Microsoft exec called AI scraping ‘the largest theft of labor in human history,’ new unredacted filings reveal

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New unredacted information in the copyright lawsuit The New York Times brought against OpenAI and Microsoft three years ago reveals an admission that AI scraping was tantamount to theft, and that AI products pose a major threat to publications.

Per the lawsuit, a top Microsoft executive privately described the companies’ AI training practices as “theft,” and OpenAI’s own leadership said its AI models posed an “existential threat” to the publishers and journalists whose work trained them.

The unsealed material also details how the companies allegedly obtained and used that content by bypassing paywalls undetected, building training datasets via mass scraping, and deliberately stripping copyright notices from training data.

It’s worth noting that much of the new information comes from The Times’ own brief, not the underlying exhibits, which remain sealed. The quotes below are presented without their original context.

The unredacted filing is the latest escalation in the three-year-old lawsuit, in which The New York Times initially alleged the firms violated copyright law by training generative AI models on its content.

The question of whether AI firms can legally use copyrighted material to train AI has no clear answer, but judges have been largely favorable to AI companies’ arguments that training constitutes “fair use.” This legal rule lets people use copyrighted work without permission in certain cases, like parody, news reporting, or criticism. Earlier this month, the Trump administration contributed a brief in defense of OpenAI’s unlicensed use of copyrighted material to train its LLMs.

Several of the new admissions, however, run counter to OpenAI’s fair use defense, particularly the rule’s requirement that use doesn’t substitute or harm the market for the original work.

For example, Microsoft’s own data shows its Copilot “answer engine” caused click-through rates for The New York Times’ domain to drop as much as 93% compared to traditional Bing search.