Substack has identified a real problem. It may have chosen the worst possible solution.On July 21, Substack co‑founder Chris Best announced a new feature called “scan for AI text.” Readers can run posts, Notes, comments, and replies longer than 100 words through an AI detector made by Pangram. The resulting report estimates how much of the text was written by a human and how much was generated or assisted by artificial intelligence.An unexplained algorithmic score is not proof of authorship.Best calls the problem “Claudefishing”: Passing off machine‑generated writing as the product of a human mind. He argues that readers deserve to know whether they are engaging with a person or, in his words, something “written by no one.”That concern is legitimate. Anyone who spends much time online can recognize the rise of AI slop: The smooth, bloated, strangely lifeless prose that says very little in far too many words. It’s taking over LinkedIn, search results, marketing blogs, and, increasingly, journalism.But Substack’s solution risks creating another problem: a culture in which every writer is treated as a potential fraud and every reader is encouraged to become an investigator.Pangram is good, but still not good enoughPangram is not one of the primitive AI detectors that declares that Dickens used ChatGPT. It may be the most capable product in its category.The system is a machine‑learning classifier trained on both human‑written and AI‑generated documents. It breaks text into tokens, converts them into numerical representations, and looks for patterns its model associates with human or machine authorship.Pangram says it repeatedly searches large collections of human writing for passages its model gets wrong, adds those difficult examples to its training data, and retrains the detector.Its current model, Pangram 3.3, also uses an architecture called EditLens, which attempts to estimate not merely whether AI was involved but how extensively a human text may have been edited by AI.Pangram claims an overall accuracy above 99% and a false‑positive rate of one in 10,000 human documents. Independent research has generally found it far more accurate than competing detectors. One University of Chicago study found almost no false positives among roughly 3,000 medium‑ and long‑form samples.That sounds reassuring until Pangram is applied to individual human beings.A detector can be excellent at identifying broad trends across millions of documents and still be inappropriate as a lie detector for a particular writer. At sufficient scale, even a one‑in‑10,000 error rate produces innocent people who must somehow prove that they wrote their own words.Pangram CEO Max Spero has said the detector should “never be the ending arbiter.” But he has also acknowledged that the model’s internal reasoning is largely uninterpretable. It can provide a score, but it cannot necessarily explain which meaningful piece of evidence caused it to reach its verdict, according to the Atlantic.That is a serious problem when the verdict carries the implicit accusation that an author is a liar.Substack’s own moral authority was falsely flaggedThe irony is hard to miss.In his announcement, Best invokes writer Freddie deBoer to explain why undisclosed AI writing is a betrayal of the reader. Best quotes from a June essay by deBoer arguing that the value of art lies partly in knowing there is a human consciousness behind it.But just two days before Best announced the Pangram integration, deBoer published a detailed account of Pangram failing to recognize his own human writing.RELATED: Is Google's next Android feature always-on eavesdropping? Yuliia Kaveshnikova/Getty Images Someone had accused deBoer of using AI after Pangram classified a roughly 300‑word section of one of his essays as entirely AI‑written. DeBoer knew that was false.When he submitted the full 5,000‑word essay, however, Pangram declared it entirely human. When he divided the supposedly artificial passage into smaller pieces, those pieces were also classified as human.The same words became human or artificial depending on how much surrounding text deBoer submitted.So the writer Substack chose as its moral authority on the importance of human authorship had just become a case study in the inability of Substack’s chosen detector to identify human authorship reliably in an individual case.Best apparently agrees with deBoer that readers should not be deceived about whether a human being stands behind a piece of writing. But Pangram had placed deBoer in exactly the opposite position: A human writer was forced to defend himself against a machine suggesting that no human stood behind his words.That contradiction should have given Substack pause. Instead, it gave every reader access to the machine.Technology journalist Taylor Lorenz was also accused of using AI after Pangram flagged her work.