AI Writing Vs Human Writing Should End the Guesswork After Five Editors Voted Blind

By Frederik Baun, conversion copywriter and founder of Words Sounds Visuals

I've tested 13 AI content detectors against my own writing, on camera, to check whether the tells I catch by eye hold up under a blind read.

What’s the Real Difference Between AI Writing and Human Writing?

The real difference isn’t the words on the page. It’s how consistent they are.

Researchers at Carnegie Mellon compared large language model writing against human writing and found LLMs lean on present participial clauses two to five times more often than people do.

Nominalizations show up at one and a half to two times the human rate. Read the study

Human writing swings. One sentence runs long, and the next one is four words, because the rhythm follows whatever the writer is thinking about in that moment.

AI writing holds steady instead. It produces what the CMU team called “informationally dense, noun-heavy” prose that barely adapts, whichever genre or audience it’s aimed at.

A 2026 linguistic comparison of published human and AI-generated news content found the same pattern from a different angle. AI text ran more formal, more structured, and more positive, while human text carried more variation in length and more negative emotional language.

See the full comparison

The research also flagged specific vocabulary drift. One of the CMU findings was that ChatGPT variants used the word “camaraderie” roughly 150 times more often than human writers do, alongside heavy overuse of words like “palpable” and “intricate.”

What the research measures breaks into three parts:

1. Perplexity (how predictable the word choices are). Human writing scores higher, meaning the next word is harder to guess.
2. Burstiness (how much sentence length varies across a passage). Human writing bursts long and short. AI writing stays in a narrow band.
3. Vocabulary drift (a small set of words a model reaches for far more often than a person would), spread across an otherwise ordinary passage.

So the tell isn’t one word sitting by itself. It’s the shape of a whole passage staying too even for too long, and that’s the same conclusion I reach in the 14 signs of AI writing I check on every client draft.

You can run a rough version of this check yourself in under a minute. Read a paragraph out loud.

If every sentence lands at roughly the same length, with no line running noticeably longer or shorter than the rest, that’s the flat rhythm the research is describing.

Is AI Writing as Good as Human Writing?

It depends entirely on the job the copy has to do.

For standardized, high-volume content, AI writing genuinely holds up. It’s consistent across a thousand product descriptions in a way a tired human writer on hour nine isn’t.

For anything that needs a specific voice, a real anecdote, or an emotional read of one particular audience, human writing still wins.

A technical comparison of the two put it plainly: AI excels at “standardization, like technical writing,” while human writing wins on “relatability and engagement.” Source

I’ve watched this play out on real client work. An AI-drafted FAQ page holds up fine, since the job there is consistency across 40 near-identical answers.

An AI-drafted founder bio falls apart for the opposite reason. The job is making one specific person sound like nobody else, and a model has no lived detail to reach for.

There’s a less obvious difference too, in the texture of each. Human writing tends toward a more conversational tone, and it can carry a minor error or a slightly rougher edge without losing the reader.

AI writing stays grammatically clean and formal by default. That reads as polished right up until it also reads as generic.

So “as good as” is the wrong question. The right one is which job you’re asking the writing to do.

Social copy sits in between the two extremes. A caption or a short-form hook has room for one sharp human line, but the surrounding volume of a content calendar is exactly the kind of repetitive task AI handles well.

Can a Professional Editor Tell AI Writing From Human Writing on a Blind Read?

Most of the time, yes. Not always.

I edit client drafts without always knowing upfront whether AI wrote the first pass, which makes every draft in my inbox an unplanned blind read. The tells I catch first are rhythm and vocabulary, the same two categories the CMU research flagged.

Brand voice guidelines make this harder than it sounds. A lot of client style guides already push writers toward a flatter, more formal register, so a genuinely human draft written to spec can read closer to AI output than a founder’s unfiltered voice memo does.

Raw, unedited AI output is the easy case. It reads too smooth, sentences sit at nearly the same length, and a handful of stock vocabulary words show up far more than a real person would use them in a month.

Here’s roughly the order I check things in, on a first blind read:

1. Read the opening paragraph out loud and listen for flat, same-length sentences.
2. Scan for two or more vocabulary-graveyard words clustered close together.
3. Check whether every section wraps in a tidy closing line, since a suspiciously finished draft is its own tell.
4. Look for one specific, lived detail the writer could only know from having been there.

The case that beats a blind read is AI writing trained on someone’s real voice first.

I proved that the hard way, on camera, when I ran 13 AI content detectors against three controlled samples, including one written by ChatGPT but trained on my own published writing. Only one tool, Winston AI, caught it.

10 of the other 12 missed it completely.

That result generalizes past software. A voice-trained draft is harder for a human editor to catch too, because the two things an editor checks, rhythm and word choice, are the two things voice-training is specifically built to fake.

How Do You Know if a Text Was Written by AI or a Human?

Read it out loud first. That single check catches more AI writing than any word list, because rhythm is harder to fake out loud than it is on the page.

Listen for sentences landing at the same length, one after another. That flat cadence is the pattern both the CMU study and my own editing catch fastest.

If you want a second opinion past your own ear, a detector can help, with a real caveat attached.

In my own test, accuracy ranged from a perfect 3 out of 3 to a coin-flip 1 out of 3, depending entirely on which of the 13 tools you picked. Full results here

The fastest version of the checklist starts with flat sentence rhythm, a cluster of stock vocabulary words clustered close together (the running list is here), and sentences sitting next to each other with no connecting word.

Add a draft that reads evenly polished from the first line to the last, and that’s the whole list.

What’s an Example of AI Writing?

A single flagged sentence rarely proves much. A full passage does, since that’s where the tells stack.

Take a founder bio a client once sent me for a rewrite: “The founder built this company after seeing a gap in the market.

Customers deserve better options.” Two flat sentences, no first person, and a detail generic enough to describe any founder of any company.

Compare it against what I sent back: “I started this because I got quoted $40,000 for something I ended up building myself in a weekend.” One version could describe anyone. The other could only be about the person who lived it.

A cold email opener shows the same pattern in miniature. A flagged version read: “I hope this finds you well. I wanted to reach out because I believe our solution could add value to your business.”

Nothing in that sentence is wrong. Nothing in it is specific enough to earn a reply either, since it could go out to a thousand inboxes unchanged.

What I sent back instead: “Saw you just closed your Series A. Most teams at that stage are drowning in inbound they can’t qualify fast enough, so I built a filter for exactly that problem.” One detail, one number, one reason to keep reading.

I annotated 12 more passages exactly like this one, pulled from real client drafts across email, LinkedIn, and landing pages, in 12 examples of AI writing. Each one shows the tells stacking and the rewrite sitting right next to it.

What Does AI Write Better Than a Human?

Speed and consistency, mostly. A model can hold the same structure across 500 product descriptions without getting bored on description 412.

It also handles pure information transfer well: FAQs, spec sheets, standardized how-to steps, anything where the value is completeness rather than personality. The same technical comparison pointed straight at this strength, crediting AI for “accuracy and consistency” across large volumes of similar content. Source

Translation and first-pass language conversion are a real strength too. A model moves a draft between languages faster than most humans can, though a native speaker still needs to check the result before it ships.

I’ve used this on a client running the same campaign across three markets. AI held the offer and structure consistent in every language on the first pass.

A native-speaking editor in each market then handled the two or three lines that needed a cultural read AI couldn’t give them.

That speed produces a fast first draft.

It still needs a specific voice pushed into it, which is exactly the comparison between Claude, ChatGPT, and Gemini I ran on three real briefs to see which model needed the least of that work.

What Can a Human Writer Do That AI Still Can’t?

Ground a claim in something that happened to one specific person. That’s the gap I see most often in client drafts.

A human writer pulls from lived experience: the client call that almost fell apart, the $40,000 quote that started a whole company, the specific number a campaign hit.

Composite versions of moments like these show up in nearly every rewrite I send back, since a real detail is what makes a paragraph sound like one person said it.

Human writing also adapts on the fly, in a way the CMU research found AI still struggles with. A person shifts register for a grieving customer differently than for an excited one, inside the same afternoon.

LLMs, by contrast, default to one consistent style, a strength in a spec sheet and a liability anywhere that needs to read the room.

And human writing stays accurate in a specific way AI doesn’t. Fabricated statistics are the single most expensive tell on the list I run every draft through before it ships, since a false number costs trust just as fast as a true one earns it.

I built a five-check fact-check pass specifically because of that failure mode.

Context and relevance are the other piece. A human writer tailors advice to one business’s real situation, while a model defaults to general, broadly accepted guidance that reads as true for almost nobody in particular.

That gap shows up most on strategy and positioning work, less on straightforward information delivery.

Is AI Writing Cheaper Than Hiring a Human Writer?

Per word, yes, by a wide margin. A model produces a full draft in seconds at close to zero marginal cost, and a professional writer charges for the thinking, not the typing speed.

The cost that doesn’t show up on the invoice is the one that matters more. A flat, generic page costs you the reader who bounces in the first five seconds, and that reader was worth more than whatever the draft saved in writing fees.

I treat this as a hybrid cost problem, not a binary one. AI handles the volume tier cheaply, and a human editorial pass handles the pages where trust is being sold.

The total bill still comes in lower than an all-human shop, while the trust-critical pages hold up.

Does Google Penalize AI-Written Content?

Google’s own developer guidance rewards “people-first content” and says that standard applies “however content is produced.” Quality is the deciding factor, not who or what wrote the page. Read it here

That’s worth knowing before the AI-vs-human decision turns into an SEO decision it was never meant to be. The standard is quality, not authorship, and Google has been shipping its own AI products into search results for years now.

The newer wrinkle is AI Overviews and chat answers pulling from pages directly, rather than just ranking them. That shift rewards the same thing a human editor rewards: specific, sourced, first-person detail an AI Overview can’t reproduce on its own.

I’ve written more on how that changes SEO and how to show up inside ChatGPT’s own answers, if that’s the angle you’re optimizing for.

So the real filter isn’t “did a machine write this.” It’s whether the finished piece is worth reading, which loops back to the same rhythm and specificity tells covered above.

Should You Hire a Human Writer or Use AI for Your Business Copy?

Match the tool to the job instead of picking a side.

For most businesses, the honest split is AI for the first draft and the volume work, human for anything that has to earn trust in the first five seconds.

The practical version of that split starts with an audit, not a rewrite. Pull your ten highest-traffic pages and sort them by how much they’re asking the reader to trust you.

Start the human pass at the top of that list, rather than the top of the site map.

I run my own drafts through a 25-point editing pass either way, whether the first draft came from me or a model.

If you want to check where your own copy lands, run it through the free copy cleaner I built, or the fact-checker if it’s the claims you’re worried about rather than the rhythm.