Why AI Writing Sounds Robotic (and How to Fix It by Hand)

AI writing sounds robotic largely because it is generic. Research on 2024 models suggests tuning pushes them toward one dense, noun-heavy style, and they know nothing about your facts or your reader. The fix is to cut the stock moves and add what only you know, which a word swap can't do.

We tested this on nine fresh AI drafts, and the usual advice mostly missed. A tool such as our AI Humanizer, free to try on the web, is built to rework repeated phrasing and hedging, but it can't supply your facts. That part stays with you, and the six edits further down show how.

What we found in nine AI drafts

On 2 November 2026 we asked three Claude models (Haiku 5.5, Sonnet 5.5 and Opus 5.5) for a blog post intro of about 130 words on each of three topics: why small businesses should build an email list, why teams should write down meeting notes, and how to give feedback to a coworker. We sent the prompts through an agent tool that adds its own background instructions, which may affect style, told the models to reply with the text only, kept the first reply and edited nothing. Then we checked the passages against the advice common in the guides we read.

So what did they share? Four things kept coming back, though we did ask for intros, and human intros do some of this too:

• An announcement near the end. Seven of the nine closed by describing what comes next: "In this post, we'll walk through..." or "Here's why..."
• Three-part lists. We counted 16 in 68 sentences, and eight of the nine passages had at least one.
• No source or figure to check. No passage contained a digit, or named a person, a company or a source.
• The same thoughts in the same words. Sonnet and Opus each wrote, word for word, "An email list is different, because it belongs to you." They also both used the phrase "Good notes don't need to be long or polished." All three email drafts said the list "belongs to you."

Nothing in these drafts is obviously false, but models from the same company reached for the same sentences, and any business could publish them.

This was a small check, not a study. It covers nine short passages from one company's models and a single style of prompt, with no human writing to compare against. Most passages came from one request per model, so they aren't fully independent runs. Other models and longer texts may behave differently: one vendor benchmark cited in Wikipedia's guide put about 60% of GPT-6's sentences at 8-21 words. It does show that a word list can come back clean on text that still reads as generic.

Why AI writing sounds this way

Tuning pushes models toward one style

The best evidence comes from a 2025 study in PNAS by Alex Reinhart and colleagues, who had models continue human-written texts and compared the results with how the humans actually went on. They tested GPT-4o, GPT-4o Mini and several Llama 3 variants. Instruction-tuned models used present participial clauses at 2 to 5 times the human rate and nominalizations, nouns built from verbs or adjectives, at 1.5 to 2 times the human rate.

The raw Llama base models used these features at rates similar to human texts. The authors' reading is that instruction tuning "appears to make the model output less human, not more." They suggest it also limits how well a model can imitate other styles, so asking for "write like a person" may only go so far.

The study also complicates a popular tip. GPT-4o used the agentless passive ("mistakes were made") at about half the human rate, so hunting for passive voice may be fixing the wrong thing. These were 2024 models, and newer ones may differ. Treat the numbers as a direction, not a current measurement.

Human feedback may play a role

Why might tuning do this? One lead, on vocabulary rather than grammar, comes from a COLING 2025 paper by Tom Juzek and Zina Ward on why ChatGPT overused words like "delve." They found no evidence that training data, architecture or algorithm choices were the cause. Their tests on Llama were consistent with feedback from human raters playing a role, but the evidence was mixed and they do not claim to have proven it. Treat it as a possible partial explanation.

The model doesn't know what you know

Our prompt gave a topic and a length. It said nothing about customers or results, so the models filled the space with what could be said of any business or team. When one reached for a concrete scene, it had to invent one: Sonnet's meeting-notes draft has one person remembering a Friday deadline and another swearing it was Tuesday. That is a nice touch, and no such meeting happened.

The visible tells keep changing

Wikipedia's editors keep a long field guide to signs of AI writing. It sorts some signs by era and files overused em dashes under historical signs, dated 2022 to September 2026, while noting that they still appear. A July 2026 study it cites found that, of contemporary models, only Claude used em dashes more than professional writers, so the zero in our Claude drafts may reflect newer models or our setup. It also warns that these signs are not proof, since people write this way too, and some are specific to Wikipedia. A checklist of tells can go out of date quickly.

How to fix it by hand: six edits, in order

Here is the Haiku email draft from our test, unedited (145 words):

Building an email list is one of the few marketing moves a small business fully owns. Social media feeds change their algorithms, ad costs climb, and a platform can restrict your reach overnight. An email list belongs to you. Every subscriber is someone who asked to hear from you, and that permission is worth more than a fleeting impression. Email also tends to convert well: a message lands in a customer's inbox and waits there, ready to be read when they're ready to buy. For a small team with a limited budget, that makes a list one of the most dependable ways to bring customers back, announce a new product, or simply stay in touch. In this post, we'll walk through how to start a list from scratch, what to offer people in exchange for their address, and the simple habits that keep subscribers engaged.

1. Delete the announcement. The last sentence describes the article instead of starting it. Cut it and let the next paragraph do the work.

2. Run the swap test. Ask of each sentence: could a competitor publish this unchanged? "An email list belongs to you" fails, and so does nearly everything else here. Mark what fails. Those sentences get replaced or cut.

3. Add what only you know. No tool can do this step for you. What happened to you? What number did you see? Who said what? If you can't answer, cut the claim. Don't ask a model to "add a personal story," because it will make one up.

4. Turn nouns back into verbs. "That permission is worth more than a fleeting impression" is about abstractions. "They asked to hear from us" is about people. Name who did what.

5. Break up the three-part lists. "Bring customers back, announce a new product, or simply stay in touch" sounds complete and says little. Keep the one item that matters most to your reader.

6. Take a position, then read it aloud. Say what you'd actually do. Keep a hedge such as "tends to" when it is accurate, and drop it when it is only there to sound safe. Where you stumble reading aloud, rewrite.

Here is the result as a template. We left brackets rather than invent a shop, because every bracket is something only the writer can fill honestly:

Last [month], [platform] stopped showing our posts to most of our [number] followers. We noticed when [what dropped]. Our email list has [number] names, and [platform] doesn't decide who sees what we send them. They signed up [where and why], and when we send [what], [what happens next]. So our advice is to start a list before you spend more on ads. Ours began with [the simplest thing you did].

Those 70 words carry more than the original's 145, once the brackets are true. If you can't fill most of them, the draft isn't ready for editing. You have no point to make yet, and more polish won't give you one.

What not to do

• Don't add typos or awkward phrasing on purpose. They make your work look careless and tell the reader nothing.
• Don't invent anecdotes, sources or numbers. A made-up detail reads well until someone checks it.
• Don't just swap synonyms. The same empty sentence with a rarer word is still empty.
• Don't edit for a detector. Detectors make mistakes in both directions, as we cover in are AI detectors accurate?. Edit for the person reading.

Editing also doesn't change the rules you work under. If your school or employer restricts AI use, a good rewrite doesn't make the original use allowed, so check the policy first.

Where tools fit

Tools handle the mechanical part. Better inputs help before you ever edit: give the model your facts, your reader and a sample of your own writing, and ask it to leave blanks rather than guess. In Clever AI Hub you can ask GPT, Claude, Gemini, Grok and others from one chat and switch models in a tap, so it's easy to try the same prompt on several and keep the draft that needs the least work. You can also save your instructions in an agent and reuse them in any chat.

After your hand edit, the AI Humanizer can smooth phrasing in 20 to 4,000 characters at a time, in one of five tones. We did not test its output for this article, so we make no claim about the results. Its own page says no tool can promise to pass every detector, and you should read the result and fix anything that doesn't sound like you. A grammar check at the very end catches what's left.

Common questions

Is it wrong to use AI for a first draft? That depends on where it's going. Schools and employers set their own rules. For web content, Google says that "appropriate use of AI or automation is not against our guidelines," while content generated mainly to manipulate search rankings breaks its spam policies. It also says using AI gives content no special gains.

Can people reliably spot AI writing just by reading it? Not reliably. Wikipedia's guide says many of these patterns also appear in human writing, and that studies have found people often do little better than chance, though some experienced readers do better. Don't accuse anyone on style alone.

How long does this take? The cuts and the swap test are quick. Step 3 takes as long as it takes to remember what actually happened. When you have nothing to add there, the time is better spent finding out than polishing.