Unsealed filings show a Microsoft exec called AI scraping "the largest theft of labor in human history" in an internal memo
Newly unredacted documents in the New York Times' copyright suit against OpenAI and Microsoft show internal alarm, in January 2023, over the scraping practices both companies kept using, plus evidence Times click-through traffic fell as much as 93% after Copilot's launch.
Newly unredacted filings in In Re: OpenAI Inc. Copyright Infringement Litigation (1:25-md-03143, S.D.N.Y.) show a Microsoft executive privately calling the industry's scraping practices "the largest theft of labor in human history," TechCrunch reported September 17. The consolidated case bundles claims by the New York Times, Daily News, Center for Investigative Reporting and other publishers against OpenAI and Microsoft; Merit AC covered its cross-motions for summary judgment on September 13. The line comes from a January 2023 internal memo by Brent Hecht, Microsoft's director of applied science, who called it "an astonishing theft of unprecedented proportions." He wrote it more than a year before the Times filed suit, and years before either company changed the underlying practice.
What the documents show
Per the filings, OpenAI employees developed methods to pull New York Times content past its paywall undetected, and training datasets built by both companies stripped copyright notices before use. One Common Crawl-derived set alone reportedly contained more than 2 million documents from nytimes.com, part of a broader collection of over 91,692 copies of Times, Daily News and other publisher works. The filings also cite evidence that Microsoft's Copilot cut the Times' click-through rate by as much as 93% compared with traditional search referrals, and quote OpenAI's Nick Turley describing publishers as facing an "existential threat" from chatbots. Microsoft CEO Satya Nadella has testified that paywalled content should require licensing.
Why this matters beyond the courtroom
An internal admission, a year before litigation, that the company's training methods amounted to "theft" is different evidence from the fair-use arguments both sides are making to Judge Sidney Stein. For buyers weighing AI vendor risk, training-data provenance is more than a compliance checkbox. Internal paper trails become deposition material, and eventually licensing costs or judgments that flow down the vendor chain to the enterprise customers using these models today.