How do we lead with purpose, make better decisions, and navigate an uncertain future? On If/Then, Stanford GSB faculty break down cutting-edge research on leadership, strategy, and more, exploring enduring questions and the forces reshaping business and society today, from AI to geopolitics. Hosted by senior editor Kevin Cool.
Site
RSS
Apple
Data updated on 24/09/2026
Recent rankings
Latest chart positions across Apple Podcasts and Spotify rankings.
Shared links between episodes and podcasts
Links found in episode descriptions and other podcasts that share them.
An AI@GSB Special: Demis Hassabis Thinks We’re in the ‘Foothills of the Singularity’
Season 3 · Episode 57
Wednesday, September 23, 2026 • Duration 54:49
When Demis Hassabis pitched DeepMind to a few venture capitalists back in 2010, the business plan was almost comically audacious. “Step one: Solve intelligence. Step two: Use it to solve everything else,” he recalls in a conversation at Stanford Graduate School of Business with Stanford University President Jonathan Levin. “And people were quite confused. But we really meant it.”
Sixteen years later, the “broad arcs” of that plan have gone “unbelievably well,” says Hassabis, a chess prodigy turned video game developer turned neuroscientist turned Nobel Prize-winning AI pioneer. Today he’s on a mission to create “the ultimate tool for science,” building on his decision to give away AlphaFold, the groundbreaking AI system that predicts the structures of proteins. The future, Hassabis says, is just around the corner: “Ten years from now, I think we’ll realize that we were standing in the foothills of the singularity now.”
Chapters:
00:00:00 Introduction
00:03:31 A career built around one big question
00:06:19 Building DeepMind and pursuing AGI
00:10:09 The breakthroughs that made AI feel possible
00:14:48 From AlphaGo to scientific discovery
00:17:15 Why AlphaFold was given away for free
00:24:00 The “foothills of the singularity”
00:26:41 Public concern, disruption, & AI’s promise
00:29:59 How AI could reshape work and entrepreneurship
00:34:12 Competition, safety, & regulating frontier AI
00:41:19 Expanding AI’s benefits globally
00:45:28 Preparing society for AGI
00:48:21 What should AI leave untouched?
00:51:01 Advice for the first AI-native generation
Think Fast Talk Smart: "What Other People Teach You About Your Communication"
Season 3 · Episode 56
Wednesday, August 26, 2026 • Duration 20:19
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.
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
Stanford Legal: "The Importance of Critical Thinking and Civil Discourse in Today's Polarized World"
Season 3 · Episode 55
Wednesday, July 8, 2026 • Duration 32:34
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.
“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.
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.”
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
The Art of Friction
Season 3 · Episode 52
Wednesday, May 20, 2026 • Duration 23:52
“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.
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
Unconventional Wisdom
Season 3 · Episode 51
Wednesday, May 6, 2026 • Duration 26:11
“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.
“When people come to view attitudes and opinions towards, say, political policies or issues as relevant to their identities, they become more extreme in their attitudes,” says Christian Wheeler, the StrataCom Professor of Management and Professor of Marketing at Stanford Graduate School of Business. “I become more positive or negative towards an issue the moment it becomes relevant to who I view myself as being.”
Wheeler’s research offers insight into our increasingly polarized politics. However, his work has also yielded ideas for bridging divisions — beginning with how we listen to each other and how we see the people we disagree with.
The moment we see someone as an individual rather than a category, we become more likely to find common ground. “Instead of viewing you as a Democrat or a Republican, I can view you as an individual,” Wheeler recommends. “Anything that humanizes you and moves you away from this simple category will help me to view you as an individual and less as just an interchangeable member of a category.”
“Masculinity is my new frontier,” says Ashley Martin, an associate professor of organizational behavior at Stanford Graduate School of Business. Martin, whose work examines why gender plays such a central role in how we perceive and make sense of others, has been looking at how traits associated with masculinity are simultaneously organizationally rewarded even as they’re personally harmful to men.
“We spend a lot of time talking about gender inequality through the lens of women’s disadvantage,” she says. “I think that many of the problems that we’re seeing today… are actually bound up in masculinity.”
What impact do you think masculinity and femininity have on our work and our world? Tell us more at ifthenpod@stanford.edu.
00:00 How movies shape our ideas about masculinity
04:02 Introduction
05:15 How Ashley Martin got into studying gender
05:58 When gender is removed from hiring
07:10 The “pet rock” study
10:35 The universal use of gender
13:02 Gendering objects
What We Actually Learn From Experience
Season 3 · Episode 48
Wednesday, March 25, 2026 • Duration 25:56
Steven Callander has spent years building a mathematical framework to answer the question of how people learn from experience. “Here in Silicon Valley, the expression that you learn from failure is very widespread and very intuitive. But the question is… what do you learn? How do you optimally learn from that experience?”
In this episode, Callander, the Herbert Hoover Professor of Public and Private Management and Professor of Political Economy at Stanford Graduate School of Business, explains the hidden, deceptively simple logic of correlated learning — and it may change how you think about finding the right job, the right market, or the right strategy.
“It fascinates me and I can't stop thinking about it,” he says.
00:00 Ann Miura-Ko on learning and the search for patterns in Venture capital
02:51 Introduction
05:23 What is correlated learning?
06:40 Where does this research apply in the real world?
09:28 Brownian Motion
Related Shows Based on Content Similarities
Discover shows related to If/Then, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
