A fundamental question about AI is when it contributes to human development and when it substitutes for it. How its use affects labour markets, productivity, and more generally, human capacity is one of the questions of our time. In this episode I am joined by Youngjin Yoo, Professor of Information Systems and Innovation at the London School of Economics and Political Science.

We discuss what makes digital technology different from what came before. That includes Youngjin’s idea of computational value, where AI’s value is assembled at the moment of use rather than designed in advance. It also includes endogenous utility, where you and the product improve together, and how that differs from addiction. We ask whether AI will create genuinely new demand for human work, or whether that demand will simply be met by more AI, and whether handing it more of our thinking means handing over control. Running through it all is one question: if AI gives you a competitive advantage, is choosing not to use it a real choice?

As a leading voice on digital innovation, Youngjin has published over 170 peer-reviewed articles, cited more than 33,000 times, in MIS Quarterly, Information Systems Research, Organization Science, the Academy of Management Journal, Harvard Business Review, and Communications of the ACM. His research has attracted funding from the US National Science Foundation, NASA, the Knight Foundation, the Walmart Foundation, and Samsung Electronics.

Citations

Nicholson, W. (Writer), & Kavanaugh, R. (Director). (2026). Shadowlands [Live performance]. Aldwych Theatre, London, UK.

Nietzsche, F. (1977). A Nietzsche Reader (R. J. Hollingdale, Trans.). Penguin Books.

Hosted on Acast. See acast.com/privacy for more information.

What does it mean to teach ‘leadership’?

For most of human history, education and leadership formation were essentially the same thing. Only future leaders were formally educated. To educate someone was to form them - morally, civically, intellectually - for the responsibility of leading others. The classical traditions, whether Aristotelian, Stoic, or Confucian, did not aim to develop a leadership skill set, remaining agnostic to its ends. They aimed at wisdom, attempting to form a person who would be able to be a ‘good’ leader.

Then education became universal and at roughly the same moment, the shared moral and philosophical frameworks that had given classical formation its content were fragmenting under the weight of Enlightenment pluralism.

In response, modern leadership development largely abandoned the project of forming the person in favour of helping individuals discover themselves and acquire tools to pursue whatever ends they chose. We exchanged formation for self-discovery, from becoming-before-doing, to becoming-through-doing. We have gained a great deal in that exchange — leadership development became accessible, personal, directly applicable to real dilemmas, and less paternalistic. But did the trade come with costs we don't always name?

One cost may be that we have shifted the psychological burden onto the individual leader and assume they arrive in our classrooms with their own developed moral and ethical codes. This episode explores that tension. We discuss how we got here and what the most sophisticated modern responses to the problem look like – in the case of HEC Paris’s Leadership Signature. We close with a question I find myself increasingly focused on: whether modern leadership development, in its emphasis on personalised insight and emotional resonance, may be optimising for the experience of insight rather than the slower, harder, more humbling work of insight formation — and whether AI is making that question newly urgent rather than answering it.

I cannot think of anyone better to talk about all this than Emmanuel Coblence. Emmanuel is Associate Professor at HEC Paris and Academic Director of the school's Leadership programs in Executive Education — an architect, in other words, of one of the most thoughtful modern answers to the question we are asking. His "Leadership Signature" approach refuses the prescriptive recipe: rather than telling executives what kind of leader to be, it gives them a method for discovering and refining a leadership style that is genuinely their own, scaffolded by mentors, mirrors, and sponsors rather than by a fixed doctrine. I have had the pleasure of seeing him in the classroom – a masterclass in the art and science of executive education.

Apart from his academic life, he has been a municipal councillor in Paris since 2014, with a focus on educational policy. This gives him a practitioner's eye on the civic dimensions of formation that pure academics often miss. And he is currently building an AI Leadership Mentor at HEC — which makes him one of the very few people qualified to think honestly about both the promise and the limits of what AI can give us in this domain.

It is always a pleasure to speak to Emmanuel. He is generous, rigorous and is willing to engage with genuinely uncomfortable questions - including questions at the heart of both of our work. I learned a great deal. I hope you do too.

Citations

· Bojovic, N., Sabatier, V., & Coblence, E. (2019). Becoming through doing: How experimental spaces enable organizational identity work. Strategic Organization, 18(1), 147–167.

· Rosa, H. (2016). Resonance: A sociology of our relationship to the world. Polity Press.

· Rosa, H. (2013). Social acceleration: A new theory of modernity. Columbia University Press.

· Sardais, C., Lortie, J., & Coblence, E. (2019). Inside the “Panacousticon”: How orchestra conductors play with discipline to produce art. International Journal of Arts Management, 22.

Hosted on Acast. See acast.com/privacy for more information.

AI is becoming ubiquitous in our lives. It shapes how we work, play, interact, create, and even manage our health—and this is only the beginning. To understand where we are and where we might go, we first need to understand how we got here. By tracing the evolving nature of machine intelligence, we can appreciate how today’s AI differs from its past and how it is likely to evolve. With that in mind, we can begin to ask the big questions: When should we trust AI over human judgment? How should we govern its development? How will it change what it means to be human? And what roles will humans play in the future of work?

To help us through this journey, I’m delighted to welcome back to TRIUM Connects Professor Vasant Dhar, the Robert A. Miller Professor at NYU’s Stern School of Business and Professor of Data Science at NYU. Vasant is one of the world’s leading thinkers on the impact of AI on society. He was present at the birth of AI and has been involved in every step of its evolution—both as an entrepreneur and as a scholar. He also hosts the acclaimed podcast Brave New World, which explores how machines are transforming humanity in the post-COVID era.

In this episode, we discuss his newest book, Thinking With Machines: The Brave New World of AI. It’s a remarkable hybrid: part autobiography, tracing how his professional life has intertwined with the development of AI; part user’s guide, offering a lucid framework for deciding when to trust machines over human control; and part deep dive into the philosophical and policy implications of creating an alien intelligence.

It was a real pleasure to welcome Vasant back onto the show. I learned a lot during our conversation, and I hope you will enjoy it as much as I did.

Citations

Dawid A, LeCun Y. Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. arXiv. June 5, 2023.

Dennett DC. Intentional systems. J Philos. 1971;68(4):87-106.

Dhar V. Thinking With Machines: The Brave New World of AI. Galloway S, foreword. Hoboken, NJ: Wiley; 2025.

Frank, R. H., & Cook, P. J. The winner-take-all society: Why the few at the top get so much more than the rest of us. Penguin Books; 1995.

Ganguli D, Askell A, Henighan T, et al. Alignment faking in large language models. arXiv. December 20, 2024.

Harari YN. Nexus: A Brief History of Information Networks from the Stone Age to AI. New York, NY: Random House; 2024.

Kauffmann J, Dippel J, Ruff L, et al. Explainable AI reveals Clever Hans effects in unsupervised learning models. Nat Mach Intell. 2025;7:1–10.

Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books; 2018.

Pfungst O. Clever Hans (The Horse of Mr. Von Osten): A Contribution to Experimental Animal and Human Psychology. Rahn H, trans. New York: Henry Holt; 1911.

Popper KR. The Logic of Scientific Discovery. London, UK: Hutchinson; 1959

Suleyman M, Bhaskar M. The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma. New York, NY: Crown; 2023.

Yudkowsky E, Soares N. If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All. New York, NY: Little, Brown and Company; 2025.

Hosted on Acast. See acast.com/privacy for more information.