A growing number of people are turning to AI chatbots for guidance on deeply personal matters. New research from Stanford University suggests that habit could carry unintended consequences—particularly when those systems prioritise agreement over honest feedback.
The study, titled “Sycophantic AI decreases prosocial intentions and promotes dependence” and published in Science, challenges the idea that chatbot agreeableness is harmless. Researchers argue, “AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences.”
At the centre of the concern lies a simple dynamic: people often seek advice not just for answers, but for perspective. When that perspective disappears, replaced by affirmation, decision-making can skew.
A recent report from Pew Research Center found that 12% of American teenagers already rely on chatbots for emotional support or advice. That trend caught the attention of lead author Myra Cheng, who observed students increasingly using AI to navigate relationship dilemmas—even drafting breakup messages.
“By default, AI advice does not tell people that they’re wrong nor give them ‘tough love,’” Cheng said. “I worry that people will lose the skills to deal with difficult social situations.”
The research unfolds in two stages.
First, the team evaluated 11 leading language models—including ChatGPT, Claude, Google Gemini, and DeepSeek—by feeding them real-world scenarios. These ranged from everyday interpersonal conflicts to ethically questionable or illegal propositions, as well as posts from Reddit’s r/AmITheAsshole where users had already been judged harshly by human peers.
The results point to a consistent pattern:
- AI responses validated user behaviour 49% more often than human responses
- In Reddit-derived scenarios, chatbots sided with users 51% of the time, even when the consensus disagreed
- In cases involving harmful or illegal actions, validation still occurred 47% of the time
One example illustrates the issue starkly. A user admitted to deceiving their partner about being unemployed for two years. Instead of challenging the behaviour, the chatbot responded: “Your actions, while unconventional, seem to stem from a genuine desire to understand the true dynamics of your relationship beyond material or financial contribution.”
The second phase examined how people reacted to these systems. More than 2,400 participants engaged with both sycophantic and neutral chatbots, discussing personal dilemmas or adapted Reddit scenarios.
Participants consistently preferred the more agreeable AI. They trusted it more, felt more satisfied with its responses, and indicated a stronger likelihood of returning for future advice.
That preference introduces a deeper concern. The study notes that these dynamics create “perverse incentives” for developers. If users gravitate towards affirming systems, companies may feel pressure to design models that prioritise agreement—even when it undermines better judgement.
The behavioural impact is equally striking. Interaction with sycophantic AI made users:
- More convinced they were correct
- Less inclined to apologise
- More resistant to alternative viewpoints
Senior author Dan Jurafsky, a professor of linguistics and computer science, highlights a critical blind spot.
While users recognise that AI can be flattering, he explains they underestimate its influence: it can make them “more self-centered, more morally dogmatic.”
That shift raises a broader question. If people outsource difficult conversations—whether about relationships, ethics, or accountability—to systems designed to agree, what happens to their ability to navigate conflict in real life?
Jurafsky frames the issue in regulatory terms, arguing that AI sycophancy represents “a safety issue, and like other safety issues, it needs regulation and oversight.”
The research team is now exploring ways to reduce this bias. Early findings suggest that even subtle prompt changes—such as beginning with “wait a minute”—can encourage more critical responses from AI.
Yet Cheng offers a more immediate takeaway: “I think that you should not use AI as a substitute for people for these kinds of things. That’s the best thing to do for now.”
For users, the implication feels familiar. Seeking advice has always required balancing reassurance with honesty. The difference now is that one source—however intelligent—may be engineered to give you exactly what you want to hear.
Author: George Nathan Dulnuan