The prompt is not the work | sgnt.ai

We need a better vocabulary for describing contribution in the age of generative AI.

There’s a moment that keeps recurring in offices, group chats, and comment sections. Someone shares a piece of writing, a design, a chunk of code, and someone else asks, half-joking and half-hostile: “Did AI write this?” The question sounds like a question about authorship. But usually it’s a question about contribution: did you originate this, direct it, revise it, verify it, or merely release it? Those are five different answers, and the binary on offer — “yes” and “no” — lacks sorely needed expressive power.

A grand new theory of authorship and ownership produces mush, inscrutable academic papers, and navel gazing. A more useful project is smaller and more social: we need everyday language — call it a contribution grammar if you wanna sound fancy — for what kind of human involvement a piece of work represents. The failure of the language we have is one of expression before one of morality: long before anyone lies about their contribution, honest people mislead by accident, or go quiet, because they lack the words to describe their contribution. Law, publishing, and academia are already busy building their own vocabularies but the rest of us just need to be able to answer the question (“did AI write this?”) that one person is actually asking another when they squint at a document and wonder how it got made.

“AI-generated” is a bad label

AI-generated currently covers the person who typed “write me 2,000 words on supply chain resilience” and pasted the output into a LinkedIn post without reading it. It also covers the developer who described an architecture in detail, had a model draft the code, then reviewed every function, rejected two approaches, caught a race condition, and rewrote the error handling. It covers a writer who spent three hours in dialogue with a model, arguing, correcting, and discarding, whose final text doesn’t contain a single sentence from the first draft. It covers the artist who generated fifty images, curated one, and composited it with photographs. It’s a label with a severe lack of explanatory power and a shitty illuminator of what actually happened, as it covers both “help me think this through” and “do my thinking for me”.

Prompted once, pasted the output,<br>posted it unread.<br>decisions: 1

Specified the architecture, reviewed<br>every function, rewrote the errors.<br>decisions: dozens

Argued for three hours; kept nothing<br>of the first draft.<br>decisions: hundreds

Generated fifty images, curated one,<br>composited it with photographs.<br>decisions: fifty-one

AI-GENERATED

Because these are wildly different processes: different amounts of effort expended, different levels of conscientiousness, different levels of ownership, and different levels of intellectual contribution. Fundamentally different levels of deployed agency. A label that treats them identically isn’t any kind of useful disclosure, and worse, it’s usually paired with a moral charge: “AI-generated” now functions as an accusation, so people either hide their process or over-confess to it, and neither response tells you anything useful about the work.

And this isn’t a hypothetical: researchers studying scientific authorship have started calling it the transparency paradox. An author who used a model to tighten a few sentences faces the same disclosure stigma as one who generated whole sections from a prompt, so authors avoid useful tools or stay quiet. Non-disclosure, the evidence suggests, mostly stems from uncertainty rather than outright deceit. In today’s vocabulary, even honest disclosure occludes as much as it reveals.

Typing was always a poor proxy

Part of why we’re stuck is that we’ve quietly treated word-by-word production as the gold standard of contribution: the real author is the one whose fingers made the sentences. But that standard was never coherent, even before AI.

An editor can transform a manuscript so thoroughly that the published book is arguably a collaboration, yet only one name goes on the cover. The Old Masters would have apprentices fill out the dull details. Nobody’s expecting politicians to have actually written their memoirs themselves.

So we already know that contribution comes in varying forms: originating an idea, directing its execution, selecting among options, refining, verifying, approving. But we’ve never had a pressing need for everyday language to distinguish them, because every form involved effort — and effort could stand in for all of it. Generation without effort breaks the proxy. What’s left to describe is agency: not whose fingers made the sentences, but who decided what the sentences would be.

The scarce contribution is judgment

A six-fingered hand in an AI image is embarrassing: not because a machine made it, we all know (now, anyway) that a machine made it. It’s embarrassing because nobody could be bothered to check it. The failure isn’t a failure of...