Realtime AI News
Report: Anthropic's Secret Project Destroyed Millions of Books to Train AI
A new report claims that Anthropic ran a secret project that destroyed millions of physical books as part of its AI training data efforts. The revelation adds to mounting scrutiny over how frontier labs acquire the content used to build their models.
A new report claims that Anthropic ran a secret project that destroyed millions of physical books as part of an effort to build training data for AI. The report, picked up by GreekReporter, describes the project as operating quietly at a scale measured in millions of books.
The headline claim — that the books were destroyed — points to a workflow in which physical volumes were processed and then discarded rather than returned or preserved. The report frames the destruction as part of how the project handled the books after using them.
Anthropic has not yet publicly responded to the claims in the report. The reporting does not detail exactly how the books were used in training, what kinds of titles were involved, or where the operation took place.
The story lands at a moment when frontier AI labs' data-sourcing practices are under intense legal and public scrutiny. Copyright disputes with publishers and authors have become a defining legal risk for the industry, and physical-book digitization sits squarely in that contested territory.
If the report's account holds up, the destruction of physical books adds a new and particularly visible dimension to the debate: not just whether copyrighted text can be used for training, but whether rare or irreplaceable physical collections were lost in the process.
Why it matters: training-data provenance has become a reputational and legal battlefield for every major lab. A story about millions of destroyed books is the kind of detail that turns abstract copyright debates into concrete public outrage.
What to watch next: whether Anthropic issues a response, whether any publisher or author acts on the report, and whether other labs' physical digitization efforts come under similar scrutiny. The report adds to a growing list of contentious data-sourcing practices facing frontier AI labs.
Sources
Why it matters
Training-data provenance disputes are widening from copyright lawsuits to the fate of physical collections; how Anthropic responds will shape public and regulatory views of AI data acquisition.
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