Amine Bennouna: The "Fossil Fuel" of AI, Decisioning Under Uncertainty, and the Tenure Trap

You might have heard of Amine Bennouna from a previous episode (here)  – from the Math Olympiad in Morocco to an MIT PhD and now a Professor at Northwestern's Kellogg School of Management. Recently, he has been thinking deeply about a critical question: Do we actually need more data for AI? In this episode, Amine discusses the idea that data is the "fossil fuel" of AI , and we have largely burned through the easy reserves. He believes the next frontier isn't about scale, but about quality – knowing exactly which "soil samples" to collect before building the subway line, rather than just feeding the model the entire map.We dive deep into his research on optimal decision-making under uncertainty in relation to data, but we don't stay in the theory. We also wade into the messy, human incentives that shape our world: The Tenure Game: Why the academic pressure to publish volume is killing "moonshot" research – and why we need more people willing to be misunderstood (like Geoffrey Hinton) to make real breakthroughs. The Data Marketplace: A future where we stop giving our data away for free and start treating it like the currency it is. The Crisis of Busyness: Why our generation is wealthier but often less happy , and how "optimizing" your life is meaningless if you don't know the objective function.This conversation is an invitation to pause and ask: Are we collecting the right information, or just more of it?