusing more on the training act itself rather than downstream distribution.

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