If/Then

Google DeepMind's CEO discusses solving intelligence, giving away AlphaFold, and why he believes AGI is just around the corner.

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 LoweryChapters:00:00:00 Introduction00:01:14 Meaning vs. purpose00:02:14 How to find purpose00:02:50 Finding meaning through service to others00:04:49 How relationships shape identity00:05:54 The leader’s role in shaping others00:09:32 Self-awareness and psychological safety00:11:10 Respecting perspectives you don’t share00:12:23 Sincerity vs. authenticity00:13:28 Giving feedback that people can hear00:17:47 What it means to be a better human00:18:57 Conclusion See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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 PodcastChapters:00:00:00 Introduction00:01:23 The course, the book, & what motivated it00:04:06 Habits of mind for better decision-making00:06:20 Probabilistic thinking and intellectual humility00:09:57 An “opinion diet”00:12:16 Reasonable doubt, community, & collective judgment00:14:13 Scientific optimism and the problem of cynicism00:17:31 Why trust in science has eroded00:20:10 Law, science, & the value of procedure00:22:50 Steel-manning the other side00:24:58 Public policy as provisional problem-solving00:30:07 Deliberative democracy and informed public debate00:32:03 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 [email protected] Content:Douglas Guilbeault faculty profileRead "A Simple Threshold Captures the Social Learning of Conventions" hereChapters:00:00:00 Introduction00:01:40 Why human learning matters for AI00:05:03 Satisficing and the limits of optimization00:06:41 Why LLMs learn differently from humans00:09:58 The stakes of AI hype00:13:11 “Humanity has had a good run”00:15:19 Intuition, insight, & conceptual leaps00:17:38 Beyond statistics: metaphor, vibes, & reasoning00:19:39 A simple rule for social learning00:21:18 Is there a ceiling for AI?00:23:00 Randomness, disorder, & the path to insight00:25:00 What an optimization mindset leaves out00:27:54 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.

Abundance, anyone? While the risks posed by artificial intelligence are extraordinary, so is the potential, says professor of economics Chad Jones.