During one of my classes at @GonzagaU, a student asked about AI and the job market: “What do I need, and how do I learn it?” Here’s what I told them, but I'd love to hear from you too. Some of those students now follow me and will benefit directly. My answer had two parts: First, in any era where work becomes commoditized and uncertainty is high, relationships, differentiation, adaptability, and impact matter more than ever. Contribute to mission and team, develop some superpowers, be flexible to change, and treat everyone decently. Opportunity will follow. But none of that works without a connection to what you're doing — a combination of aptitude, intuition, and curiosity. You can’t invest enough to build differentiated impact without connection. So don’t pick a career that analytically makes sense but that you hate. Pick a direction where you have aptitude, intuition, and curiosity, and just start making a difference. Second, get comfortable building and contributing in a world that is increasingly data-driven and algorithmically managed, for better and for worse. Concretely, I think every undergrad should take a basic data science class, get comfortable using AI tools (broadly and in field-specific ways), and develop an intuitive and healthy skepticism around the use of data. When I took econometrics at @GoldmanSchool, I learned a lot of techniques I’ve carried with me. But what do I remember most? Just how easy it is for models to spit out bad results, and for humans to misuse good results. How would you answer this question?