AI-generated. Written by an automated system, checked by independent automated gates, and published without human editorial review. How we label and verify.
Short answer: AI writing sounds like AI mostly because of its shape, not its words. Rigid templates, sentences of the same medium length, lists that always have three items and stock filler phrases create a steady rhythm readers notice before they can name a single sentence. In our content pipeline, loosening the structure and varying sentence length raised the human-voice score while factual accuracy stayed the same.
Why does AI writing sound like AI?
Most readers can feel a robotic voice before they can point to the cause. The cause is rarely vocabulary. It is structure. A rigid template gives you consistency, and it also gives you sameness. That sameness was the single biggest cause of the AI feel in our drafts.
The structural tells to look for
- An opening that announces the structure: “First, second, third…”
- Uniform sentence length. A metronome of medium sentences.
- Every list has three items, phrased the same way.
- Filler such as “it’s worth noting”, “importantly”, “in practice”.
- Heavy use of em-dashes and other punctuation flourishes.
How to make AI content sound human
- Break the rhythm. Mix short sentences with longer explanatory ones. Contrast is the point.
- Change list sizes. Two items. Four items. Not always three.
- Cut the roadmap intro. Open with one concrete hook instead of announcing what follows.
- Replace stock transitions with specifics about the reader or the task.
- Run a separate voice pass after the factual edit, with explicit checks for rhythm and structure.
Why the writer can’t judge its own voice
When the same model wrote and judged, it gave its own prose a clean bill of health and missed the robotic patterns. A different reviewer model flagged them consistently. We also found that checking accuracy was far easier than scoring how human prose feels. Accuracy and voice behaved independently, so voice became its own pass/fail gate with its own reviewer.
Should you trust AI-detector scores?
Use them as a signal, not a target. Detectors are noisy. We used the percentages to find specific tells and reduce them, not to chase a score of zero.
When a template is still the right call
For scannability, compliance or search-driven formats, a consistent structure helps readers and editors. Keep the template, but allow controlled variation inside it: different sentence lengths, alternative subheadings, optional slots for an example.
A quick edit checklist
- Does the intro announce a list? Rewrite it toward one concrete hook.
- Are most sentences medium length? Add a short one, or a longer one that explains.
- Are the transitions generic? Make them specific.
- Is every list three items? Change the sizes and formats.
FAQ
Won’t varying sentence length hurt readability?
Only if you overdo it. Aim for contrast: short sentences for impact, longer ones for explanation.
Is a separate voice review necessary?
In our experience, yes. Accuracy checks don’t catch robotic rhythm. An independent reviewer, human or a different model, catches the structural patterns.
What changed when we loosened the structure?
In our A/B tests the human-voice score went up and factual accuracy stayed the same.
Fix the shape before you fix the words.
Sources: ebizapple first-hand experiments and pipeline testing.
What research says about the AI writing style
Researchers have started to measure the AI voice instead of just describing it. The findings line up with what editors feel, but they also show something more uncomfortable: some of the tells are words after all, and they spread.
Certain words spike, and they are easy to track
A team led by Dmitry Kobak counted word frequencies in PubMed abstracts and compared recent years against a pre-LLM baseline. They found a surge of “excess” style words after ChatGPT arrived, and concluded that LLMs had an impact on biomedical writing surpassing the effect of major world events such as the Covid pandemic. Their method needs no labeled set of AI texts. It simply looks for words whose use jumped faster than anything in the record before.
Why those particular words? Tom Juzek and Zina Ward identified 21 “focal words” that ChatGPT-3.5 overuses in scientific writing, “delve” among them. They tested the obvious suspects and found no evidence that architecture, algorithm choices or training data cause the overuse. Reinforcement learning from human feedback remained a candidate, though a lack of transparency around model development made it hard to prove.
Less variety than a human writer
Vocabulary range is the second signal. A 2025 study compared texts from several ChatGPT versions with essays by 240 native and non-native English writers. The LLM texts differed significantly from the human texts on every lexical diversity measure, and the newer models were, if anything, less human-like than the older ones. Better models do not automatically sound more like people.
Predictability is the hidden thread
Many detectors lean on one property: generated text tends to be the text a model finds likely. DetectGPT, presented at ICML 2023, scores a passage using only log probabilities computed by the model of interest plus small rewrites of the passage. Uniform sentence length and stock transitions are the human-visible side of that same predictability. They are what “likely” reads like.
Why AI detectors are not a reliable judge
If the style is measurable, why not let software decide? Because the measurements are statistical, and a score about a population says little about one paragraph.
- OpenAI withdrew its own detector. At launch it correctly flagged 26% of AI-written text and wrongly labeled human text as AI 9% of the time. On July 20, 2023 the company noted the tool was no longer available due to its low rate of accuracy.
- Independent testing found the same. A team led by Debora Weber-Wulff tested 12 public tools plus two commercial systems and concluded they were neither accurate nor reliable, with a bias toward calling text human. Machine translation and light obfuscation made results worse.
- Non-native writers pay the price. Researchers found that widely used detectors consistently misclassified writing by non-native English speakers as AI-generated, while native samples were judged correctly. Simple prompting both reduced that bias and bypassed the detectors, and the authors cautioned against using them in evaluative or educational settings.
- Small error rates scale badly. Vanderbilt University turned off Turnitin’s AI detector in August 2023. Its reasoning: Turnitin claimed a 1% false positive rate, and with about 75,000 papers submitted in 2022, that could mean around 750 student papers wrongly labeled.
The non-native finding matters for anyone editing for voice. Plain, careful prose with a limited vocabulary can look “too predictable” to a detector. Polishing a draft until a tool goes quiet can push you toward ornate wording that serves no reader.
How the research connects to our own findings
Our pipeline tests pointed at structure first: templates, metronome sentences, three-item lists. The published work does not contradict that. It adds a layer. Overused vocabulary is real and measurable, so a voice pass should look at word choice too, just after it fixes the shape.
The detector studies also explain why we treated scores as a signal rather than a target. A tool that misses most AI text and still flags some human text cannot certify a voice. It can, at most, point at a paragraph worth rereading.
One finding of ours has no direct counterpart in the papers above: the writing model gave its own prose a clean bill of health, while a different reviewer flagged the patterns. The research does support a related point. If even purpose-built detectors are unreliable, a model grading its own output deserves less trust, not more.
A revision checklist grounded in the research
- Fix structure first. Vary sentence length, break the three-item habit, cut the roadmap intro.
- Search for the usual suspects. Run a find for “delve” and for any word the draft repeats more than you would in speech. Keep it only if it is the precise word.
- Widen the vocabulary on purpose. Swap repeated abstractions for the concrete noun: the product, the person, the number.
- Prefer specifics over smoothness. A detail the model could not have guessed reads as human, because it is.
- Read one paragraph aloud. If every sentence lands with the same weight, rewrite two of them.
- Treat detector output as a pointer. Never as a verdict, and never on someone else’s work without other evidence.
Is “delve” really a giveaway?
On its own, no. It is a frequency signal: researchers found it and similar words overused by ChatGPT-3.5 in scientific writing tasks, but humans use them too. One word proves nothing. A cluster of them, inside rigid structure, is a reason to revise.
Can an AI detector prove a text was written by AI?
No. OpenAI withdrew its classifier over accuracy, and an independent evaluation of 14 tools found them neither accurate nor reliable. Use scores to find passages to reread, not to accuse a writer.
Why do detectors flag non-native English writers?
Detectors reward unpredictable wording, and writers with a smaller working vocabulary often choose common, predictable words. That is how one study found detectors biased against non-native English writers. Simpler prose is not machine prose.