mlsanityData to 14 Oct 2026

AI systems out-persuade expert humans

arXiv 2606.16475 ↗First posted 15 Jun 2026cs.CY cs.AIcited by 5

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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.

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

10 of 38 references.

References · 10

  1. 2018AI safety via debatecited by 400
  2. 2024Debating with More Persuasive LLMs Leads to More Truthful Answerscited by 266
  3. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 188
  4. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 109
  5. 2023Deception Abilities Emerged in Large Language Modelscited by 101
  6. 2024The Political Preferences of LLMscited by 84
  7. 2023Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Noveltycited by 27
  8. 2025Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effectscited by 13
  9. 2025When Large Language Models are More PersuasiveThan Incentivized Humans, and Whycited by 13
  10. 2025A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societiescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.