Import AI 462: Superpersuasion; self-sustaining AI; paths to ASI

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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.”

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 restart the fabs? It’s quite possible that would take decades. Because even though you might have the textbooks, there’s a lot of tacit knowledge inside these machines,” Lee notes. Ajeya’s response is that this is something the tech might be able to route around: “There are two counters to the tacit knowledge hypothetical. One is that we’d have trained AI systems with reinforcement learning on that tacit knowledge because it’s profitable to automate what the Taiwanese worker was doing. The other is that AIs might get really generally intelligent in the sense of quickly figuring out new things by trying them, reading textbooks, and experimenting efficiently.”

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”.