How is AI reshaping human reasoning? What is cognitive surrender, and how do we avoid its negative impact? What is system three thinking, and how can we get the most out of it? Artificial intelligence researchers Gideon Nave and Steven D. Shaw have some answers, some questions, and some suggestions.
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Gideon Nave is a professor of marketing at The Wharton School, University of Pennsylvania, where he studies how people think, decide, and behave — and how technology is changing all of that. He earned his PhD from Caltech, where he focused on decision neuroscience, and before that he trained as an electrical engineer. That mix of backgrounds shapes how he approaches research: He likes to build on rigorous methods from multiple fields to ask questions that actually matter.
A big part of his work right now is about AI and its effects on human psychology. How does relying on AI tools change the way we think? Does it make us more creative, or does it quietly narrow our thinking? When AI makes better predictions about us than we make about ourselves, what does that mean for personal autonomy and privacy? These are questions he finds genuinely important, and increasingly urgent.
He also studies the biological side of behavior, using tools like brain imaging, genetics, and hormonal measures to understand where preferences and decisions come from. And he cares a lot about whether findings in psychology and marketing actually replicate, so some of his work focuses on improving how science is done.
Steven D. Shaw is a Postdoctoral Researcher in the Marketing Department at The Wharton School of the University of Pennsylvania.
His research integrates emerging technologies, novel data sources, and interdisciplinary methods to advance marketing theory and practice. He examines how innovations—from generative AI to genomic and epigenetic data—reshape consumer psychology, segmentation, and strategy.
His job market paper introduces Tri-System Theory of Cognition, and demonstrates the rise of “cognitive surrender“: deference to AI without verification. Other work introduces biological age (BioAge) as a next-generation demographic variable, using epigenetic clocks to reveal insights into the aging consumer. Another project explores how large language models (LLMs) can enhance the generalizability of experiments through the automated creation of stimulus universes. He also studies consumer neuroscience, including how neural activity can forecast market trends.
He holds a Ph.D. in Marketing and an M.A. in Statistics from the University of Michigan, Ann Arbor and dual B.Sc. degrees in Genetics & Psychology and Animal Behavior from the University of Western Ontario, Canada.
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Thinking: Fast, Slow, and Artificial
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