Does this LinkedIn post read as AI?
Paste a post and get a 0 to 100 “reads human” score, a plain verdict, and the phrases, patterns and punctuation that make it sound generated. The same judge we ran on 134,000 real LinkedIn posts.
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A 0 to 100 “reads human” score, a verdict, and the phrases and patterns that make the post sound generated.
Why sounding like AI costs you on LinkedIn
In our study of 134,000 posts, posts that read as AI got about 22% less engagement than the same author's human-sounding posts. That is a within-author comparison: the same people, writing on the same account, did worse when the post read as generated. See the full study.
Readers do not need a detector to feel it. Evenly polished sentences, stock contrasts and a question answered in one word all signal “template”, and people scroll past templates. The fix is rarely to stop using AI. It is to put back what only you know.
What the checker looks for
The score comes from a language model asked one question: does this sound like a real person typed it? On top of that, simple rules flag these patterns so you know which lines to edit. They are patterns, not proof.
- Em dashes. Models reach for the em dash constantly. On LinkedIn it has become the best known AI giveaway, fair or not.
- "It's not X, it's Y" contrasts. The set-up-and-flip contrast is a signature model move, especially more than once in a post.
- "Here's the thing" style set-ups. Stock transitions like "Here's the thing" or "Here's why" announce a point instead of making it.
- Stock openers and phrases. Generic openers such as "In today's fast-paced world" are among the most over-used model phrases.
- Words models over-use. Words like "delve", "tapestry" and "seamless" appear far more often in model text than in how people talk.
- Runs of short punchy fragments. Three or more very short sentences in a row ("They listen. They learn. They lead.") read as a template rhythm.
- Rhetorical question, instant answer. Asking a question and answering it in a word or two ("The secret? Consistency.") is a very common generated pattern.
- Emoji bullet lists. Several lines in a row that start with an emoji (checkmarks, rockets, arrows) is a typical generated layout.
- Engagement-bait closer. Ending on "Agree?" or "Thoughts?" is a stock sign-off that models add by default.
An honest caveat
This is a judgement of style, not proof of authorship. Nobody can reliably tell from text alone whether a model wrote it, and you should not use this tool to accuse anyone of anything. Use it on your own drafts, to find the lines that make you sound like everyone else.
Fixing AI-sounding posts by hand gets old. LigoSocial writes in your voice from the start.
LigoSocial learns how you write from the posts you have already published on LinkedIn, then drafts new posts and comments that sound like you, not like a model. You approve what goes out.
See how LiGo learns to write like you
The AI detector stays free and needs no signup. The account is for the LigoSocial product.
Common questions
- How does the LinkedIn AI Detector work?
- A language model reads your post the way a LinkedIn user would and scores how much it sounds like a real person typed it, on a 0 to 100 scale. Separately, simple rules in your browser flag the common AI patterns in the text, such as em dashes, "it's not X, it's Y" contrasts and runs of short punchy fragments.
- Can it prove a post was written by ChatGPT or another AI?
- No. It judges style, not authorship. A person can write in a polished, formulaic way and a model can be prompted to sound casual. Treat the score as a read on how the post will come across, not as evidence of who or what wrote it.
- Do you store the posts I paste?
- No. The text is sent to our server to be scored and then discarded. We log only its length and the score, never the text itself. The pattern list is computed in your browser.
- Why does it matter if a LinkedIn post reads as AI?
- In our study of 134,000 posts, posts that read as AI got about 22% less engagement than the same author's human-sounding posts. The comparison is within each author, so it is not explained by bigger accounts writing differently.
- What do the verdicts mean?
- Reads human means the post has the uneven rhythm, specific details and voice people bring. Mixed means some of it sounds generated. Reads as AI means it is evenly polished, abstract and formulaic, the way model output tends to be. The thresholds come from our study of real LinkedIn posts.
- How many posts can I check?
- The free LigoSocial tools share a small daily allowance per visitor. A check that fails on our side does not count against it.
- How do I make an AI-assisted post sound more human?
- Add one concrete detail only you know (a number, a name, what actually happened), cut the stock set-ups and contrasts the checker flags, break the even rhythm with a longer sentence, and drop the engagement-bait closer. Then check it again.