AI has collapsed the cost of execution. What it has not collapsed — and may in fact be widening — is the distance between making something and knowing what's worth making.
For most of the history of creative work, talent and taste traveled together, almost by necessity. To paint convincingly, you needed years at the easel. To write a working piece of software, you needed to master a language's syntax and its failure modes. To design a typeface, you needed a hand steady enough, and trained enough, to draw a stroke that felt inevitable. The skill required to execute an idea acted as a filter. If you had bad taste, you rarely had the patience or technical grounding to produce much of anything — the two were bottlenecked through the same narrow door.
Generative AI removes that bottleneck. A model can now produce a competent draft, a plausible layout, a passable riff, on command. The barrier that used to separate people with ideas from people who could realize them has effectively disappeared. This is, on balance, a remarkable democratization — more people than ever can externalize what's in their head. But it has also exposed something that was previously hidden by the difficulty of execution itself: most of what made “good” work good was never really the execution. It was the thousand small judgments that preceded and surrounded it — what to keep, what to cut, what to imitate, what to refuse, what the work was for.
Call this taste: the capacity to discriminate, reliably and under pressure, between the merely adequate and the genuinely right. Taste is not an aesthetic preference in the abstract sense of liking certain colors or fonts. It's closer to a compressed, internalized model of an audience, a medium, and a moment — built from exposure, failure, comparison, and correction over years.
What makes taste hard to manufacture is that it is formed by consequence. You develop it by shipping something that didn't land and understanding why, by watching a decision you were confident about fail in front of real people, by sitting with the gap between what you intended and what you actually made. A model has no equivalent process. It has seen an enormous amount of finished work, but none of the discarded versions, none of the arguments, none of the moments where someone chose the harder option because the easy one was hollow. It learns the surface of good judgment without ever having borne the cost of bad judgment. It can imitate discernment statistically. It cannot originate it.
This has a practical consequence for how creative careers and organizations should now be structured. The premium is moving away from people who can produce and toward people who can select, edit, and direct — toward the person on a team who looks at fifty AI-generated options and immediately knows which three are worth iterating on, and precisely why the other forty-seven are wrong. This role used to be implicit, buried inside the act of production itself, because the person doing the producing was, by necessity, also the one making the judgment calls. Now the two can be separated, and increasingly are. A person with strong taste and weak technical skill, augmented by AI, can now out-produce a person with strong technical skill and weak taste. That reversal is new, and it's not fully priced in yet — not in hiring, not in education, not in how creative teams assign status.
None of this means talent stops mattering. Deep technical skill still matters enormously at the edges — in solving genuinely novel problems, in knowing when a tool's output is subtly, dangerously wrong, in doing the last ten percent of refinement that separates competent from exceptional. But talent is no longer the gate. Taste is. And taste, frustratingly for anyone hoping for a shortcut, is not something a prompt can install. It's built the slow way: by making things, showing them to people who'll tell you the truth, being wrong in public, and paying close enough attention to absorb why. AI can compress the time between having an idea and seeing it made real. It cannot compress the years it takes to know, with any reliability, which ideas deserve that treatment in the first place.
