Episode
What AI Can’t Do — And Why
- Podcast
- If/Then
- Published
- Jun 25, 2026
- Duration seconds
- 1730
- Processing state
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Summary
“Humans manage to do so much with surprisingly little,” says Douglas Guilbeault , an assistant professor of organizational behavior at Stanford Graduate School of Business. “Whereas AI, by comparison, is doing relatively little, but with so much power, so much compute, so many resources, and by comparison, relatively fewer constraints.” On a bonus episode of the If/Then podcast, Guilbeault describes the implications of his recent work. Although he readily acknowledges that AI is “increasingly able to do quite a lot,” Guilbeault and his colleagues believe they have identified a key principle that distinguishes human intelligence from machine intelligence — and one which illuminates the limitations of machine thinking. Although some researchers and AI boosters believe both humans and AI learn via optimization, Guilbeault and his colleagues have shown that another process more accurately captures how people distill the seemingly infinite complexity of the world and act based on limited information. “You encounter a lot of noise, a lot of chaos, a lot of randomness,” Guilbeault says. “We somehow figure out how to make meaning and establish strong understandings from within that.” What limitations have you encountered in your work with AI? Share your story with us at [email protected]. Related Content: Douglas Guilbeault faculty profile Read "A Simple Threshold Captures the Social Learning of Conventions" here Chapters: 00:00:00 Introduction 00:01:40 Why human learning matters for AI 00:05:03 Satisficing and the limits of optimization 00:06:41 Why LLMs learn differently from humans 00:09:58 The stakes of AI hype 00:13:11 “Humanity has had a good run” 00:15:19 Intuition, insight, & conceptual leaps 00:17:38 Beyond statistics: metaphor, vibes, & reaso…