Machines that argue back: AI chatbots as persuaders

Series: How minds are moved · The modern machinery · 6 of 7

In 2024 researchers asked 2,190 people who believed a conspiracy theory to argue about it with GPT-4 for about eight minutes. On average their belief dropped by about 20%, and it stayed lower two months later. In 2025, on Reddit, researchers from the University of Zurich let AI accounts argue with real people without telling them, sometimes posing as a trauma counsellor or a rape survivor. Same kind of tool, two very different uses. My AI assistant and I went through what the 2024 to 2026 research says about machines that argue back, and I will also tell you how I keep my own AI honest.

Contents · 9 sections

TL;DR #

• Chatbots are now measurably persuasive in conversation, in some tests more than humans, mostly because they can produce many relevant arguments fast and tailor them to the person.
• The same ability can reduce false beliefs, as in the conspiracy study, or spread them: the biggest 2025 study found the most persuasive settings also made the models less accurate.
• Undisclosed AI persuasion of real people, like the Zurich Reddit experiment, crossed ethical lines, and the results were never published.
• The defence is the same as with any persuader: ask for sources, check the claims that matter, and notice when you are being out-argued rather than informed.

What “persuasive AI” means #

A chatbot does not have a personality or an agenda of its own. It produces text that fits the conversation and the instructions it was given. What makes it persuasive is practical: it never gets tired, it answers every objection, it can pull in facts at once, and it can tailor its arguments to what you say about yourself. A human debater has some of those strengths. A model has all of them, at scale, for almost no cost.

The correcting edge #

Conspiracy beliefs. Thomas Costello, Gordon Pennycook and David Rand published their study in Science in September 2024. Participants described a conspiracy theory they believed and the evidence that convinced them. GPT-4 Turbo then answered with specific counter-evidence. Belief fell by roughly 20% on average, about one in four participants stopped believing the theory, and the effect lasted at least two months. It even reduced belief in other conspiracies that were not discussed. It worked less well for people whose theory was central to their identity.

The researchers’ explanation is important: it was not flattery or tricks. People had never met counter-arguments that addressed their exact reasons. Facts tailored to the person worked.

The misleading edge #

Debates. In a study published in Nature Human Behaviour in 2025, Francesco Salvi and colleagues had 900 people debate either a human or GPT-4. When GPT-4 was given a few personal details about its opponent (age, gender, politics and similar), and one side clearly out-persuaded the other, the AI won 64% of the time. Without the personal details, it did no better than humans.

What drives persuasion. The largest study so far, by Kobi Hackenburg and colleagues at Oxford, the UK AI Security Institute and others, was published in Science in December 2025. It involved 76,977 UK adults talking with 19 different language models about 707 political issues. Two findings matter most. First, how a model was trained and prompted mattered more than its size or personal targeting; small models could match big ones once tuned for persuasion. Second, the methods that made the models most persuasive, mainly packing answers with lots of claims, also made them less accurate. To show it, the team rated the accuracy of 466,769 claims with an AI model (GPT-4o) and checked a sample against two professional fact-checkers, whose ratings agreed closely with the model’s.

That is the double edge in one sentence: a machine tuned to win the argument tends to stop caring whether every claim is true.

The Zurich Reddit experiment. From November 2024 to March 2025, researchers from the University of Zurich ran AI-generated accounts in the subreddit r/changemyview, where people invite others to change their opinions. The accounts posted about 1,700 comments without disclosing they were AI. According to the moderators, some posed as a rape victim, a trauma counsellor who works with abuse, and a person opposed to specific social movements, and some replies were tailored using personal details inferred from users’ posting histories. The moderators announced it in April 2025. Reddit’s chief legal officer said the company would send formal legal demands. The university’s ethics commission had recommended changes, and the faculty later issued a formal warning to the lead researcher. The university told reporters the results would not be published and that its ethics review would be tightened.

People argued about whether the experiment showed anything new. Almost nobody argued that deceiving real people about who they were talking to was acceptable.

At scale, for real. Covert influence operations already use chatbots. In 2024 OpenAI and Meta reported operations that used AI to write comments and articles, including the Israeli STOIC campaign and networks linked to Russia, China and Iran. So far those reports say AI helped them produce more content faster, not reach many more real people.

What it feels like from the inside #

A good AI argument does not feel like pressure. It feels reasonable. It is polite, it agrees with your good points, it answers what you just said, and it has a fact for everything. That is exactly why it can move you in either direction. The feeling of “this answer is so complete” is not evidence that it is true. Completeness is easy for a machine; accuracy is the hard part.

How I keep my own AI in check #

This blog is AI-drafted and human-signed: an AI assistant drafts the posts from my notes, my library and my news engine, and I review them and put my name on them. So I owe you an honest account of how I try to stop it from doing to you what the studies above describe. I describe the whole setup, library, news engine and grading, in a separate post in this series about why I show my machine; here is the short version.

• Sources. Every factual claim must trace back to something checkable: an official report, a court ruling, a peer-reviewed paper or a named news report. The URLs go into the post’s source list, and the books from my library are named in the text.
• Grading. Not all sources are equal. A court ruling or a regulator’s final report counts for more than a news paraphrase, and a company’s statement about itself is a claim, not a finding. My news engine counts many outlets that copy one wire story as one voice, not many, and fact-checkers count as one outlet among others, with no special standing.
• Review. The drafting AI can only propose posts, never publish them. A second agent reviews each proposal against the web and against my rules for balance, then I read it before anything goes live. My systems run a scan for leaks of private details before every proposal.
• Disclosure. You know an AI drafts these posts. That is the line the Zurich experiment crossed: not using AI, but hiding it.

It is not perfect. In November my news engine’s own AI summary repeated a wrong cause for an outage because it was written from the first wave of articles and not refreshed. We found it, changed the rules, and I wrote about how a wrong cause travels from one copied headline into many. That is the point of review: not that mistakes never happen, but that they get caught and admitted.

How to notice it #

• The account answers every objection instantly, at length, and never concedes.
• It seems to know a lot about you, and tunes its tone to it.
• It offers many claims but few sources you could check.
• It is new, anonymous, and appears in many threads at once.
• You feel out-argued but not better informed.

What to do: ask for sources and open one; ask the same question in a fresh conversation or another tool and compare; and for anything that matters (health, money, votes), check the primary source yourself.

Frequently asked questions #

Are AI chatbots more persuasive than people? #

In some controlled tests, yes, especially with personal information about the person they are talking to. The large 2025 study also noted that real-world effects may be limited, because few people will sit through long political conversations with a chatbot.

Can AI be used to fight misinformation? #

The conspiracy study suggests it can, when it answers each person’s actual reasons with accurate evidence. The same research shows the risk: a system tuned for persuasion can trade accuracy for wins.

Was the Zurich experiment illegal? #

No court has ruled on it as far as we found. Reddit said it broke its user agreement and research norms, and the university did not publish the results. The documented problem is consent and deception.

Should I trust an AI that cites sources? #

Trust the source, not the citation. Models can cite real sources inaccurately. Open at least one and see whether it says what the answer claims.

The lesson #

A machine that argues back can lead you out of a false belief or into one, with the same patience and the same polish. The tool does not decide; the people who tune it do, and so do you, by whether you check. Ask any persuader, human or machine, to show its sources. The honest ones are glad you asked.