Product taste is the last moat when AI can execute anything.
Part of the Great Tastes series on LearningFirst. In an AI era where execution is increasingly automated, Product Taste — the judgment to decide what to build, why it matters, and how to specify it — becomes the core skill. This collection explores what Product Taste is, how to recognize it in the wild, how to define and measure it, how to develop it, how it operates in the AI era, its different levels, and its most common traps.
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Defining a skill most people confuse with opinion or intuition.
Defining a skill most people confuse with opinion or intuition.
The signals you can read in a product before reading a single review.
The signals you can read in a product before reading a single review.
Turning taste from "I know it when I see it" into something you can argue about.
Turning taste from "I know it when I see it" into something you can argue about.
The consumption diet, habits, and practice that build taste over time.
The consumption diet, habits, and practice that build taste over time.
Why taste becomes the core skill when execution is automated.
Why taste becomes the core skill when execution is automated.
Feature, product, team, and founder taste — four different skills, often confused.
Feature, product, team, and founder taste — four different skills, often confused.
Consensus, trend-chasing, feature creep, and the power-user trap.
Consensus, trend-chasing, feature creep, and the power-user trap.
“A rare collection on a skill most people assume cannot be taught — and an argument that they are wrong.”
Treats taste as a trained, falsifiable judgment rather than opinion — the move that makes the whole collection work. The sections on recognizing taste in the wild and developing it through teardowns and writing are the ones product people will earmark.
A series treating taste as a trained, falsifiable skill across disciplines — coding, product, writing. Each collection is a curriculum for building judgment where most assume it cannot be taught.
Ranked by semantic similarity
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