LinkedIn's war on AI slop is not just a policy update—it is an admission that the platform lost control of its feed
Source: Matthias Bastian, The Decoder · 2026-05-20
The business world's Facebook is tackling a growing problem: more and more posts and comments on the platform are apparently being cranked out by AI with zero real value. Linkedin uses the now common term "AI slop:" content that looks polished on the surface but says nothing.
"When AI is overused, especially at scale and in an automated way, it dilutes the valuable insights that real human conversations can spark," writes Laura Lorenzetti, Vice President and Executive Editor at Linkedin Global Editorial, in a blog post.
AI as a writing aid is fine, she adds. But posts and comments need to sound like the person behind them. "Your posts and comments need to represent your voice and your perspectives," Lorenzetti says.
That this comes from Microsoft, of all companies, is pretty ironic. Linkedin's algorithm—like every social media algorithm—already tends to reward gimmicky, overly personal posts over stuff with actual substance.
But the irony cuts deeper. Microsoft itself has been actively pushing AI use on Linkedin. Just days ago, the company rolled out a new Copilot feature in the browser that promotes AI-assisted writing on the web. The demo platform? Linkedin, complete with a maximally generic post.
LinkedIn is betting on new technical systems trained with its in-house editorial team, Lorenzetti says. They pick up on what "appears to be generated by AI and lacks clear perspective" and should get better over time. The goal: tell apart articles that bring context, expertise, or a fresh take from those that read as generic or repetitive.
Posts aren't the only target. The measures also go after comments pumped out in bulk by automation tools with little or no human input, and replies that just parrot the original post without adding anything.
Content flagged as AI-generated and thin on opinion will get less reach. Instead of landing in other users' feeds, it'll mostly stay within the author's own network.
Early numbers look good from LinkedIn's side: in initial tests, the company says it correctly tagged generic content 94 percent of the time. Users already report seeing fewer junk posts. LinkedIn expects that to hold.
Still, the company hasn't shared any data that can be independently verified. How often legitimate posts get wrongly flagged as "AI slop" is anyone's guess.
Beyond content filtering, LinkedIn is also going after fake profiles. Bots and AI-generated fake accounts kill genuine engagement, Lorenzetti says. To help, LinkedIn is leaning on its verification system, claiming that over 100 million members are now verified.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
Are AI-generated articles systematically disadvantaged in search ranking and user engagement?- What happens to reach when a post gets flagged incorrectly?
- How much of new web content is AI-generated by mid-2025?
- Why are Technology and Software Development categories highest on Medium?
- How often does LinkedIn wrongly flag legitimate posts as AI-generated?
- How does LinkedIn's verification system affect what content appears in feeds?
- How does LinkedIn's approach differ from other AI content moderation systems?
- Do members who flag AI posts actually see fewer AI-generated posts afterward?
- Does flagging AI content change engagement and distribution like downvoting does?
- How much of LinkedIn's feed is genuinely AI-generated versus human-written content?
- What triggers LinkedIn's detection of inauthentic content from heavy AI use?
- How does LinkedIn's platform response address detected AI-generated content?
- Why do AI posts collect likes without generating replies on social media?
- How does LinkedIn's comment-versus-post AI split compare to Reddit's?
- How accurate is the detector labeling these posts?
- What percentage of workplace communication now contains AI-generated content?
- How accurate is OSM-Det when applied to real social media posts?
- Does improving detection accuracy change how slop accusations function socially?
- What signals do AI text detectors actually measure in their classification?
- Can AI-rewritten text still be detected as machine-modified?
- Can user feedback flags rival AI detector accuracy for identifying AI slop?