There's this economist everyone in Silicon Valley can't stop talking about. His hunger for understanding systems, markets, and human behavior made him a legend in tech circles. But here's the question that keeps popping up in conversations: does that insatiable appetite for knowledge still matter when AI can process information faster than any human mind?

We're living through a weird moment. Machine learning models devour datasets that would take economists lifetimes to analyze. They spot patterns invisible to traditional research methods. Some argue that raw computational power is replacing deep thinking. Others believe human intuition and contextual understanding can't be replicated by algorithms—no matter how sophisticated they get.

The real tension isn't about whether AI is powerful. Obviously it is. The question is whether the traditional economist's approach—asking why, building frameworks, challenging assumptions—becomes obsolete or becomes more critical than ever. When machines optimize for answers, who's responsible for asking the right questions?