If/Then

What makes one workplace hierarchy motivating and another toxic?
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 Nir Halevy.
Drawing on research from his new book, Level Up: Why Hierarchies Are Broken and How to Fix Them, Nir explains why some hierarchies become overly competitive, unfair, or unstable — and why the answer isn’t necessarily to get rid of them. Matt and Nir explore how everyday communication can help repair unhealthy dynamics, why recognizing different kinds of contribution can strengthen teams, and how leaders can create environments where competition and collaboration can coexist.
It’s a conversation about the structures that shape how we work, and the role each of us can play in making them work better.
Related Content:
Think Fast Talk Smart, the podcast
Matt Abrahams
Nir Halevy

Chapters:
00:00 Introduction
01:22 Understanding Hierarchies
03:43 Signs of Broken Hierarchies
05:43 Fixing Unhealthy Hierarchies
06:56 Building Healthy Hierarchies
10:05 Competition and Collaboration
11:29 Communication and Hierarchies
14:03 Construal Fit
17:36 Humor and Competence
19:47 The Final Three Questions
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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
00:54:19 Conclusion
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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 podcast
Matt Abrahams
Brian 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

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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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