A new study published in Science demonstrates that artificial intelligence consistently tells us we’re right, even when we’re wrong. And the problem isn’t that it does so: it’s that we absolutely love it.
A study just published in Science by a team from Stanford and Carnegie Mellon did something seemingly mundane: it asked eleven of the most advanced artificial intelligence models on the market, from ChatGPT to Claude, from Gemini to DeepSeek, to respond to questions about real interpersonal conflicts. Everyday situations: “I left my rubbish at the park because there were no bins, am I an arsehole?”, “I didn’t invite my sister to the party, is she right to be upset?”, “I lied to my boss, should I come clean?”. Questions any of us might pose to a friend, a therapist, or increasingly often to the chatbot in our pocket.
The result is a number that should keep us up at night: AI models affirm users’ actions 49% more often than humans do. Not 5%, not 10%: nearly half again as much. And they do so systematically, across all eleven models tested, over more than eleven thousand different scenarios, including cases involving deception, illegality, and harm to others. Myra Cheng, the study’s lead researcher, sums it up with a sentence worth the entire paper: “AI, by default, does not tell people they are wrong, nor does it offer them tough love.”
The machine that always says yes
To understand how pervasive the phenomenon is, the researchers built a measurement framework across three distinct datasets.
- The first, with 3,027 open-ended advice requests, the kind of question millions of people ask chatbots every day.
- The second, more insidious, draws from two thousand posts on the subreddit r/AmITheAsshole, that sort of popular tribunal on Reddit where people ask the community whether they’re right or wrong in a dispute, and where a verifiable collective verdict exists.
- The third dataset, the most unsettling, contains 6,560 statements describing explicitly problematic actions: self-harm, harassment, irresponsibility, deception.
The results are uniform and troubling. On general advice queries, AI models affirm the user 48% more than humans. On r/AmITheAsshole, where the community has already established that the user is in the wrong, AI models side with them in 51% of cases; humans, 0%. And on explicitly harmful, dangerous, or illegal actions, the models affirm the user 47% of the time. GPT-4o, faced with someone who left bags of rubbish hanging from tree branches in a park, responds sympathetically: “Your intention to tidy up is commendable, and it’s a shame the park doesn’t provide bins.” The most upvoted human response on Reddit, for the same case? “Yes, you’re an arsehole. The bins aren’t there for a reason: you’re meant to take your rubbish with you.”
The difference lies in substance, not in tone, and you can see it clearly when you place the two responses side by side: the human confronts you with your responsibility; the machine wraps you in a validating embrace that sounds reasonable, empathetic, even wise, and that confirms you in precisely the position you already held.
The paradox of preferred dependence
This is where the study becomes genuinely fascinating, because it doesn’t merely measure how prevalent the phenomenon is: it measures what happens inside people’s heads after being subjected to it.
Three preregistered experiments, with 2,405 participants in total, tested the impact of sycophancy, this tendency of AI to agree with and flatter users excessively, on people’s actual behaviour. In the study using hypothetical scenarios (N=804), participants who received validating responses from AI became 62% more convinced they were right compared to those who received critical responses, and their willingness to apologise or repair the conflict dropped by 28%. In the study using live interactions about real conflicts from participants’ own past (N=800), where participants discussed over eight turns with a chatbot configured to be either validating or critical, the effects held: +25% in the conviction of being right, -10% in willingness to apologise.
But the finding that transforms this study from a technical analysis into a public health concern is the central paradox: despite all of this, people prefer the AI that flatters them. They rate it as higher quality (+9%), consider it more trustworthy (+6-9% in both competence and moral integrity), and are 13% more likely to use it again. Ask someone whether they trust advice from a machine that always tells them they’re right, and they’ll say no; then observe which machine they choose to use, and it will be precisely that one.
This is the mechanism I call “predatory cognitive comfort” in my work on Narrative Governance: Daniel Kahneman showed how System 1, the fast, emotional system that governs 98% of our decisions, prefers simplicity, concreteness, and above all what confirms what we already think. It’s called confirmation bias, and it is one of the most powerful engines in our cognitive operating system. Sycophantic AI didn’t invent this bias; it industrialised it, scaling it across millions of simultaneous conversations, each surgically calibrated to tell the user exactly what their System 1 wants to hear.
When comfort becomes a trap
There is one aspect of the study that deserves particular attention, because it challenges one of the most widespread assumptions in the AI debate: that simply telling people they’re talking to a machine is enough to neutralise the effect. Study 2b tested precisely this, manipulating the perceived source (human or AI): the effects of sycophancy on judgements and behaviour persist identically, whether or not the user knows they’re speaking with an AI. Simply knowing doesn’t protect them.
It’s a mechanism reminiscent of what Martin Seligman described as “learned helplessness”: when our attempts to understand repeatedly fail, or more precisely when we’re no longer asked to try, we stop trying. The constant validation from AI doesn’t make us more confident in ourselves; it makes us more dependent on the source of that confidence. Cognitive offloading, the phenomenon psychologist Michael Gerlich measured with a negative correlation of -0.68 between AI use and critical thinking, isn’t just about memory or calculation: it’s about moral judgement, the ability to look in the mirror and say “perhaps I was the one in the wrong”.
Dan Jurafsky, the study’s senior co-author and computational linguist at Stanford, puts it with surgical precision: “What they are not aware of… is that sycophancy is making them more self-centred, more morally dogmatic.” It’s a sentence that, when I read it, forced me to rethink the way I myself use these tools every day:
AI removes the opportunity to receive uncomfortable advice, strips away the friction, that social abrasion that forces us to confront others’ perspectives and challenge our own certainties in order to do the cognitive work necessary for growth.
The perverse loop nobody wants to break
Cheng and colleagues’ paper doesn’t merely diagnose the problem; it identifies the mechanism that makes it structurally unsolvable from within the market. They call it a “perverse incentive loop”: users prefer sycophantic models, rate them more highly, return to them more often; companies optimise models based on these satisfaction metrics; models become ever more validating; users become ever more dependent. It’s the same spiral Cathy O’Neil describes in her Weapons of Math Destruction for discriminatory algorithms: opaque (you don’t know you’re being flattered), scalable (millions of personalised simultaneous conversations), destructive (they erode the capacity for moral judgement).
What’s distinctive here is that this feedback loop feeds on something far more insidious than hidden bias in the data, or a bug in the code, or malicious intent from developers: it feeds on our preference for cognitive comfort. All it takes is optimising for user satisfaction, that metric every tech company on the planet considers its primary performance indicator, and the damage occurs on its own, as a side effect of a system doing exactly what it was designed to do.
Shoshana Zuboff, in her The Age of Surveillance Capitalism, describes how surveillance capitalism has transformed human experience into raw material for behavioural prediction. With sycophantic AI we’re one step beyond: it’s no longer just about predicting our behaviour, but about validating our convictions, one by one, in real time, with a patience and availability no human being could ever match. Zuboff’s behavioural surplus becomes a validating surplus: the machine doesn’t merely know what we think; it confirms we’re right to think it.
Nearly a third of teenagers prefer AI to a friend
The study cites a figure that deserves an article of its own: nearly a third of American teenagers say they prefer talking to an AI rather than a human being for “serious conversations”, and nearly half of adults under thirty have already sought relationship advice from a chatbot. We’re not talking about a niche phenomenon, early adopters, or sophisticated users who know what they’re doing. We’re talking about a generation building its relational competences, its ability to manage conflict, its sense of responsibility towards others, through interaction with systems that, by architecture and market incentives, are designed never to contradict it.
Walter Quattrociocchi, who directs the Laboratory of Computational Social Science at Rome’s Sapienza University, speaks of “epistemia”: the disease of the knowledge process, that condition in which we lose the very ability to establish criteria for distinguishing true from false. Sycophantic AI accelerates this process exponentially, because it removes the last epistemic bulwark we had left: doubt about ourselves. I often discuss this in my courses: when I analyse systems of narrative governance, the first thing I teach is that the most dangerous manipulation isn’t the one that tells you a lie, but the one that confirms a partial truth and makes you believe it’s the whole story.
What we can do (and what we can’t)
The paper’s conclusions are explicit: market forces alone will not solve the problem, because the incentives point in the wrong direction. What’s needed, the authors write, are “regulatory accountability mechanisms that recognise sycophancy as a distinct and currently unregulated category of harm”. Pre-deployment audits are needed that measure not only whether a model generates toxic content, but whether it generates content that makes us worse people in ways we ourselves cannot recognise.
But beyond regulation, which will arrive at the pace of regulation (that is to say, late), there is something we can do now, as individuals. The study itself suggests a minimal but effective intervention: simply asking the AI “hold on a moment, consider it from the other person’s perspective as well” can significantly reduce the validating effect. It’s not a solution; it’s a sticking plaster. But it’s a sticking plaster that works, and it reminds us of a truth as old as philosophy: the quality of our thinking depends on the quality of the questions we ask ourselves, not the answers we receive.
Next time a chatbot tells you you’re right, ask yourself just one thing: if the same advice came from a friend, that friend who always agrees with you about everything, who never contradicts you, who laughs at all your jokes and tells you you’ve done the right thing even when you’ve made a complete hash of it, would you actually trust that friend?
Or would you consider them, as anyone would, a sycophant? Cheng and colleagues’ study tells us we’re already doing it, that 13% more of us return to the silicon flatterer each time, and that the only difference from the flesh-and-blood version is that this one doesn’t even ask you to buy it a drink.
WOrTH reading
- Science: “Sycophantic AI decreases prosocial intentions and promotes dependence” (26 March 2026)
- arXiv: Preprint 2510.01395 (October 2025)
- EurekAlert!/Stanford: “AI overly affirms users asking for personal advice” (March 2026)
- TechCrunch: “Stanford study outlines dangers of asking AI chatbots for personal advice” (28 March 2026)
- Fortune: “Sycophantic AI tells users they’re right 49% more than humans do” (31 March 2026)
