When an X account used Pangram to accuse Canadian-Haitian novelist Thelyson Orelien of writing his Goncourt-listed debut with AI, newsrooms ran the book through a dozen AI detectors and got every possible answer. This article sets out what each test found, how the tools work, what independent benchmarks from Chicago, Brussels and the Authors Guild show, the arithmetic of false positives, and how to use AI detectors without wronging anyone.
AI-assisted writing is now the hardest thing on the internet to label, and that is the argument Pangram co-founder and chief executive Max Spero brought to TechCrunch’s Equity podcast on 2 September 2026. The 24-minute episode, produced by Theresa Loconsolo and hosted by Rebecca Bellan, was published under a blunt headline: AI detection is harder […]
Pangram, a 24-person Brooklyn startup, has become publishing’s de facto AI referee: its scores cancelled a Hachette novel, embarrassed The New York Times and cast doubt on a major literary prize, and Substack now bakes the detector into its platform. Independent NBER benchmarking really does put it far ahead of GPTZero, Originality.ai and Turnitin on false positives — yet humanizer tools evade it over 96 percent of the time, verdicts wobble on short excerpts, and bias questions hang over who gets accused. This article weighs the evidence from WIRED’s investigation, the Chicago Booth benchmark and the company’s own disclosures to answer the practical question: when should you trust a Pangram score, and when should you not?
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