Good Taste the Only Real Moat Left
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AI and LLMs have made competent output cheap and abundant, shifting the real competitive advantage to human judgment and taste. Taste here means the ability to distinguish generic from specific, diagnose why something fails, and refuse the statistically average output. However, taste alone is insufficient — reducing humans to passive selectors of AI output is a fragile role. The real edge comes from combining taste with context, stakes, domain constraints, and the willingness to build something that couldn't emerge from the statistical middle. Practical advice includes using AI to rapidly explore design space, then applying precise human judgment to reject what's generic or context-blind, and always asking what you're adding that the model couldn't have added on its own.
Table of contents
What taste actually meansWhy AI and LLMs flatten the middleThe new bottleneck is judgmentAI as a mirror for your own tasteA practical loop for training tasteWhy taste alone is not enoughWhat humans still do that models cannot ownWhy this matters for buildersA better way to use AITaste as a side-effect of serious workSort: