Taste as Infrastructure
AI without judgment is commodity. Encoded taste is operating leverage.
By “taste” we mean judgment: your philosophy, constraints, standards, examples, and rules of thumb. It is what makes the work recognizably yours instead of merely plausible.
LLMs are good at finding the center of the distribution. That is useful, but it is not differentiation. Differentiation comes from a point of view: what you believe, what you refuse, what you reward, what you consider good enough, and what you would never ship.
Taste is structural, not mystical:
TASTE = philosophy + constraints + output contracts + behaviorThat structure can be written down, tested, deployed, shared, and improved. Once captured, it becomes infrastructure that shapes every AI interaction: pipelines, agents, interfaces, operating rituals, and customer-facing work.
What Taste Looks Like
- Philosophy: What we believe about how the work should be done
- Constraints: What we will not do, even if we could
- Output standards: What good looks like in concrete examples
- Behavioral rules: How we communicate, decide, escalate, and verify
This is why context work matters. A prompt is temporary. A captured standard can be reused by people and systems.
Implication
Companies that encode judgment will get more consistent, more differentiated output from AI. Companies that do not will compete with commodity output that sounds like everyone else.
The work is not “add AI features.” The work is “capture the judgment that matters, then deploy it where decisions and outputs happen.”
Contrarian To
“AI will figure out what good looks like from the data.”
No. AI can infer patterns, but taste requires a point of view. If the point of view is not explicit, the system will drift toward generic.