# AI systems out-persuade expert humans

arXiv: https://arxiv.org/abs/2606.16475 · HTML: https://mlsanity.com/paper/2606.16475/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty](https://mlsanity.com/paper/2303.07462/) (2023)
- [Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects](https://mlsanity.com/paper/2504.09865/) (2025)
- [When Large Language Models are More PersuasiveThan Incentivized Humans, and Why](https://mlsanity.com/paper/2505.09662/) (2025)
- [A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies](https://mlsanity.com/paper/2505.00036/) (2025)

## Built on by (most cited first, 5 of 5)

- [Individual-level interventions against sycophantic AI reduce its appeal but not its persuasiveness](https://mlsanity.com/paper/2607.25166/) (2026)
- [Measuring AI Accountability Through Argumentation Analysis: Can Model Reasoning Withstand Scrutiny?](https://mlsanity.com/paper/2609.05088/) (2026)
- [AI Persuasion as a Threat to Human Control](https://mlsanity.com/paper/2609.14796/) (2026)
- [LLM Persuasion Is in the Eye of the Evaluation](https://mlsanity.com/paper/2610.10232/) (2026)
- [The AI's Poker Face: Contrasting Persuasion and Sycophancy in Voice-Based Multi-Turn Conversations](https://mlsanity.com/paper/2610.14124/) (2026)

## Abstract (arXiv)

Many societal decisions are settled by contests of persuasion. Conversational AI is a powerful new entrant in these contests, but whether it can out-persuade skilled and highly incentivized humans has remained unclear. Here, in a series of four preregistered experiments (n = 18,978 conversations from 6,923 people), we pitted AI systems against a range of human persuaders, including laypeople, winners of a separately preregistered four-round online persuasion tournament, professional canvassers, and world championship debaters. We found that AI systems were reliably more persuasive than expert humans, even when expert humans chose their issues, researched in advance, underwent hours of live, structured practice, and were incentivized with pounds1,000 cash bonuses. In a follow-up study, AI's advantage persisted after experts received a coaching tool that let them practice against the AI that beat them, review their performance history, and see what AI would have said at key moments. We found converging evidence that AI's advantage stemmed from rapidly deploying larger quantities of information: after coaching, expert humans could tie an AI constrained to respond at human speeds and with human-length messages. In a final study, we show that AI's advantage extends to consequential real-world behavior: AI was nearly 3x more effective than professional canvassers from a UK fundraising firm at raising real-money donations to Save the Children. Together, these results establish that frontier AI systems out-persuade expert humans in conversation, with significant implications for political communication.