AI说服力超越人类专家，研究揭示四大实验结论

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now AI can decisively out-persuade humans:…“AI systems were reliably more persuasive than expert humans”…Researchers with the University of Oxford, UK AI Security Institute, Stanford University, and the London School of Economics and Political Science, have studied how well AI systems can persuade humans to change their minds around policy issues and change how much money they might donate to charity. The results are definitive: across four experiments involving 18,978 conversations across 6,923 people, AI systems are, today, better than humans at text-based persuasion with real world consequences – though humans can be equivalent to them if we place some artificial constraints on the AI systems. “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 £1,000 cash bonuses”, they write. “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.” “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.” The strongest persuaders were Opus 4.1 and Opus 4.6, followed by a range of models from OpenAI (GPT-4o and GPT-5.4), Google (Gemini 2.5 Pro), and xAI (Grok 4.20). What they studied and what they found: The researchers evaluated the AI systems in four different studies.Study 1 – persuasion: “Persuadees first rated their agreement with one of 10 prespecified UK policy stances on a 0–100 scale, then were randomized in real time (via a custom multiplayer platform) to engage in a text conversation with either an AI or a human persuader,” they write. “The results from Study 1 show that, on average, AI exceeded every class of human persuader we tested: random laypeople, tournament-selected laypeople, and even elite debaters.” Study 2 – human coaching: In study 2, the researchers “gave 43 returning Elite Debaters a coaching tool built around the AI that had beaten them. The tool let debaters chat with the AI, see how it had been prompted, view their own Study 1 transcripts annotated with how much each conversation had shifted the persuadee’s attitude, and let them see, for any point in any past transcript, what the AI would have said in their place”. The results of this study were an improvement in the performance of the humans, but none of them were better than the AI. “Coaching therefore narrowed but did not close the human–AI gap.” Study 3 – constrained AI: Next, the researchers sought to limit the AI to try and give humans more of an advantage. “When forced to write human-length messages at human writing speeds, AI’s advantage over the strongest human comparator within Study 2 (Coached Elite Debaters) collapsed from +4.1 pp to a non-significant 0.0 pp”, they write. “The rate at which AI produces written content is likely to be the source of its persuasive edge… the largest reductions in persuadees’ post-conversation partner ratings associated with constraining AI were concentrated on the two informational items: the perceived strength of the partner’s arguments and how much persuadees felt they learned from the conversation”. Study 4 – real world expertise and real world money: They recruited 19 very experienced canvassers from a UK firm, then they attempted the same tasks as in Study 1. “AI still exceeded Professional Canvassers by 5.9 pp”. This effect persisted when evaluating for real money donations – the researchers “collaborated with the UK canvassing firm AppcoUK to center Study 4 on the cause their canvassers were best equipped to fundraise for: Save the Children. The canvassing team provided by AppcoUK had operated real fundraising operations for the charity from 2016 to 2023, raising £824,297 from 22,583 donors over that period. After conversing with AI or one of 18 canvassers recruited from AppcoUK, persuadees were given the opportunity to donate any portion of a £1 study bonus to Save the Children”. Here, the results were significant again: “AI elicited substantially more real-money giving than the canvassers, exceeding them by +10.8 pp of the £1 bonus,” they write. AI raised “both the share of persuadees who donated anything and the average donation among donors”. Why this matters – if AI can out-persuade us, those who control AI can change society: “One effect of AI that can out-persuade even human experts could be a consolidation of influence among already-powerful actors”, they write. On the other hand, “if highly capable persuasion became cheap and widely available, it could help under-resourced actors (e.g., pro se litigants and public defenders, small charities, grassroots activists) compete against more established and better-funded rivals, narrowing long-standing gaps in access to justice and assisting civic advocacy more broadly”. This lays out a societal choice ahead of us, which is how to monitor the use of AI for persuasive purposes and how to see how these capabilities alter the balance of power between various actors. Do we want to solely let the market allocate these capabilities? That’s one way of doing it, though it implies that things like advertising and marketing will get far more effective, perhaps creating negative externalities. On the other hand, if you made persuasive capabilities solely the domain of governments, you’d then risk concentrating power within governments – something that could be acutely dangerous if wielded by authoritarian regimes to keep themselves in power. We will have to make choices about what to do with this technology, and as they say in politics, ‘not voting is voting’. “Our findings establish frontier AI as a more capable conversational persuader than the most prepared, incentivized, and expert humans we could recruit. Training humans does not appear to close that gap,” they write. “As access to these systems continues to grow, the question is no longer whether AI can out-persuade humans but how, where, and on whose behalf this capability will be exercised.” Read more: AI systems out-persuade expert humans (arXiv). Tweet thread about the research (Kobi Hackenburg, researcher at AISI). *** When could we get self-sufficient AI? It all depends on humanoid robots:…What comes after RSI? Self-sustaining AI…I’ve spent a lot of this year writing about recursive self-improvement – the notion that we might soon build AI systems that are smart enough they can autonomously design their own successors. But RSI still requires datacenters and these datacenters require equipment and electricity and everything else. An interesting interview in Asterisk magazine asks the question about when we might get self-sustaining AI, which one of the interviewees – Ajeya Cotra, a forecaster and on staff at METR, defines as “AI systems integrated with physical infrastructure — factories, mines, fabs, robots to operate all of those — such that they don’t need any cognitive or physical inputs from human labor to keep growing their own population.” How far away is it? Ajeya thinks we could get self-sustaining AI within 10 years (so by 2036). The other interviewee, Timothy B. Lee, journalist and author of Understanding AI, has much longer timelines: “less than 10% chance that it happens within 20 years. I’d say there’s a 10 or 20% chance it’s never, and my median would be 50 years.” What are some challenges – tacit knowledge might be one: “Imagine if all the employees in the entire semiconductor industry disappeared — the machines and textbooks remain, but none of the people. How long would it take for the rest of humanity to res

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