LinkedIn is testing a new reporting option that lets users flag posts as "seems like AI slop," the company's Chief Product Officer Hari Srinivasan announced in a post on the platform. The flag privately notifies the poster via LinkedIn's analytics dashboard that others feel the post may have come off as inauthentic or heavily AI-generated, and flagged posts will see reduced algorithmic reach (Engadget).

The reporting button will also serve as a training signal for LinkedIn's AI-detection systems (CNET). LinkedIn has reportedly improved those detection systems recently, resulting in fewer AI-generated posts being recommended in user feeds. The company previously made an update to reduce the reach of AI-generated content on the platform (Engadget).

In tandem with the reporting tool, LinkedIn is removing its built-in AI writing tools, which allowed users to enhance posts and messages using large language models. The company is replacing them with a proofreading feature that checks words but does not alter the user's voice (Engadget). The removal of the LLM-powered writing assistance is documented in LinkedIn's help pages (LinkedIn Help).

The scale of the problem the company is addressing is substantial. AI detection company Pangram found that LinkedIn was the most AI-saturated social media platform it measured, with more than 40% of longform posts flagged as fully AI-generated (Engadget). Within the last few months, LinkedIn caught hundreds of thousands of automated comments and blocked billions of attempts at automated posting on the platform (Engadget).

The introduction of the "seems like AI slop" button follows prior reporting by 404 Media on how LinkedIn had become saturated with AI-generated content (404 Media).

The decision to remove its own AI writing tools while introducing a crowdsourced slop-detection mechanism is a notable reversal for LinkedIn. The platform had integrated generative AI writing assistance directly into its composer, effectively lowering the barrier to producing the kind of content it is now asking users to police. The proofreading-only replacement suggests the company has concluded that LLM-assisted post generation, at least in the form it shipped, contributed to the authenticity problem rather than solving a user need.

The "seems like AI slop" label is deliberately phrased as a perception signal rather than a definitive classification. A user flagging a post is not making an adjudicated claim about provenance; the poster receives a private, soft notification through their analytics dashboard. But the flag carries a hard consequence in the form of reduced algorithmic reach. That combination, a soft signal with a hard penalty, is worth examining. It means the threshold for a post's distribution can be influenced by other users' subjective impressions of authenticity, and those impressions feed back into LinkedIn's automated detection models as training data. The system is designed to improve over time, but in its early phase it relies on the judgment of flaggers whose own accuracy at identifying AI-generated text is, at best, inconsistent.

Pangram's finding that over 40% of longform LinkedIn posts are fully AI-generated gives context for the urgency. That figure, if accurate, means a user scrolling a feed of professional content is encountering machine-written prose at a rate that would have been unthinkable two years ago. LinkedIn's enforcement numbers, hundreds of thousands of automated comments caught and billions of automated posting attempts blocked, describe a platform under sustained programmatic pressure from bots and content farms, not merely individual users leaning on ChatGPT for their thought leadership posts.

For technology professionals who use LinkedIn as a primary professional networking surface, the practical implications are immediate. Posts that read as formulaic, overly polished, or templated, regardless of whether generative AI was actually used in their creation, now carry the risk of both social friction and reduced distribution. The reporting tool is in testing, and LinkedIn has not specified how many flags trigger a reach reduction or whether any appeal mechanism exists for posters who believe they were flagged incorrectly.