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Recent experiments with Anthropic\u2019s Claude Opus 4.7 reveal that advanced generative AI systems can identify writers from diverse and unpublished texts, threatening anonymity and raising ethical questions about AI\u2019s pattern recognition capabilities.<\/p>\n<\/div>\n
As generative AI systems become better at pattern recognition, they are also becoming more useful at something far less celebrated: identifying who wrote a text. That unsettling possibility sits at the centre of a recent essay by Kelsey Piper in The Argument, where she described feeding unpublished writing into Anthropic\u2019s Claude Opus 4.7 and watching it repeatedly name her as the author, even when the material came from different periods of her life and from very different registers.<\/p>\n
Anthropic describes Claude Opus 4.7 as its most capable model, built for long-running, complex work and tuned for reliability, with the company saying it has been extensively tested for safety and security. Its public materials also emphasise strong performance on reasoning, coding and document analysis. But Piper\u2019s experiments suggest that those same pattern-matching strengths can be turned towards a more intrusive use: attributing authorship from short stretches of text, even when the writer is using an unpublished draft or a piece written years earlier.<\/p>\n
What makes the account especially striking is that it was not limited to one genre or one obvious sample. According to Piper, the model identified her from a short excerpt of a political column, a school report, a fantasy manuscript and even a college application essay she wrote 15 years ago. Other systems were less consistent, but the result was still enough to underscore a broader point: for people with a substantial public writing history, anonymity may now be far more fragile than many assume.<\/p>\n
That concern is not new, but AI is giving it fresh force. Stylometry, the long-established practice of analysing writing style to infer authorship, has been used for years in scholarship, journalism and investigations. What is changing is the speed and accessibility of the process. Tools that once required specialist effort can now be run in seconds, and even when they are wrong they may still be persuasive enough to send a researcher or journalist further down the trail.<\/p>\n
There are also limits. The New York Times recently reported on John Carreyrou\u2019s efforts to identify Bitcoin\u2019s pseudonymous creator, Satoshi Nakamoto, which showed how hard it can be to move from linguistic clues to a defensible conclusion. His work combined stylistic observations with real-world leads, illustrating that text analysis alone is rarely decisive. Yet the fact that AI can now perform similar screening so quickly means the threshold for suspicion has been lowered, even if the final verdict remains uncertain.<\/p>\n
That is the deeper warning in Piper\u2019s piece. Anonymous writing has never been perfectly secure, but it once offered a meaningful buffer between a voice and a name. With models such as Claude Opus 4.7, that buffer is shrinking. Even if the results are imperfect, they are likely to be good enough to encourage more probing, more cross-checking and more attempts at unmasking. In practice, that may matter almost as much as perfect accuracy.<\/p>\n
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