Entrepreneur reported on August 27, 2026, that more than one million LinkedIn members used the platform's new “Seems like AI slop” feedback option during its first two weeks. The feature is notable because LinkedIn is asking users to judge value and authenticity, not merely whether software helped produce a post.
How the feedback works
LinkedIn's help page says members can open the menu on a post or comment and select “Seems like AI slop.” The signal tells LinkedIn that the content did not feel valuable. If a post receives enough community feedback, its author may see a tip in analytics; LinkedIn says that tip is not a takedown or policy decision.
Entrepreneur's report attributes the million-use figure to LinkedIn chief product officer Hari Srinivasan. It also reports his statement that members were seeing 40% fewer views of content LinkedIn classifies as AI slop. Those are company-reported product metrics, not an independent audit of content quality.
LinkedIn is targeting low value, not every AI tool
LinkedIn defines AI slop as low-effort, likely AI-generated material that sounds polished but lacks a distinct perspective or substance. Its guidance explicitly allows AI assistance when the author reviews the result and contributes real experience or expertise.
That distinction matters because automated authorship detection is uncertain and writing style is not proof of how text was made. LinkedIn's June product note says its systems look for generic, repetitive or automated-at-scale content and reduce its distribution. Community feedback adds another signal, but LinkedIn has not published enough detail to calculate how one click changes ranking or how abuse is prevented.
What creators and platforms should learn
For creators, the practical response is editorial: verify claims, remove generic repetition, add first-hand context and take responsibility for the final post. Merely varying wording to evade a detector would miss the product's stated quality goal.
For platforms, the harder problem is calibration. A useful system must resist coordinated reporting, account for language and cultural differences, and avoid penalizing concise or formulaic professional writing that is still accurate. Transparency about appeals and aggregate error rates will matter as the signal influences distribution.
Sources and methodology
This article uses Entrepreneur's August 27 report, LinkedIn's feature documentation and LinkedIn's June product announcement. Company metrics are labeled as such, and the analysis does not treat user feedback as proof that a post was generated by AI.