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Geoffrey Litt, a design engineer at Notion, argues that as AI agents write code faster than humans can absorb it, understanding still matters — not to verify correctness, but to remain a creative participant in the project. He walks through three techniques for building that understanding efficiently — structured "explainer" docs with embedded quizzes, interactive micro-worlds for stepping through changes, and shared team spaces for building common mental models.
Chess prodigy turned martial artist Josh Waitzkin joins Andrew Huberman to discuss the principles of learning that transfer across chess, tai chi, jiu-jitsu, and foiling, including his 'most important question' practice, thematic interconnectedness, and the shift from preconscious to postconscious competition.
Steph Ango describes a "parasite" that thrives when people outsource understanding of their health, money, and infrastructure instead of learning it themselves, moving through stages of acceptance, extraction, and intervention. He argues building your own understanding — even imperfectly — pays dividends that delegation never does.
Tom Yeh (AI by Hand, CU Boulder) argues that teaching AI math by hand at human speed — actually writing out the matrices — builds the kind of foundational skill that survives every technology wave, from big data to deep learning to quantum computing.
The Gyeongbokgung palace analogy lands well: the whole palace burned down in the 1500s except the stone foundation, and they rebuilt on the same base centuries later. Matrix multiplication is that foundation. His hiring point is equally sharp — if you hire for genuine curiosity and problem-solving, AI adoption follows automatically. No "AI-native" mandates needed.