BlockBeats reported on August 22 that the U.S. Federal Trade Commission (FTC) is being urged to investigate the practice of some AI companies acquiring AI training data by purchasing, scanning, and destroying books. According to an open letter obtained by Axios, more than ten civil society organizations are calling on the FTC to use its regulatory powers to examine the so-called "destructive new methods of data acquisition" by large AI companies. Previously, The Washington Post, citing court documents, reported that Anthropic spent millions of dollars purchasing books, removing their spines, and scanning the pages to train Claude; Google, Microsoft, and OpenAI have also faced similar copyright lawsuits.

These organizations want the FTC to further determine whether such actions constitute unfair competition. They argue that by acquiring and destroying physical books, AI companies may be effectively "hollowing out" key data resources from the market, particularly rare books that could be permanently lost, while digital companies retain the last physical copies. These organizations warn that this "hoarding and destruction" practice could increase competitors' data acquisition costs and cut off crucial raw data for startup AI companies to train their models, thereby further widening the competitive barriers for leading AI companies.

However, these organizations are not asking the FTC to restrict AI model training, but rather hoping that regulators will focus on scrutinizing the destruction of existing works and intervene before large AI companies use this to gain a market advantage. The open letter states that this practice is not simply a data acquisition strategy, but could become another structural means for leading AI companies to build an "insurmountable systemic moat." Currently, the FTC under the Trump administration aims to maintain a relatively favorable regulatory environment for American companies while simultaneously expressing concern about market competition and the risks of monopolistic practices by large technology companies.

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