Essays on AI, software and the shape of technical work, by Cesaire Tobias.
by Cesaire Tobias
I keep seeing critiques of AI-generated writing where I think — but that’s how I would have written that. The em-dash. The tripled list. The “it’s not just X, it’s Y” cadence. The careful hedging. Even the use of bullet points! People point at these as if they’re machine fingerprints, and increasingly I want to ask: whose writing do you think the machine learned from in the first place?
The “AI tells” people detect are artifacts learned from us. But there’s something interesting in which of us — in which patterns get smoothed out, which get amplified, and who ends up being accused because of it.
Every pattern AI produces was learned from human text. Not human-adjacent or synthesised. You and me. The em-dash, the careful hedging, the structured paragraph with a thesis sentence and supporting beats — these weren’t invented by a model. They came from books, papers, blogs, essays, forum posts, readmes and repos.
When someone declares “this reads as AI,” they are pattern-matching against their own register and mistaking unfamiliarity for artificiality. The “AI tells” they cite aren’t tells of machinehood. They’re tells of careful prose — the kind written in academia, in formal correspondence, in essays by people taught to structure their thoughts. The kind written by people who learned English as a second language and overcorrected toward formality. The kind written, frankly, by anyone who took a writing class seriously. Machines didn’t invent these patterns but we’re now seeing them being echoed at scale.
AI doesn’t attempt to write like any single human. It writes like the centroid of millions of them. Every phrase is human-derived, but the combination trends toward median register — the idiosyncrasies, dead-ends, half-finished thoughts, and voice quirks that mark individual writers get smoothed out in averaging. Then RLHF — the human-feedback training that shapes how models respond — adds a second layer on top: a learned preference for structure, hedging, helpful framing and slightly formal tone.
So the “AI flavor” critics detect is not imagined but it isn’t fake humanness either. It’s averaged humanness. Which is a more interesting target than “AI sounds unnatural,” because it explains why the false-positive pattern lands where it does.
The people who write closest to that average, careful-prose baseline are the ones most likely to get caught in the dragnet. Their writing is closest to what AI was trained to produce.
If you write for a living, or think for a living, the loud version of this matters less than the discourse around it suggests. The honest reading is the inverse of the accusation: the patterns flagged as machine-generated are the patterns of the most common kind of human writing. AI didn’t invent them — it learned them from us.
But by now we’ve all read AI-generated content, often without flagging it as such. What we actually care about, in the moment of reading, is whether the substance holds up and whether we enjoyed it — or, for more formal content, whether it lands clearly and without friction. When those are there, the question of provenance fades into the background. When they aren’t, the source — human or machine — doesn’t save it.
There’s a fair caveat. In creative or literary writing, sounding like the average is itself a problem. Voice is the point, and AI’s centroid-tendency is a real limitation there. But for the vast majority of functional writing,readability and substance are what we actually grade against.
So the scrutiny gap, in the end, is a highly subjective one. What readers are really trying to detect probably isn’t AI involvement at all — it’s evidence of thought. Whether the writer engaged with what they produced or just shipped a half-baked prompt. That’s the fair version of “this reads as AI.” The unfair version mistakes careful human prose for the same absence. The test we actually run when we’re reading is simpler than the one we invoke when we’re complaining: was this worth reading?
May 4, 2026
tags: ai - writing