How do you engage effectively across deep disagreement without shutting down the conversation?

This week on If/Then, we’re sharing an episode from our colleagues at Stanford Legal, the podcast from Stanford Law School that looks at the cases, questions, and conflicts shaping public life.

In a world where confidence is rewarded and humility can feel like a liability, Stanford Law professor Robert MacCoun argues for something radical: fewer unwavering opinions, more critical reflection, and a better way to disagree. On Stanford Legal, MacCoun joins co-hosts Pam Karlan and Diego Zambrano for a conversation about how “habits of mind” borrowed from science can help citizens, lawyers, and policymakers think more clearly, listen more carefully, and build better public debate around difficult questions that don’t have easy answers.

Trained as a social psychologist, MacCoun's work sits at the intersection of law, science, and public policy, with decades of research on decision-making, bias, and the social dynamics that shape how evidence is interpreted. In the episode, he draws on his most recent book, Third Millennium Thinking: Creating Sense in a World of Nonsense, co-authored with Nobel Prize–winning physicist Saul Perlmutter and philosopher John Campbell, to explain why probabilistic thinking, intellectual humility, and what he calls an “opinion diet” are essential tools for modern civic life.

Related Content:

• Robert MacCoun faculty profile
• Third Millenium Thinking
• Stanford Legal Podcast

Chapters:

00:00:00 Introduction

00:01:23 The course, the book, & what motivated it

00:04:06 Habits of mind for better decision-making

00:06:20 Probabilistic thinking and intellectual humility

00:09:57 An “opinion diet”

00:12:16 Reasonable doubt, community, & collective judgment

00:14:13 Scientific optimism and the problem of cynicism

00:17:31 Why trust in science has eroded

00:20:10 Law, science, & the value of procedure

00:22:50 Steel-manning the other side

00:24:58 Public policy as provisional problem-solving

00:30:07 Deliberative democracy and informed public debate

00:32:03 Conclusion

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“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 ifthenpod@stanford.edu.

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, & reasoning

00:19:39 A simple rule for social learning

00:21:18 Is there a ceiling for AI?

00:23:00 Randomness, disorder, & the path to insight

00:25:00 What an optimization mindset leaves out

00:27:54 Conclusion

If/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership.

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Chad Jones, a professor of economics at Stanford Graduate School of Business, recently published a paper, “AI and Our Economic Future.” Using more than 100 years of economic data, he modelled several potential AI-infused economic futures we may experience. These include the good (abundance, we never work again), the not-so-bad (business more or less as usual), and the ugly (a superintelligence that turns on us, among other catastrophic options). Cheery stuff, Jones acknowledges, but essential to face.

“I think the ability for an AI to do everything on a computer that the best software engineer can do, that seems like it’s either here now or will be here within five years easily,” Jones says. “Hacking the electric grid, hacking the financial system, these kinds of scenarios are things that we definitely have to worry about. The good news is, I think if we get through that, the ability of AI to transform the economy for good, it is really there and present. And, that would be a very great and bright future.”

Related Content:

• Chad Jones faculty profile
• What’s the Price Tag for Preventing an AI Apocalypse?
• At What Point Do We Decide AI’s Risks Outweigh Its Promise?

Chapters:

00:00:00 Introduction

00:01:32 The difference between now & previous periods of innovation

00:02:29 Two scenarios for AI-driven growth

00:06:18 The case for business-as-usual

00:11:06 Weak links and the limits of automation

00:17:53 What the models are showing about growth

00:19:58 The economics of abundance

00:25:29 The weak-link model’s timing & possible adaptations

00:27:51 Who gains in an AI economy?

00:29:55 Catastrophic risk and the downside of acceleration

00:34:31 The downsides of the weak link model

00:36:38 Meaning, identity, and human value

00:39:55 Leisure in a post-work world

00:41:43 What the next generation may inherit

00:44:07 Conclusion

If/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.