When AI makes competent output cheap and abundant, the competitive advantage shifts from production capability to judgment — the ability to distinguish what is generic from what is true and worth pursuing. "Taste" in this context means distinction under uncertainty: what you notice, what you reject, and how precisely you can diagnose what feels wrong. However, taste alone is insufficient; it must be combined with authorship, stakes, and the willingness to build something that could not have emerged from the statistical average.
Why AI flattens the middle
LLMs are pattern-compression engines that recombine absorbed patterns at speed, naturally trending toward the safe center of the distribution. The result is a "crowded 7 out of 10 world" where:
- Landing pages share the same structure with different logos
- Product copy could describe almost any app
- Visual design looks modern but not memorable
- Essays have clean headings and little lived judgment
Average used to be hard enough to create separation. Now it is abundant.
The new bottleneck: judgment
AI compresses the cost of first drafts, moving value downstream. The scarce skill is now refusal — the ability to say:
- "This looks fine, but it is too generic"
- "This sounds impressive, but it hides the real trade-off"
- "This interface is polished, but it doesn't fit how the user actually thinks"
Taste becomes useful when it moves from vibe to diagnosis.
AI as a mirror for taste
Generating 10+ versions of an artifact with AI reveals how clear your own judgment is. If your critique stays vague ("this feels off"), your taste is underdeveloped. If it becomes precise ("this collapses a regulatory constraint into marketing language"), your judgment is stronger than the model output.
Why taste alone is not enough
If humans reduce themselves to selecting from AI outputs, they become discriminators in a machine-driven loop — useful but fragile. Important work historically emerged from co-creation under constraint: arguing with reality, collaborators, budgets, materials, and consequences.
What humans still own that models cannot:
- Holding the stake — real consequences (trust, regulatory exposure, team capacity) that don't fit in a prompt
- Working with the truly new — genuinely novel ideas look wrong at first because they don't resemble the training set
- Choosing direction — what problem is worth solving, what trade-off is acceptable, what you refuse to optimize for
A practical loop for training taste
- Pick one high-leverage artifact from your week
- Generate 10–20 versions with AI
- For each, write one sentence starting with "fails because..."
- Rewrite the strongest version with hard constraints (no buzzwords, one idea per sentence, must acknowledge a real trade-off)
- Ship the final version and observe what happens
The goal: build a sharper rejection vocabulary.