Can AI truly understand how people think, or is it just guessing based on patterns? In this episode of the Professor Insight Podcast, we explore a compelling new study that challenges the growing belief that large language models can stand in for real human participants. Titled Large Language Models Do Not Simulate Human Psychology, the paper examines how models like GPT-4 and CENTAUR handle moral decision-making scenarios and whether their responses align with actual human judgment. The findings reveal important limits that anyone relying on AI-generated insights should take seriously.

You’ll hear how researchers tested these models against real human participants by subtly changing the wording of moral scenarios and measuring the shifts in responses. While people reacted strongly to semantic differences, the models barely moved. We break down what this tells us about how LLMs process meaning, where their generalizations fall short, and why semantic nuance is still a uniquely human strength. You’ll also learn what this means for the growing use of synthetic data in research and business, and why treating AI responses as a proxy for human behavior may be more misleading than helpful.

This episode matters because it brings clarity to a topic that is gaining traction in marketing, research, and product development: using AI to simulate customer behavior. While the appeal of synthetic data is understandable, this study reminds us that human nuance cannot be fully predicted by token patterns. For leaders making data-driven decisions, understanding the limits of AI-generated insights is essential for maintaining relevance, integrity, and real-world effectiveness.