Why Most AI Writing Still Sounds Like AI

You can usually feel it within a sentence or two. The text is grammatically perfect, reasonably informative, and unmistakably written by a machine. Nothing is wrong with it. That’s exactly the problem — nothing is wrong with it, and nothing is alive in it either.

I’ve spent a lot of time on this because Propelr, one of our products, exists to solve it. Here’s why AI writing sounds like AI, and what actually fixes it.

Generic is the default, not a bug

A language model predicts the most likely next word given everything it has seen. Absent any information about you, the most likely next word is the average one — and the average of all business writing on the internet is bland, hedged, and corporate.

So when you ask a raw model to “write a LinkedIn post about hiring,” it doesn’t write badly. It writes the mean of every LinkedIn post about hiring ever published. That mean is generic by definition. It’s not the model failing; it’s the model succeeding at the wrong target.

This is why “make it sound more human” prompts disappoint. Human isn’t a direction the model can move toward, because humans don’t share a voice. You have a voice. The model needs yours, not a vague instruction toward humanity in general.

The tells, specifically

Machine-written text has a fingerprint. Any one of these is harmless; all of them together is the giveaway.

• Cliché connectors. “In today’s fast-paced world.” “Let’s dive in.” “It’s not just a tool, it’s a game-changer.” These are the model reaching for the highest-probability transition, which is also the most worn-out one.
• Relentless evenness. Every sentence is medium length. There’s no short one. No fragment. No rhythm. Human writing varies its pace; machine writing idles at one speed.
• Hedging on everything. “Can help,” “may improve,” “is often considered.” The model avoids commitment because commitment is where the average dissolves into a specific, falsifiable claim.
• The eternal rule of three. Everything comes in tidy triples. Real thinking is lumpier than that.
• No specifics and no opinion. The surest tell. Machine text describes the category; a person names the thing, drops the real number, and says what they actually think.

Propelr bans more than thirty of these outright — a block-list you can see and add to. Not because the phrases are forbidden words, but because their pileup is the fingerprint, and removing them forces the writing back toward something specific.

Voice is data, not an adjective

The real fix is to stop treating voice as a style you request and start treating it as data you provide.

Propelr reads three of your past posts and extracts what it calls your Style DNA: your typical sentence length, your hook patterns, how formal you are, the openers you actually use. Every draft then passes through that filter. The model is no longer imitating the average writer — it’s imitating you, because now it has seen you.

The difference is stark. “Write like a human” gives you the mean of humanity. “Here are three things I wrote, now write the fourth” gives you something that sounds like it came from your account, because statistically it’s shaped like the things that did.

Why this matters beyond social posts

The lesson generalizes past marketing copy. Any time you want AI output to sound like a specific person or brand, the move is the same: give the model examples of the target, not adjectives describing it. Show, don’t tell — the oldest writing advice there is, applied to the newest writing tool.

Generic is the floor you get for free. Sounding like yourself takes giving the model something of yourself to work from. That’s the whole idea behind Propelr — and it’s why its posts read like you wrote them, not like a machine guessed what you might say.

If you’re shopping the category rather than taking our word for it, we compared it against Taplio, AuthoredUp and Supergrow on price and on what each one does to your account.

Frequently asked questions

Why does AI-generated writing sound generic?

Because a model with no information about you defaults to the statistical average of everything it read — which is corporate, hedged, and smooth. Generic writing is what you get when the model has no voice to imitate, so it produces the mean of all voices. The fix isn't a cleverer prompt telling it to 'sound human'; it's giving it your actual writing to imitate.

What are the tells that text was written by AI?

Cliché connectors ('in today's fast-paced world', 'let's dive in', 'it's not just X, it's Y'), relentless evenness with no short punchy sentences, hedging on every claim, tidy rule-of-three lists everywhere, and a total absence of specific detail or opinion. Individually these are fine; all of them together is the machine fingerprint.

How do you make AI writing sound like you?

Train it on samples of your own writing rather than prompting it to be generically human. When a model can see your sentence length, your openers, your rhythm, and the phrases you'd never use, it imitates you instead of the average. Voice-trained generation plus a block-list of clichés is far more effective than any 'write like a human' instruction.