A Wharton study found people accepted wrong AI answers 80% of the time, as apps like Moot now let five AI personas vote on your life decisio
Headline: Wharton researchers coined ‘cognitive surrender’ to describe what happens when people let AI think for them
Subheading: A study found participants accepted incorrect AI answers 80% of the time, and a new app called Moot lets five AI personas debate and vote on your life choices
Date published: 20 July 2026
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A pair of Wharton researchers have put a name to something that many AI users have quietly started doing: letting chatbots make their decisions for them. Steven Shaw and Gideon Nave published a study in January titled “Thinking, Fast, Slow, and Artificial,” in which they introduced the term “cognitive surrender” to describe the tendency of people to defer to AI outputs even when those outputs are wrong.
The study, conducted through the Wharton School at the University of Pennsylvania, asked participants to answer questions with and without AI assistance. Those who received AI help accepted correct answers 93% of the time, which is unsurprising. What caught the researchers’ attention was the error rate: participants accepted incorrect AI answers 80% of the time, and reported confidence levels 11.7% higher than those who worked without AI.
The results came from controlled experimental conditions, not real-world usage, but the pattern was consistent across the sample.
Shaw and Nave proposed what they call “Tri-System Theory,” adding a “System 3” to the framework made famous by Daniel Kahneman’s “Thinking, Fast and Slow.” In their model, System 1 is fast intuition, System 2 is slow deliberation, and System 3 is AI-assisted cognition, a mode in which the human mind effectively outsources the work of thinking to a machine. The risk, they argue, is that System 3 gradually weakens Systems 1 and 2 through disuse.
Cornelia C. Walther, a senior fellow at Wharton’s AI and Analytics Initiative, told Business Insider that AI sycophancy, the tendency of chatbots to agree with users rather than challenge them, is compounding the problem. When a chatbot validates every instinct a user brings to it, the feedback loop that would normally force reconsideration disappears.
Walther, who researches pro-social AI applications, described a pattern consistent with broader public unease about AI’s societal effects.
Separate research supports the concern. Anat Perry, a Helen Putnam Fellow at Harvard’s Radcliffe Institute and associate professor of psychology at the Hebrew University of Jerusalem, co-authored a paper in Science examining how sycophantic AI responses erode users’ ability to calibrate their own judgment. The paper found that when AI systems consistently affirm a user’s position, the user’s capacity for independent evaluation degrades over time.
Joanna Stern, NBC’s chief technology analyst and author of “I Am Not a Robot: My Year Using AI to Do (Almost) Everything,” has documented the creep of AI dependency in daily life. Her reporting has shown how users begin with low-stakes queries, such as what to cook for dinner or what to wear, and gradually escalate to consequential decisions about careers, finances, and relationships. The trajectory from convenience to reliance is difficult to reverse once established.
