The AI Slop Backlash: Why Perspective Beats Production Volume
- Cassidy Consulting
- 2 hours ago
- 7 min read
LinkedIn has introduced a new way for users to respond to generic, automated, or inauthentic content: they can report it as “AI slop.”
The platform is also improving systems designed to identify low-quality content, reduce its visibility in recommendations, and privately notify users when their posts may appear overly dependent on artificial intelligence. LinkedIn is even replacing its own AI-powered “enhance your post” feature with a proofreading tool intended to preserve the author’s original voice, according to TechCrunch’s reporting on the announcement.
This should get the attention of every organization using AI to increase its content output.
LinkedIn is not rejecting artificial intelligence. It has explicitly acknowledged that AI can be useful for refining language. The platform is targeting content that may sound polished but lacks original perspective, context, expertise, or substance.
That distinction matters.
AI slop is low-value content created or heavily shaped by artificial intelligence that lacks meaningful human perspective, originality, or intent.
The problem is not simply that AI helped create the content.
The problem is that the content gives audiences no reason to believe a person meaningfully contributed anything.
Why Is LinkedIn Cracking Down on AI Slop?
LinkedIn depends on people believing they are interacting with real professionals sharing real expertise.
That value begins to erode when feeds become filled with interchangeable leadership advice, automated comments, recycled observations, generic personal stories, and superficial thought leadership.
In May 2026, LinkedIn described AI slop as low-effort content that may appear polished but offers little unique perspective or substance. The company said posts that appear AI-generated and lack a clear point of view would be less likely to receive widespread distribution beyond the author’s immediate network.
LinkedIn also reported that its initial system was correctly identifying generic content 94% of the time. Its systems are designed to distinguish content that adds perspective, context, or expertise from content that merely repeats familiar ideas. LinkedIn explains the approach in its article, “Keeping Conversations Real on LinkedIn.”
The message for communicators is clear: producing more content will not necessarily result in more reach, authority, or trust.
When content offers little value, greater volume may only make the problem more visible.
What Does AI Slop Look Like?
There is no single phrase, punctuation choice, or writing style that proves something was created by AI.
AI slop is better understood through the experience it creates for the audience.
It often feels familiar before it feels useful. It presents predictable observations as major insights and uses professional-sounding language without offering a clear point of view.
Common characteristics include:
Advice that sounds reasonable but is too general to apply
Personal stories with few credible or specific details
Long introductions that delay an obvious conclusion
Familiar ideas repeated in slightly different language
Manufactured vulnerability or inspiration
Confident claims unsupported by evidence or experience
Comments that merely summarize the original post
Content that could be published by almost anyone under almost any name
A person can write generic content without using artificial intelligence. AI can also help create original, valuable work when it is directed by someone with genuine knowledge and intent.
The issue is not simply whether AI was used.
It is whether the final product contains meaningful authorship.
Why Perspective Matters More Now
For years, organizations treated polished content as a signal of effort and professionalism.
AI has changed that equation.
Clear grammar, confident language, attractive visuals, and professional formatting can now be produced almost instantly. Those qualities still matter, but they no longer prove that the content contains expertise, creativity, or original thought.
As production becomes easier, the elements that remain difficult to automate become more valuable:
Firsthand experience
Informed judgment
Original research
Specific examples
Institutional knowledge
Credible opinions
Emotional awareness
Accountability for what is published
These are the elements that make content worth consuming.
AI can organize notes, refine language, summarize research, identify patterns, test structures, and adapt content for different channels. It cannot independently know what an executive genuinely believes, which experiences shaped an organization, what customers routinely misunderstand, or which lessons were learned through difficult work.
Those insights have to come from people.
How Can Organizations Use AI Without Creating Slop?
Organizations do not need to stop using AI. They need to give it the right role.
Begin with real human source material
Strong AI-assisted content usually begins with something that already contains substance.
That might include:
An interview with an executive or employee
Notes from a presentation or meeting
Original data
Customer questions
A specific success or failure
A firsthand observation
A clearly stated opinion
A podcast or video transcript
AI can help organize, edit, condense, or repurpose that material. The underlying experience and perspective should come from the person or organization publishing it.
There is a significant difference between asking AI to “write a post about leadership” and asking an experienced leader to explain a difficult decision before using AI to refine the response.
The first process asks technology to invent insight.
The second uses technology to communicate insight more effectively.
Require specificity
Specificity is one of the strongest defenses against generic content.
Consider the difference between these two statements:
Strong leaders listen to their teams and remain open to change.
We entered the project believing customers cared most about speed. Three listening sessions showed that predictability mattered more.
The first statement is reasonable but interchangeable.
The second introduces an actual experience, a changed assumption, and a useful lesson.
Before publishing, ask:
What happened?
What was difficult?
What changed?
What did we misunderstand?
What decision did we make?
What result followed?
What would we do differently?
Specific details do more than make content sound human. They make it useful.
Protect a recognizable voice
Human review should involve more than checking grammar and accuracy.
The reviewer should ask whether the content sounds like the person or organization whose name appears above it.
Would this executive actually use these words? Does the organization normally speak this formally? Does the piece reflect its values, experience, and way of interpreting events?
AI often gravitates toward balanced, orderly, broadly agreeable language. That can improve clarity, but it can also remove the qualities that make a voice recognizable.
Editing should clarify the author’s voice rather than replace it.
Give experts permission to express judgment
Many organizations produce generic content because their approval processes remove anything distinctive.
A strong claim becomes a safe observation. A specific example becomes a broad summary.
A moment of uncertainty becomes a polished success story.
Each revision may reduce risk, but it can also reduce value.
Effective thought leadership does not require unnecessary controversy. It does require a perspective that the audience could not have generated on its own.
Organizations should identify where executives and subject-matter experts have permission to share informed opinions, explain tradeoffs, acknowledge uncertainty, and discuss what they have learned.
Do not confuse capacity with strategy
AI makes it possible to publish more frequently. That does not mean an organization has enough meaningful insight to justify the additional content.
Before increasing output, organizations should consider whether they have enough:
Expertise to share
Stories to tell
Evidence to support their claims
Audience questions to answer
Employees willing to contribute
Editorial capacity to protect quality
A content strategy should be based on the organization’s supply of useful knowledge, not the production capacity of its software.
A Five-Question Test for AI-Assisted Content
Before publishing AI-assisted content, ask these five questions:
1. What did a person contribute?
Identify the experience, expertise, opinion, research, judgment, or creative direction that came from a human source.
2. Could this content belong to anyone?
Remove the organization’s name and imagine a competitor publishing the same piece. When nothing feels out of place, the content probably needs more specificity.
3. What will the audience learn?
The answer should be more valuable than a familiar reminder, inspirational phrase, or broad principle.
4. Can every claim be supported?
Verify statistics, quotations, names, dates, examples, and factual assertions. Confident language is not evidence.
5. Does it sound like us?
Read the content aloud. Consider whether it reflects the real voice of the organization and whether the named author would confidently defend it in a conversation.
When a draft cannot pass these questions, publishing more of it will not solve the problem.
Should Organizations Disclose Their Use of AI?
Not every use of AI requires the same level of disclosure.
Using AI to proofread a sentence is different from creating a realistic spokesperson, customer testimonial, photograph, voice, or video.
The Interactive Advertising Bureau recommends a risk-based approach. Its 2026 AI Transparency and Disclosure Framework says disclosure is most important when AI materially affects authenticity, identity, or representation in a way that could mislead an audience. Routine production assistance does not necessarily require labeling. The full framework is available through the IAB.
Organizations should consider disclosure when AI:
Creates a realistic person, voice, image, or event
Produces a testimonial or firsthand experience
Substantially changes the meaning of the content
Simulates an employee or customer perspective
Plays a role audiences would reasonably consider significant
Transparency cannot make weak content valuable, but it can help audiences understand what they are seeing.
The AI Slop Backlash Is an Opportunity
LinkedIn’s crackdown reflects a larger shift in how audiences evaluate content.
People are becoming less impressed by production itself. They want to know whether the content contains real experience, credible expertise, and a perspective worth considering.
That creates an opportunity.
When feeds are filled with familiar language, specificity stands out.
When automated comments repeat the obvious, a thoughtful response creates connection.
When everyone can produce polished thought leadership, demonstrated expertise becomes more valuable.
The organizations that benefit most from AI will not necessarily be those that publish the most. They will be the ones that use technology to make real knowledge clearer, more accessible, and more useful.
AI has made content easier to produce.
It has not made meaningful communication easier to fake.
Frequently Asked Questions About AI Slop
What is AI slop?
AI slop is low-value content created or heavily shaped by artificial intelligence that lacks meaningful human perspective, originality, expertise, or intent. It may sound polished while offering little substance.
Is LinkedIn banning AI-generated content?
No. LinkedIn has said that AI can be useful for refining language. It is reducing the reach of content that appears AI-generated and lacks a clear perspective, while also targeting automation and repetitive comments.
Does using AI reduce LinkedIn reach?
Using AI alone does not necessarily reduce reach. LinkedIn has indicated that generic content lacking perspective, context, or expertise may be less likely to receive distribution outside the author’s immediate network.
How can brands avoid AI slop?
Brands can avoid AI slop by starting with real human source material, using specific examples, protecting a recognizable voice, verifying factual claims, and using AI to refine expertise rather than invent it.
Build an AI Content Strategy Around Trust
Artificial intelligence can help organizations communicate more efficiently, but efficiency alone does not create authority.
Cassidy Consulting helps organizations develop content strategies, editorial processes, brand voices, and responsible AI practices that protect credibility while taking advantage of new technology.
Contact Cassidy Consulting to build an AI-assisted communications strategy that sounds like your organization and gives audiences something worth hearing.
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