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Taste is the scarce skill in an AI-native team

By Cat Wu · Head of Product, Claude Code + Co-work, Anthropic · 2026-04-27 · podcast · How Anthropic's product team moves faster than anyone else — Lenny's Podcast

Tier B · TL;DR
Taste is the scarce skill in an AI-native team

Claim

With code generation cheap, the new high-leverage job is deciding what to write — taste. Taste comes from any background; an engineering background helps in the short term because it gives you a sense of how hard something should be, which feeds prioritization. The premium on taste rises the more capable models become.

Mechanism

When the cost of producing artifacts collapses, the constraint shifts to selection: which artifact, for whom, in what order. That selection work is taste — accumulated judgment about audience, craft, market, and tradeoffs. It is not separable from execution but is increasingly the larger share of the value.

Conditions

Holds when the team operates in domains where outputs are infinite-but-rankable (copy variants, design directions, feature options). Fails in domains where outputs are infinite-and-unrankable (open-ended research where nobody knows what good looks like) — there, taste is no advantage because no one has it.

Evidence

"As code becomes much cheaper to write, the thing that becomes more valuable is deciding what to write."

— Cat Wu on Lenny's Podcast, 2026-04-27

Krithika Shankarraman echoes this directly: "Taste = differentiator in the AI era... companies that distinguish themselves show their craft." Jenny Wen at Anthropic Design adds the heretical caveat that AI taste will get good — humans hold the decision seat, not the unique-creative-judgment seat.

Signals

Counter-evidence

"Taste" is easily a vibes word. Without a written quality bar (Cat's team-principles, Krithika's brand consistency, April Dunford's positioning), "we hire for taste" becomes "we hire people who agree with the loudest senior person." Codify the bar before claiming taste as a differentiator.

Cross-references

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