Why AI Writing Sounds Generic, And What Actually Fixes It

It is not your imagination

Ask three different people to describe the same product and you get three different write-ups, with different emphasis, different sentence rhythm, different things left out. Ask a model the same question three times, or ask three different models, and the family resemblance is obvious: similar hedges, similar structure, similar handful of favorite words. Readers have gotten good at spotting it, which is exactly the problem if the point of writing is to be read as yours.

The cause is not that the model is bad at writing. It is that "generic" is close to the literal objective. A model trained to predict the most probable next word, then tuned to avoid answers that could offend or mislead anyone, converges on the phrasing least likely to be wrong for the widest possible audience. That is a safe average, and a safe average has no voice, because a voice is specific and specificity is exactly what gets sanded off in the direction of safety.

Where the "tells" actually come from

Certain constructions show up disproportionately in AI writing: heavy use of "moreover," "furthermore," and "it's worth noting," a habit of restating the question before answering it, triplets of adjectives, and a fondness for the em dash as a way to add a clause without committing to a sentence break. None of these are wrong on their own. What makes them a tell is frequency, the same handful of moves used at a rate no individual human writer sustains, because no individual writer is drawing from the same statistically averaged corpus on every sentence.

This site bans the em dash in its own guides for exactly this reason. It is one of the more reliable single markers of unedited model output, and removing it forces a rewrite into a full stop, a colon, or a comma pair, each of which reads as a more deliberate choice.

What does not fix it

Telling a model to "sound more human" or "write casually" does very little, because those instructions are themselves generic. The model has no example to work from, so it reaches for its own stereotype of casual writing, which produces a different flavor of average rather than your actual voice.

Running the output through a second pass that swaps synonyms and breaks up sentence rhythm can defeat a naive detector, but it does not produce writing anyone would mistake for a specific person, because it is still optimizing against a detector rather than toward a voice. Fixing the tell is not the same as fixing the cause.

What actually works

• Feed it your own writing, not a description of your writing. "Write like a confident, no-nonsense expert" is a genre. Three to five paragraphs you actually wrote is a fingerprint. The model can match phrasing patterns it can see; it cannot match an adjective describing them.
• Give it the rejects, not just the picks. A short list of phrases you never use is often more useful than a style guide of what to do. Models default toward the phrases you are trying to avoid precisely because those phrases are the safest, most average choice.
• Ask for a first draft, then edit against a checklist, not a vibe. "Remove any sentence that could have been written about a different product" is a concrete filter. "Make it punchier" is not.
• Shorten the distance between draft and publish. The version that reads as generic is usually the first thing the model produced, taken as-is. A human pass that cuts one clause per paragraph and swaps one generic noun for a specific one closes most of the gap, and it is a few minutes of work, not a rewrite.

The same problem shows up in visual output

The underlying mechanism is identical for images and interfaces: a model with no reference defaults to its training data's safe average, which is why an unguided request for "a modern website" or "a professional logo" tends to converge on the same look regardless of which tool generated it. The fix is the same shape too, feed the model an actual reference instead of an adjective. See giving a coding agent real design references for the interface-design version of this problem.

The honest summary

A model has no voice of its own to lose, only an average to default to. Writing that reads as generic is writing that never got a reference to pull away from that average. The fix is not a magic instruction, it is giving the model your actual words to work from, telling it what to avoid as specifically as what to include, and treating the first draft as a draft rather than a finished piece. For the mechanics of writing prompts that carry that information instead of a vague tone request, see how to write effective AI prompts.