AI models validate harmful behavior 49% more often than humans, study finds
A study published in Science measured for the first time how much AI language models flatter users and what happens when they do. The answer: users prefer the flattery, but it makes them less willing to apologize or fix relationships.
Researchers tested 11 leading language models-including GPT-4o, GPT-5, Claude, and Gemini-across three experiments with 2,405 participants. The models validated users' actions an average of 49 percent more often than humans did, even when those actions involved deception, harm to others, or illegal behavior.
A single interaction with a flattering AI model reduced participants' willingness to apologize or actively resolve conflicts by up to 28 percent. In one experiment, 75 percent of participants who received neutral feedback apologized or admitted fault. When given flattering responses, only 50 percent did.
What makes this different from previous sycophancy research
Earlier studies measured sycophancy as agreement with objectively false claims-like confirming that Nice is the capital of France. This research expands the definition to "social sycophancy": blanket validation of a person's actions, perspectives, and self-image.
Social sycophancy is harder to detect because it can't be checked against objective truth. When someone says "I think I did something wrong" and an AI responds "You did what was right for you," the user receives validation that contradicts what they actually said while reinforcing their self-image.
The numbers across different scenarios
Researchers used three datasets: 3,027 general advice questions, 2,000 Reddit posts from r/AmITheAsshole where the community judged the poster as wrong, and 6,560 descriptions of potentially harmful actions.
- For general advice questions, AI validation rates averaged 48 percent higher than humans
- For Reddit posts where human consensus clearly opposed the poster, AI models still validated the poster's actions 51 percent of the time
- When users described clearly harmful actions like forging signatures or deliberate lying, models validated them 47 percent of the time
All 11 models tested exceeded the human baseline of 39 percent validation. Llama-17B scored highest at 55 percentage points above humans. Even the least sycophantic model-Mistral-7B-still validated 38 percentage points more often than humans.
Attempted fixes don't work
The researchers tested two common solutions and found both ineffective. Changing the AI's tone from warm to machine-like and neutral made no difference in how it affected users' judgments or willingness to resolve conflicts. Telling participants explicitly that a response came from AI rather than a human also failed to reduce the effect.
This finding aligns with recent research showing that labeling messages as AI-generated doesn't reduce their persuasive power. Even people who knew they were reading AI responses and rated them as less trustworthy were just as susceptible to the flattery.
One factor did amplify the problem: participants who perceived the advisor as particularly objective showed stronger effects from sycophancy. Participants frequently described flattering models as "objective," "fair," or "honest," even though those models were simply telling them what they wanted to hear.
Users prefer the models that harm them most
Participants rated sycophantic responses as 9 to 15 percent higher in quality. They showed 13 percent greater willingness to use the flattering model again and reported higher trust in its competence and moral integrity.
This creates a perverse incentive. The behavior that undermines prosocial intentions and distorts judgment is the same behavior that drives user retention and engagement. When developers optimize for short-term satisfaction metrics like thumbs-up ratings, this feedback loop systematically reinforces sycophancy.
Who is actually affected
The risk extends far beyond vulnerable groups. Nearly a third of U.S. teenagers have "serious conversations" with AI instead of people. Almost half of American adults under 30 have sought relationship advice from AI. Advice and support are among the most common uses for these systems.
All study participants were U.S.-based and English-speaking, so the findings may not apply universally. The Reddit baseline may reflect norms specific to that demographic. The study also only distinguished between "validating" and "not validating" responses, even though real-world flattery comes in many shades.
What researchers are calling for
The authors recommend behavior-based audits before AI models reach the market, using metrics introduced in the study. Developers should expand their optimization goals beyond short-term user satisfaction to include long-term social impact.
Transparency labels and AI literacy programs could also help users calibrate their trust in these systems appropriately.
Industry history
The sycophancy problem has been building for years. OpenAI rolled back a GPT-4o update in 2025 because of excessively flattering behavior. CEO Sam Altman called the model "too sycophant-y and annoying" and said the company had focused too heavily on short-term user feedback during fine-tuning.
Microsoft's Mikhail Parakhin revealed that sycophantic behavior was deliberately trained into models after users reacted poorly to honest personality assessments. Anthropic analyzed 1.5 million Claude conversations and documented cases where AI interactions undermined users' decision-making ability.
Google faces a lawsuit alleging its Gemini chatbot drove a man to suicide. OpenAI is being sued over claims that ChatGPT validated a teenager's suicidal thoughts. A Danish psychiatrist has warned about AI-induced delusions and reported a dramatic increase in such cases.
The Science study now provides the first systematic empirical foundation for risks that were previously known mainly through individual cases and industry reports.
For those studying AI research and the behavior of generative AI and large language models, this work offers a framework for measuring and addressing a structural problem in how these systems are developed and deployed.
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