LinkedIn Added a “Seems Like AI Slop” Button. Marketers Should Pay Attention.

I keep thinking about a button LinkedIn recently added to its reporting menu: “Seems like AI slop.”

It is a strange phrase to see inside a product used by more than a billion professionals. Until recently, “AI slop” was mostly internet shorthand for generic posts, images, and articles that clearly took seconds to make and even less time to review. Now LinkedIn is reportedly letting people use the label to hide posts and improve its detection systems.

I laughed when I first saw it. Then I realized how bad a sign it is for marketers.

People have always ignored boring content. The difference now is that they have a name for it, and platforms are beginning to give them a button for it.

A recent X query for “AI content quality,” “AI content QA,” and “AI slop” returned roughly 38,000 posts in seven days. The results included plenty of noise, as any social conversation does, but the complaint kept showing up in different forms: AI made it easier to produce things that look finished before anyone has done the work required to finish them.

For the last few years, marketers have talked about AI mostly in terms of speed. We can draft an article faster, generate more campaign concepts, create more social variants, and get more work through the day.

I understand the appeal. The first time a model turns a blank page into a coherent draft in seconds, it feels like a breakthrough. Sometimes it is.

The problem starts when we mistake a complete-looking draft for completed work.

An article can have a headline, introduction, subheads, examples, and a conclusion while still saying almost nothing. A campaign can include every required asset without having a clear point of view. A landing page can be perfectly coherent and completely forgettable.

When people call something AI slop, I do not think they are always identifying AI accurately. They are reacting to work that feels unattended. The language is generic. The examples could belong to any company. The claims sound bigger than the evidence. No one appears to have made a difficult choice.

The problem is not visible AI. It is invisible judgment.

A recent discussion among developers described a version of this problem I recognized immediately. AI could generate the first 70 percent of a frontend experience quickly. The remaining work, including responsiveness, usability, consistency, and polish, still took hours.

Marketing works the same way.

AI can organize a brief, scaffold a campaign, draft copy, suggest variants, and assemble a first version. That saves real time. It removes blank-page work and reduces the number of times the same information has to be reformatted for different teams.

Then someone has to decide whether the idea is specific to the audience, whether the copy sounds like the company, whether the proof is current, and whether the experience gives the customer anything useful. Someone has to test the links, read the page in context, and catch the sentence that is technically fine but obviously wrong for the moment.

That work can take longer than the initial draft. It is also where most of the value is created.

The speed of generation has made normal editing feel slow by comparison. I think that is distorting our expectations. If AI gives us a faster start, we should have more time to make the ending better. We should not lower the standard because the first draft arrived quickly.

The LinkedIn button matters because it turns a quality problem into a distribution problem.

If people can report posts using “Seems like AI slop,” every generic piece of content can do more than waste an impression. It can tell the platform that people do not want to see more from the account that published it.

The recent debate about Google and AI-generated content points to the same practical lesson. One widely discussed claim suggested Google could identify AI content through watermarking and penalize it. SEO practitioners challenged that claim and returned to the guidance Google has repeated for years: usefulness, expertise, originality, and scaled abuse matter more than the production method.

That is a much simpler standard to work with. Marketers do not need to disguise the fact that AI helped. We need to make sure the work earns the customer’s attention.

If the article is useful and original, readers will not care how the first draft began. If it repeats what everyone else has already said, no disclosure or clever prompt will save it.

The most useful example I found came from academic publishing.

The American Economic Association and the Econometric Society reportedly partnered with Refine to check proofs, derivations, inconsistencies, and clarity in conditionally accepted papers. The tool handles technical diligence. Editors and researchers still judge the paper’s contribution.

That division makes sense to me.

Marketing teams can use AI to check whether claims have support, links work, required sections are present, terminology is consistent, and content follows brand, accessibility, or channel requirements. AI can compare a page with the original brief and show a reviewer where the two have drifted apart.

These checks do not make the final decision. They clear away predictable mistakes so the reviewer can focus on the decisions that require experience and taste.

We have spent so much time asking AI to create more work that we have underused it as a way to inspect the work already in front of us. The same technology producing the first draft can challenge the claims, look for gaps, and check whether the finished asset matches the assignment.

That still leaves a person responsible for the result. A system can confirm that a claim has a source. A marketer has to decide whether the claim deserves to lead the page. A system can check the brand guide. A marketer has to know when the approved language sounds lifeless. A system can confirm that every field is filled in. A marketer has to decide whether the experience respects the customer’s time.

1. Stop counting drafts as output

Words generated, variants created, and assets started are easy to count. They can make an AI program look productive while the review queue grows and very little reaches customers.

I would rather measure how much work ships, how much passes review without major rework, and whether customers respond. If AI creates more drafts but does not improve those outcomes, it has moved the bottleneck instead of removing it.

2. Name the person who owns the final version

Every AI-assisted asset needs someone who is willing to put their name behind it.

That person is responsible for the accuracy, specificity, usefulness, and quality of the finished work. They are doing more than glancing at a draft before it moves to the next step.

When ownership is vague, “the model wrote it” becomes an easy explanation for work no one fully reviewed.

3. Build checks into production

Quality cannot depend on someone remembering a checklist after the team has already created fifty variants.

Source verification, brand review, accessibility, compliance, link validation, and channel requirements need to stay attached to the work from the beginning. Automating the predictable checks gives the final reviewer more time for the decisions a checklist cannot make.

4. Use the saved time to develop an opinion

This is the part I care about most.

If AI gives marketers time back, we should spend some of it finding something worth saying. Talk to customers. Listen to sales calls. Study where people hesitate. Pay attention to the language competitors repeat until it loses all meaning. Form an opinion from experience.

Better prompting will not fix a weak point of view. Better inputs from a marketer who has been paying attention might.

LinkedIn’s button is a small feature, but it reflects a real change in how people judge content. Audiences have learned to recognize work that feels mass-produced and unattended. Search platforms are under pressure to keep low-value pages out of results. Developers are finding that fast scaffolding does not remove the need for polish. Publishers are testing AI as a technical reviewer while keeping people responsible for the ideas.

Generation is abundant now. Care is not.

That should be good news for marketers. Our value was never our ability to fill every box in a template. It comes from knowing the customer, finding the insight, making a choice, and carrying that choice into the finished experience.

AI can help with a lot of the work along the way. It can start the draft, assemble the pieces, and catch mistakes before they ship.

It cannot care how the work lands. That part is still ours.

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