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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.
Three months into AI-assisted development, the author finds the fatigue has shifted from wrestling with implementation details to constant architecture-level decision-making, since the AI is ready to implement whatever is chosen the moment it's chosen. Also flags a code-review blind spot: you can't interrogate an AI's reasoning after the fact the way you can ask a human collaborator why they made a choice.
Referenced in George Hotz's "I love LLMs, I hate hype" as a caution about AI-assisted coding increasing cognitive fatigue.