Thinking Through AI

Artificial intelligence is rapidly becoming embedded in hiring, but the most important question may no longer be whether a single algorithm is biased. The larger issue is what happens when large portions of the labor market rely on the same underlying systems to make decisions about who gets seen, who advances, and who gets left behind. Drawing on Stanford's research into algorithmic monocult...

For the past year, much of the AI conversation has focused on chips, models, and capital. But what if those aren’t the only metrics that matter? In this episode, I explore whether we're measuring AI competition correctly. While America continues to lead in frontier model development, China appears to be focusing heavily on adoption, workforce readiness, industrial deployment, and practical AI...

This episode was sponsored by the, " AI Adoption Leadership Audio Briefing ." AI is absolutely changing white-collar work, but the conversation around what gets disrupted, compressed, or replaced has become dangerously oversimplified. Not all knowledge work is structurally the same.  What works in software engineering doesn’t automatically map cleanly onto law, compliance, healthcare, fi...

What if the future of AI leadership isn’t about who builds the best models, but who learns how to use them fastest? This piece explores a critical gap in the AI conversation: the difference between access and understanding. As global competition intensifies, the real advantage may come from societies that treat learning as a shared, cross-generational system, not an individual responsibility. This...