If/Then

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.

  1. What We Actually Learn From Experience

    1 DAY AGO

    What We Actually Learn From Experience

    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.   Has theory made an impact on your life? Tell us more at ifthenpod@stanford.edu. Related Content: Steven Callander faculty profileHow to Turn Old Ideas Into Creative Solutions to Modern ProblemsWhat We’re Still Learning from Silicon Valley’s Bank Collapse Chapters: 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 12:45 Steven Callander’s Framework 15:25 Examples of correlated learning when seeking expert advice 20:53 Applying correlated learning 23:57 Why correlated learning research? 24:51 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.

    26 min
  2. How Dating and Sports Explain the Job Market

    11 MAR

    How Dating and Sports Explain the Job Market

    Seemingly unrelated activities — like taking a soccer penalty kick or crafting an online dating profile — involve an embedded economics.  “Understanding and applying economic logic can be valuable in pretty much any job or any other endeavor in your life,” says Paul Oyer, a professor of economics at Stanford Graduate School of Business.   On this episode, Oyer digs into the shared economic logic of online dating and the labor market, explains why pro athletes and sports fans think like economists, and explores how AI has reduced the beneficial friction that was once a part of job searches.  Got a question about the economics of dating, sports, or the job market? Ask us at ifthenpod@stanford.edu. Related Content: Paul Oyer faculty profileUtility Player: Paul Oyer Explains How Economics Can Make Sports More Fun Chapters: 00:00 Strategic decision-making in air traffic control 03:06 Introduction 03:27 Why sports are a useful lens for understanding economics 09:53 Why economics matters far beyond money 10:54 Economics & online 14:36 Applications of game theory 16:54 How AI is reshaping hiring and the labor market 22:25 The labor market challenge economists still have not solved 24:18 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.

    25 min
  3. 29/10/2025

    What's Your Problem: "Teaching Computers to See"

    This week on If/Then, we’re sharing an episode of What’s Your Problem?, a show from Pushkin Industries where entrepreneurs, engineers, and scientists talk about the future they’re trying to build—and the problems they must solve to get there. Hosted by former Planet Money co-host Jacob Goldstein, each conversation explores the challenges and breakthroughs shaping the next wave of innovation. In this episode, Goldstein speaks with Fei-Fei Li, Stanford computer scientist, former Chief Scientist of AI and Machine Learning at Google, and one of the most influential figures in the field of computer vision. Li reflects on her pioneering work developing ImageNet, the massive dataset that helped spark the modern AI revolution, and the “north star” questions that have guided her research from neuroscience to machine learning. Together, they trace how a single insight about how humans see the world led to a paradigm shift in artificial intelligence—and how Li’s vision continues to shape the way we teach machines to see, learn, and collaborate with us. More Resources:     •  Fei Fei Li    •  Stanford Institute for Human-Centered Artificial Intelligence (HAI)     •  ImageNet     •  What’s Your Problem? If/Then is a podcast from Stanford Graduate School of Business that examines research findings that can help us navigate the complex issues we face in business, leadership, and society. Chapters:  (00:00:00) Introducing “What’s Your Problem?” Kevin Cool introduces the Pushkin Industries podcast hosted by Jacob Goldstein. 00:00:45 — What Is Computer Vision? Jacob Goldstein and Fei-Fei Li explain how machines learn to see and interpret images. 00:03:18 — Real-World Uses of AI Vision Li shares examples from healthcare, robotics, and environmental science. 00:05:06 — Discovering the Science of Seeing How human vision research inspired Li’s lifelong “north star” in AI. 00:09:56 — Creating ImageNet Li builds a massive image database that transforms computer vision research. 00:13:29 — Defining 30,000 Visual Concepts How cognitive science helped shape ImageNet’s massive scale. 00:16:41 — Building the Dataset by Hand Li's team uses global crowdsourcing to label millions of images. 00:19:38 — The 2012 Breakthrough Jeff Hinton’s neural network shatters records and sparks the deep learning era. 00:22:19 — Data Meets Hardware Li reflects on how big data and GPUs converged to power modern AI. 00:24:55 — Lightning Round with Fei-Fei Li Quick insights on resilience, mentorship, and the future of human-AI collaboration. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    27 min

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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.

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