Carrying Masterpieces for Decades
Tatsuya Nakadai spoke of carrying 'the load of everyone's masterpieces' in his twenties. How did some people learn to stay resourced for decades?
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The Not Now List
When AI drops implementation time from two days to two hours, saying "no" doesn't get easier—it gets harder. Speed amplifies feature creep instead of reducing it. The bottleneck shifts from building to focus. Before you ask AI to build something, pause. One breath. Three questions. Most ideas don't
Draw the Line Before You Need It
Netflix defined AI principles before launching projects: what's protected, what's workable, where the boundary sits. Not technical specs—trust boundaries. Draw them first, then work within them. The alternative—deploy tools, then scramble to contain damage—erodes trust faster than efficiency can res
When Building Gets Fast, Saying No Gets Harder
AI-assisted development accelerates MVP creation, but speed increases feature creep pressure rather than reducing it. The bottleneck shifts from implementation to focus.
Draw the Line Before You Need It: Trust Boundaries in AI Work
Netflix's AI principles define what AI can touch and what stays human. These aren't technical constraints—they're trust boundaries.
Too Coherent
A computational Turing test reveals AI systems not through what they can't do, but through what they do too well: excessive politeness marks the boundary more clearly than any capability test. The authenticity asymmetry—bots trained to be ideal conversational partners fail by being too coherent, too
The Authenticity Asymmetry: Why AI Reveals Itself Through Politeness
AI systems fail the Turing test not through limited intelligence, but through excessive niceness. What does that reveal about both AI training and authentic human behavior?