Implementation Is Cheap; Taste Is the Bottleneck

AI-native product teams can generate many working artifacts quickly, so the scarce skill shifts from implementation to choosing, curating, and framing the right work. Why it matters: Polished AI prototypes can look production-ready before the problem, user need, or model reliability has been validated. Teams need stronger judgment gates, not just faster building.

Try this: For the next AI-generated prototype, label its stage explicitly: exploration, capability test, interaction prototype, beta, or release candidate.