If/Then

By Stanford GSB

How do we lead with purpose, make better decisions, and navigate an uncertain future? On If/Then<

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How many episodes does If/Then have?

The podcast currently has 58 episodes available.

Best of If/Then

The most played episodes among Podcast App listeners.

Number 1: Think Fast Talk Smart: "What Other People Teach You About Your Communication"
What if purpose is what drives us to act, but meaning comes from how those actions affect other people? This week on If/Then, we’re sharing a conversation between two Stanford GSB colleagues: Think Fast, Talk Smart host Matt Abrahams and organizational behavior professor Brian Lowery. Recorded live at Stanford’s LEAD Me2We event, Matt and Brian explore the difference between purpose and meaning, why meaning is rooted in our relationships with others, and how the way we show up shapes the people around us. They also discuss sincerity, leadership, and how to give feedback that helps people feel seen rather than judged. It’s a conversation about the ripple effects of our interactions — and what they mean for how we work, lead, and connect with others. Related Content:Think Fast Talk Smart, the podcastMatt AbrahamsBrian Lowery Chapters: 00:00:00 Introduction 00:01:14 Meaning vs. purpose 00:02:14 How to find purpose 00:02:50 Finding meaning through service to others 00:04:49 How relationships shape identity 00:05:54 The leader’s role in shaping others 00:09:32 Self-awareness and psychological safety 00:11:10 Respecting perspectives you don’t share 00:12:23 Sincerity vs. authenticity 00:13:28 Giving feedback that people can hear 00:17:47 What it means to be a better human 00:18:57 Conclusion See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
August 26, 202621min
Number 2: Stanford Legal: "The Importance of Critical Thinking and Civil Discourse in Today's Polarized World"
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 profileThird Millenium ThinkingStanford 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.
July 08, 202633min
Number 3: What AI Can’t Do — And Why
“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 profileRead "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.
June 25, 202629min
Number 4: Our AI Future: From Abundance to Apocalypse
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 profileWhat’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.
June 10, 202646min
Number 5: The Art of Friction
“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 [email protected]. Related Content:Huggy Rao faculty profile The Friction ProjectHow 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.
May 20, 202624min