Shruti KaushikAcross 500 LinkedIn posts written by one AI model, the mean checker score was 79.9 out of 100. 51.8% of posts ended on a question to the reader, 50% ended in a hashtag block and 13% quoted a percentage with no source. Asking for the post to "sound human" raised the mean score from 62.4 to 94.2.
Source and method
All 30 statistics come from our study of 500 AI-generated LinkedIn posts. One AI model wrote the posts on 9 October 2026: 25 common prompts, four prompt styles (plain, "make it engaging", "make it sound human", "write it in my voice"), five runs each. Every post was scored with the rules of the free LinkedIn post checker as they stood before the emoji fix made later that day, so the checker's closing-question rule missed questions followed by an emoji. Percentages are shares of all 500 posts unless a style is named, in which case they are shares of that style's 125 posts. The numbers describe this one model under these prompts, not AI writing in general; see the study's limitations. The full CSV holds every post and score.
Overall scores
- The mean checker score across all 500 AI-generated LinkedIn posts was 79.9 out of 100.Source: study, what we found
- The median score was 82 out of 100.Source: study, what we found
- 28.8% of the posts had no flagged patterns at all (144 of 500).Source: study, what we found
- 44.2% of posts scored under 80.Source: study, what we found
- 6.6% of posts scored under 50.Source: study, what we found
- 209 of the 500 posts (41.8%) scored 90 or higher.Source: study, what we found
- The average post carried 1.45 flagged patterns, and no post carried more than 5.Source: study, what we found
- 37.6% of posts got the verdict "Readers will notice some of this" and 6.6% got "Likely to read as AI-written".Source: study, what we found
By prompt style
- A plain request with no style instruction produced a mean score of 62.4.Source: study, score by prompt style
- Adding "make it sound human" produced a mean score of 94.2, a gap of 31.8 points over the plain request.Source: study, score by prompt style
- "Write it in my voice", with no voice samples supplied, averaged 87.5.Source: study, score by prompt style
- "Make it engaging" averaged 75.5.Source: study, score by prompt style
- 69.6% of "sound human" posts had no flags, against 0% of plain posts.Source: study, score by prompt style
- 95.2% of plain-request posts scored under 80, and 20% scored under 50.Source: study, score by prompt style
By pattern
- A block of three or more hashtags was the most common flag, on 50% of posts: 100% of plain and engaging posts and 0% of human and voice posts.Source: study, what we found
- 13% of posts quoted a percentage with no named source; of the 66 posts that used a percentage, 65 gave no source.Source: study, what we found
- "Make it engaging" posts carried an unsourced percentage 26.4% of the time, against 1.6% for "sound human".Source: study, what we found
- The invented-story rule fired on 17% of posts, most often for "write it in my voice" (23.2%).Source: study, what we found
- Customer-story posts were flagged as invented stories 55% of the time, the highest of the 25 topics.Source: study, score by topic
- The cliche-phrasing rule (for lines such as "In today's fast-paced world") fired on only 3.4% of posts, rising to 11.2% for plain requests.Source: study, what we found
- Of the checker's 15 writing patterns, 4 never fired: em dash overuse, "not just X, it's Y", markdown asterisks and the universal-lesson ending (the posts were written under a no-em-dash rule).Source: study, what we found
Openers and endings
- Counting the text directly, 51.8% of posts ended on a question to the reader; the checker rule flagged 28.2%.Source: study, closing questions by style
- 93.6% of plain posts and 91.2% of engaging posts ended on a question, against 1.6% of "sound human" posts.Source: study, closing questions by style
- Of the 118 posts whose closing question the rule missed, 114 had an emoji after the question mark.Source: study, closing questions by style
- 25% of first lines contained an emoji, and 25.8% of posts contained at least one emoji anywhere.Source: study, what we found
- 22% of posts opened with "I", including "I'm" and "I've".Source: study, what we found
- The most repeated opening was "Let me tell", which started 19 posts.Source: study, what we found
Rhythm and length
- The flat-rhythm rule fired on 23.2% of posts, and on 55.2% of "make it engaging" posts.Source: study, what we found
- Sentence length varied least in engaging posts (standard deviation 3.45 words) and most in "sound human" posts (5.56 words).Source: study, what we found
- The average post was 138 words long; "write it in my voice" posts were the longest at 156 words.Source: study, how the study was run
Cite this page
Suggested citation:
Kaushik, S. (2026, 9 October). LinkedIn AI writing statistics 2026: 30 numbers from 500 AI-generated posts. Klype. https://klype.ai/blog/linkedin-ai-writing-statistics-2026.html
To cite the underlying data, link to the original study, which includes the method, limitations and the downloadable dataset.
Common questions
How much AI-generated LinkedIn content gets flagged by a post checker?
In this dataset of 500 posts from one AI model, 71.2% had at least one flagged pattern and the mean score was 79.9 out of 100. Results from other models will differ.
Where do these LinkedIn AI writing statistics come from?
Every number on this page is computed from the results of Klype's study of 500 LinkedIn posts written by one AI model on 9 October 2026, scored with the rules of the free post checker as they stood before the emoji fix. The full study and CSV dataset are linked on the page.
Can I quote these statistics?
Yes. Use the suggested citation on this page and link to it or to the original study, and mention that the posts came from a single AI model.