There is a fear narrative about AI that I think misses the most interesting thing happening. The story goes that we will offload everything to the machine — both the thinking and the doing — and that whatever we get back will be a hollow imitation of what we used to make. Some of this is true. There will be a great deal of work made by people who have abdicated both halves of the job to the agent, and very little of it will be memorable. But the more interesting situation is the one where we keep the thinking and hand off only the doing — and what happens when we do.
The Promise of AI is More than Speed
What happens is more than speed. When you have to describe what you want to the agent — with enough precision that what comes back is what you actually wanted — you begin to think about the thing differently. Description, it turns out, is where the idea often actually gets made. You start by knowing roughly what you want, and the act of articulating it produces a clearer want, which produces a sharper specification, which produces a better thing. Then you do it again. The thing improves; so does the thought.
This is why people find it so easy to disregard AI. The best output comes after iteration. Iteration requires patience. But we have been taught to expect instant gratification. What’s more, the best output comes after we’ve learned to change our input. This is difficult to do when you’re alone with the machine, when you’ve become used to it happening organically with others.
The agent’s current maturity requires a level of input precision that a competent colleague does not. At first, this can be a frustrating blocker; we’ve depended upon a different kind of intelligence in our peers — the kind that requires no elicitation. The machine does. But in a sense, this is a gift. It forces you to think the thing through in places you might otherwise have left fuzzy.
This is not a new pattern. When desktop publishing collapsed the work of a printing house into commands one person could manage on a single screen, it made layouts faster — and it changed layouts. It changed editorial design — the relationship between text and image, between hierarchy and white space, between what could be done and what was therefore done. It changed how we read. The tools that take over a part of the work also remake the work that remains. They always have. And when they shape what is made, they also shape use.
A Three-Part Skill System
A small instance from my own desk: I have begun, lately, generating finished HTML assets where I would once have produced documents and decks. Files are the legacy default for sharing work; HTML is better-indexed, better-shared, and more flexible in how it meets a reader. The agent makes the HTML version trivial to produce. I would not have made it before — the difference in labor was too large. Now the labor is gone, and what the work is has shifted with it. The artifact is not what it was a year ago, and neither is the work.
This is happening through a three-part structure of skills I have been building with the agent.
First, there is a brand system, which holds the visual language and applies it. It is a system of tokens, rules, preferences, and directions.
Second, there is a form system, which knows the ideal structure for the kind of artifact being made — an essay, a one-pager, a deck, a dashboard — and builds accordingly.
And third, there is a content system, which does not generate, but applies editorial rules to the language, voice, and tone of what I write; it copy-edits; it fits content to form; it assists with research and fact-checking.
This system depends upon me to provide the thinking. The agent does the doing. The structure is, I think, the practical form of the argument: the systems carry the doing, and they carry it well precisely because I have spent the time to think them carefully through. We like to call this intellectual property, which I think is a bit obnoxious. It’s really intellectual investment. Every technological advance should be measured not by the measure of intellectual property it absorbs — how much it can do without us — but by how much intellectual investment is worth sowing in it.
The fear narrative is right that the “lazy” version of AI use will produce a great deal of forgettable output.
But the fear narrative constructs a straw man of AI’s utility, ironically by overstating what it can actually do, then critiquing — on practical and moral grounds — the overstated version. The more we think of it as a replacement, the more we’ll find its delivery wanting. The more we think of it as a tool, the more we’ll surprise ourselves with what we make with it.