Navigating The LinkedIn AI Slop Measures: What Users Need to Know

LinkedIn recently released a feature that enables users to 'report' AI slop when they encounter it on the platform. It's raised a fair few questions from people in my feed, and I've summarised my understanding of the move already. However, in this piece, I wanted to go into a little more detail, just in case you're after this context before deciding what it means for your own efforts.

Worth noting that a lot of what I'm saying here is based on the comments made by one of LinkedIn's product directors, and I've embedded his original video at the bottom of this post so you can refer to that and hear it from the horse's mouth.

So, What Does LinkedIn AI Slop Look Like?

There is a subtle distinction in LinkedIn’s recent updates to the platform that a lot of people seem to be missing, and it matters if you are trying to build a ineffective presence there.

The ability to ‘report’ content as ‘AI slop’ - it’s literally badged ‘Seems like AI slop’ - via the three dots in the corner of a post has sent a mild tremor through the user base. But before anyone panics that using a digital tool to fix a dodgy sentence structure is going to get them banished to the algorithmic wilderness, it pays to look at what is actually happening in the engine room.

So, first things first, the problem being addressed is not the use of artificial intelligence to assist your writing. As Sam Corrao Clanon, a product director at LinkedIn, laid out in a recent Q and A post, the target is purely empty output.

"We internally identify or define AI slop as content that is potentially sophisticated or polished in its presentation, but lacks substance. So, it doesn't have any particular experience, perspective, or insight, but is sort of just empty text that's posted to take up space, garner attention without effort on the other side. It's not anything that uses AI in any capacity. I want to be clear about that. There's a lot of people who use AI to refine their own thinking and insights...All that sort of stuff? Totally fine."

That is a crucial distinction. The platform is tackling the relentless dumping of polished text that looks like a thoughtful update on the surface, but amounts to precisely nothing underneath. And I think that should be a cause for optimism among those of us contributing useful stuff to LinkedIn on a regular basis.

How the Algorithm Handles Low Substance Content

To understand how to navigate this, we have to look at how the feature actually operates.

When a user flags a post as LinkedIn AI slop, it doesn’t trigger a human moderation review or slap a strike on your account, nor does it immediately downrank your profile. Instead, it functions primarily as a disinterest for each individual viewer. It simply tells the system that this particular user wants to see less of that specific style of content.

At scale, however, those signals feed directly into the training data for LinkedIn’s backend systems, so it learns to identify the patterns of generic, low-effort fluff and filters it out over time.

And what about the threat of coordinated action, or so-called ‘spike pods’ trying to sabotage a creator by mass reporting their work? Sam addressed that concern directly during the video.

"So, while you could spike pod someone's post and then that post would go into the training data for like a generic content classifier, that training data includes millions of other posts. So, it might mean down the road that posts that look like that are incrementally deranked, but it wouldn't be a way to actually brigade another creator's content per se."

The reality of the engine room is that your individual post is a drop in an ocean of data. A sudden spike in flags might see that content ingested into a broad dataset, but it is not going to serve as a direct signal to kill off a specific profile overnight.

AI Assistance vs AI Generation for Your Content

Which brings us to the core issue for anyone creating content.

The system is focused entirely on the presence of real substance, not the mechanical process of how the characters appeared on your screen. As Sam noted later in his update, the platform is looking at text without substance, and using AI for basic polish is explicitly clear of scrutiny.

"So two things: one, yeah, the feature again, it's not a report; it's a disinterest signal at the viewer level. Two, we then use those to train our classifiers, but the classifier is really looking at things that have text without substance…And obviously, you know, we can't be prescriptive in terms of how people indicate their own disinterest. But likewise, I wouldn't expect fixing grammar to be something that incites those mutes."

The trap lies in outsourcing your actual thinking.

People are far more likely to mark content as LinkedIn AI slop if it relies on sweeping platitudes, lacks concrete real-world examples, or uses five paragraphs to say something that could have been summarised in half a sentence. If a reader could skim your post and lose nothing by having never seen it, you are in the danger zone regardless of whether a human or a machine wrote it.

Best Practices to Avoid the AI Slop Trap

So, if you want to guard against these measures and build actual credibility, there are three practical filters your content needs to pass.

First, ground everything in specific, lived experience. Bring in the messy reality of the work. Share what actually happened when a campaign launched, why a particular strategy failed, or what surprised you during a client rollout. That hard-earned insight is the one thing a generic language model cannot replicate.

Second, ensure every piece has a clear, singular point. If you cannot articulate what the reader should think or do differently after reading your update, it is just noise.

And third, use technology strictly as an editor, never as the primary author. Let the tool polish your language, but keep direct ownership of the ideas, the narrative arc, and the strategic intent.

Ultimately, this platform shift is good news for people who actually know their stuff. As the algorithms get better at sifting out generic noise, genuine domain expertise and clear perspective will naturally stand out more.

And if you want to figure out how you, your team, or your leaders can do that, drop me a line at dan@settsocial.com and we can arrange a chat.

And here's the original post from Sam that a lot of this insight is based on: