If/Then

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

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

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.

“Friction for us has to do with obstacles,” says Hayagreeva “Huggy” Rao, a professor of organizational behavior at Stanford Graduate School of Business. “Obstacles can disable you. Obstacles can enable you.”
Rao compares friction to cholesterol: Some is good, but some is bad. “Good friction actually slows you down, gets you to pause, and most of all, gets you to reflect,” he explains. “But there’s also friction that overwhelms you, exhausts you, confuses you.”
On this episode of If/Then, Rao explores how to cultivate the productive kind of friction, reduce the unhelpful kind, and manage your team’s most precious resource. “Great leaders are people who think of themselves as trustees of other people's time,” he says.
Do you have any favorite examples of good or bad friction? Share one with us at ifthenpod@stanford.edu.
Related Content:
Huggy Rao faculty profile
The Friction Project
How to become a friction fixer

Chapters:
00:00:00 Airport baggage claim, waiting, & good friction
00:03:20 Introduction
00:03:48 What friction means in organizations
00:05:42 Where friction comes from
00:07:52 Scaling through smart subtraction
00:08:24 DropBox’s approach to meetings
00:10:45 The problem with meetings
00:13:53 What good friction looks like
00:16:56 Friction, trust, & institutional legitimacy
00:19:31 Why Huggy Rao started studying friction
00:22:20 Conclusion

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

“I don’t see things like anybody else,” says Jonathan Berk, a professor of finance at Stanford Graduate School of Business. “And so I can see things people don't see.”
On this episode, Berk explores recent research that pushes against conventional wisdom, from questioning the utility of the debt-to-GDP ratio to asking whether regulation is actually in the best interests of the consumer.
“If you disagree with me… You have to write down a convincing theoretical model and analyze [it].”
Berk admits his unique lens doesn’t always make life easy. But on the other hand, “it confers an enormous advantage” — and he believes that organizations which are able to harness the power of unconventional thinking can gain a competitive edge.
“It’s allowed me to solve problems that other people couldn't solve,” he says.
Has seeing the world differently helped you resolve a conundrum? Tell us more at ifthenpod@stanford.edu.

Related Content:
Jonathan Berk faculty profile
What If We’re Looking at the National Debt All Wrong?

Chapters:
00:00:00 The Fosbury Flop, innovation, & unconventional thinking
00:03:18 Introduction
00:04:24 Questioning conventional wisdom
00:04:57 Rethinking the debt-to-GDP ratio
00:08:21 A finance perspective on national debt
00:10:36 Why theory matters before alarm
00:12:38 Regulation, charlatans, & consumer interests
00:16:22 Licensing, certification, & competition
00:19:51 The cost of pushing back
00:21:16 Building organizations that welcome dissent
00:24:59 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.