ai marketing · 7 august 2026 · 7 min read

why ai content sounds generic — and how to fix it

You asked the AI for a post about your business and got a post about everyone's business. Swap your company name out of it and the whole thing still works — and if you've been wondering why your AI content sounds generic, that's the symptom: your customers can feel it even when they can't name it.

Here's the part most "10 prompts to fix robotic writing" articles skip: AI content sounds generic by design, not by accident. It isn't a settings problem, and the fix isn't a magic prompt. Once you see the actual mechanism, the fix becomes obvious — and it's less work than you'd think.

We should say up front: we're an AI marketing agency, so we have a stake in this argument. The mechanism below is also the reason our whole service is built around a brand voice document rather than a better prompt. Weight our opinion accordingly.

the maths of the average: why ai content sounds generic by design

A language model has one job: predict the most probable next word, given everything it has read. That's the whole trick. It read a colossal share of the internet, and when you ask it for "a social post for a plumbing business," it produces the statistical centre of every plumbing post it has ever seen.

Probable is the objective. And probable, by definition, is average.

This is why the output feels competent and forgettable at the same time. The model isn't malfunctioning — it is succeeding at the exact thing it was built to do. Ask it for marketing with no further information and the most probable marketing is the marketing everyone else already published.

It gets worse: your competitor is typing nearly the same prompt into the same model this week. Same inputs, same maths, same centre of the same distribution. The tool isn't copying anyone. It's just that "the most likely answer" is a single destination, and everyone asking a similar question gets driven to it.

the sameness is measurable — two studies that prove it

This isn't a vibe; it has been measured, twice, in the same journal.

In a Science Advances experiment, researchers Anil Doshi and Oliver Hauser had writers produce short stories, some with ideas from an AI and some without. The AI-assisted writers produced stories rated more creative and better written — but the stories were measurably more similar to each other than the ones humans wrote alone. The authors call it a social dilemma: "with generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced." Individually sharper, collectively identical.

The second study caught the sameness in the wild. Dmitry Kobak and colleagues analysed the vocabulary of more than 15 million biomedical paper abstracts from 2010 to 2024 and found the arrival of ChatGPT produced an abrupt surge in specific style words — enough to estimate that at least 13.5% of 2024 abstracts were processed with an LLM — reaching 40% in some disciplines and journals. These are scientists — professionally precise writers with their names on the page — and the model still herded them toward the same vocabulary. (The study's own title, "Delving into LLM-assisted writing," is a wink at the most notorious of those style words.)

If it can homogenise 15 million research abstracts, it will homogenise your newsletter without breaking stride.

the tells: how readers clock it in three seconds

Readers have now seen so much model-average writing that they pattern-match it instantly. The surface tells are familiar:

• The stock phrases. "In today's fast-paced world." "Unlock the power of." "Elevate your brand." "Game-changer." These are the highest-probability marketing words in existence — which is precisely why the model reaches for them and why they say nothing.
• The rhythm. Every paragraph three tidy sentences. Rule-of-three lists everywhere. A conclusion that begins by restating the introduction.
• The relentless balance. The model hedges because taking a side was never the most probable continuation. Real businesses have opinions; averages don't.

But the deep tell is the one that survives editing: no specifics. No prices, no dates, no customer's actual words, no "we tried this and it flopped." Generic AI content contains nothing that could only have come from you — because you gave it nothing that could only have come from you.

That's the test worth stealing: could this sentence sit unchanged on a competitor's site? If yes, it isn't yours yet.

Where p.a. fits: this mechanism is why we don't start any client's content with a prompt. We start by building a Brand Brain — your voice, offers, opinions and banned words, documented — and every post, email and blog is produced from that. Same models everyone has, pointed at raw material only you have.
See what a month of that looks like →

how to fix it: five moves that beat the average

Every fix below works the same way: it moves the model off the centre of the internet's distribution and onto the centre of yours. That's the entire game.

1. Give it something to average from. The model defaults to the internet's average because that's all it has. Feed it yours instead: your past emails, your best posts, the way you actually explain your service across the counter. Ten minutes of real examples outperforms any clever prompt, because you've changed the data the prediction runs on.
2. Supply the claims only you can make. Your prices, your turnaround, the job that went wrong and what you did about it, the thing customers always say at handover. AI can phrase these; it cannot know them, and it will invent them if you let it. The specifics are your moat precisely because they're the one thing the model can't generate.
3. Take a position. Decide what you'd argue before you prompt — a model asked to be interesting hedges, but a model asked to defend your stance can hold it. "Cheap paint costs more by year three" is an opinion no average will ever produce on its own.
4. Keep a banned-word list. Write down the tells above, add the clichés of your own industry, and enforce the list on every draft. Mechanical, boring, and immediately effective — sameness lives in exactly these phrases, so deleting them forces specificity into the gap.
5. Edit as the last human in the chain. Treat every output as a draft from a talented writer who has never met you. Read it once asking a single question: what would I never say, and what's missing that only I know? Fix those two things and the maths is beaten. (Worth noting: Google has said plainly that it ranks on quality "however it is produced" — the search engine doesn't punish AI, it punishes average. We cover that side in our AI marketing guide for small business.)

None of this requires an agency. It requires raw material and a spine — most businesses have both and feed the model neither.

the honest takeaway

AI content sounds generic because generic is the mathematically correct output for an empty prompt — the model's job is the most probable answer, and the most probable answer belongs to everyone. The research says the pull is real even for careful writers: individually better, collectively the same.

The fix is not a better model. It's better raw material — your voice, your facts, your opinions, fed in — so the average it produces is the average of you. Do that consistently and the same tool that flattens everyone else's marketing starts compounding yours; it's the same logic as the automation-vs-hiring maths — the system is only ever as good as what you load into it.

The blank prompt is the enemy. Feed the machine.