Armin Ronacher ran an experiment with Pangram, an AI detector that claims a 0.0041% false positive rate for flagging human text as AI. After David Sacks tweeted something that Pangram immediately labeled as AI-generated, Sacks dismissed the detector as bogus. Ronacher decided to test whether Pangram's accuracy holds up in practice by using Claude Opus 5 to regenerate Sacks's tweet based on a detailed prompt about "pacing the frontier"—a reference to Dario Amodei's post on slowing AI development and Sam Altman's agreement. The AI output read entirely synthetic, exactly as Pangram predicted. The real test came when Ronacher manually rewrote the AI-generated text from scratch without using any LLM. He kept the same structure and core arguments but rewrote every sentence, achieving only 50% similarity to the original AI version. He then ran spell-check with an LLM but didn't regenerate any content. Pangram still flagged this entirely human-written text as 100% AI. This matters because Ronacher suspects the problem isn't Pangram's accuracy so much as how LLM-assisted writing works: when you use an AI tool to structure your thoughts, the underlying patterns remain recognizable to detectors, even if every word is yours. The experiment exposes a real gap between Pangram's claimed performance and what happens in messy reality. A detector with a 0.34% miss rate and 0.0041% false positive rate sounds bulletproof on paper, but those numbers assume clean scenarios. Once human writers lean on LLMs for scaffolding—prompts, outlines, spell-check—the detector can't distinguish between "AI-assisted human writing" and "fully synthetic text." The article cuts off mid-thought, but the implication is clear: these detectors may be technically competent at their narrow job while remaining useless for what people actually want to know.