LinkedIn Adds a ‘Seems Like AI Slop’ Button as It Tries to Clean Up Its Feed

Updated July 30, 2026 6:53 pm

LinkedIn is rolling out a new way for members to flag posts they believe were produced by artificial intelligence and padded with low-value, formulaic text. The Microsoft-owned platform said on Thursday that users can now report content with a “seems like AI slop” option, a sign that one of the web’s biggest professional networks is moving more aggressively to curb synthetic filler in its feed.

The feature matters because it reflects a broader reckoning across the internet: platforms that once welcomed AI-assisted posting are now trying to separate useful automation from content that feels generic, manipulative or outright spammy. LinkedIn’s move comes as social apps, newsletter services and infrastructure companies all confront the same problem from different angles.

In public comments, LinkedIn chief product officer Hari Srinivasan said the company wants the service to remain a place where people can share genuine insight, expertise and perspective. He framed the fight against AI-generated filler as a top priority and said the company is combining user reports with technical detection tools, stronger anti-bot systems and product changes designed to preserve authentic writing.

Why LinkedIn is targeting AI-generated filler now

LinkedIn is responding to a growing volume of machine-written posts that users increasingly describe as repetitive, overly polished and lacking in real-world experience. The company’s new reporting button is part of a wider effort to reduce what many users call “AI slop” — content that may be technically coherent but feels mass-produced, derivative or deceptive.

That shift is not happening in isolation. Across the web, platforms are under pressure to prove they can still host human conversation at scale. The rise of consumer AI tools has made it easy to generate long posts, comments and newsletters in seconds, and that convenience has also made it easier for low-quality content to flood feeds.

LinkedIn, which positions itself as a professional network built around identity and reputation, is especially vulnerable to this trend. A post that feels inauthentic on a casual social platform may feel far more damaging in an environment where hiring, sales, networking and thought leadership are all tied to credibility.

How the new reporting button works

The new “seems like AI slop” option lets users signal when a post appears to have been written with AI in a way that feels low quality or inauthentic. Those reports are not just a moderation shortcut. LinkedIn says the feedback will help train and tune its detection systems, giving the company more data to identify slop-like content automatically in the future.

In practical terms, that means user reporting will feed into machine classifiers that can help reduce how much suspect content appears in recommendations, particularly from accounts outside a member’s immediate network.

LinkedIn has not said that every AI-assisted post will be demoted. The company appears to be drawing a distinction between legitimate editing assistance and content that looks like it was generated to game the feed. In other words, the focus is not simply on whether AI was used, but on whether the result feels authentic, useful and human.

Hari Srinivasan said LinkedIn is prioritizing the issue because people use the platform to connect with real professionals and exchange real ideas, not to sift through automated noise.

What LinkedIn is doing beyond the new button

The reporting tool is only one piece of a broader anti-spam and anti-automation push. LinkedIn says it is investing heavily in systems that block automated abuse before it reaches the feed, including massive volumes of bot-generated activity.

According to Srinivasan, the company now blocks hundreds of thousands of automated comment attempts every day. He added that over the past few months, LinkedIn has also stopped millions of other automation attempts, underscoring just how much artificial activity the platform is dealing with behind the scenes.

At the same time, LinkedIn is introducing new classifiers designed to spot AI slop and other low-value content more accurately. Those tools should influence the recommendations users see from outside their own networks, where the risk of generic viral posting is often highest.

The company is also adding a private feedback mechanism that alerts users when their own posts may be coming across as inauthentic because they rely too heavily on AI. LinkedIn says that feature is meant to help people improve the quality and tone of their writing, especially if they use AI to polish their own drafts rather than to generate a post from scratch.

How does LinkedIn want people to use AI?

LinkedIn appears to be drawing a line between assistance and substitution. The company still wants AI to be useful as an editor or productivity tool, but not as a replacement for the voice, judgment and specificity that make posts feel credible.

That philosophy is reflected in another product change: LinkedIn is retiring its “enhance your post” feature, which previously used AI to help write content. It is replacing that tool with one that proofreads instead of rewriting, suggesting the company is now more comfortable with AI correcting language than shaping a user’s perspective.

The change is subtle but important. Proofreading preserves the author’s own cadence and meaning, while full rewriting can flatten personality and produce the kind of polished, generic prose that many users now associate with AI spam.

How LinkedIn’s move fits a broader internet trend

LinkedIn is not the first platform to confront the backlash against synthetic content, and it is unlikely to be the last. The new button lands at a moment when multiple companies are building tools to identify, label or suppress AI-generated material as users become more vocal about the strain it places on online communities.

Newsletter platform Substack recently introduced an AI identification tool through a partnership with Pangram, allowing readers to better understand when text may have been machine-generated. Pangram also announced fresh funding this week to tackle the expanding problem of AI content flooding the internet.

Meanwhile, startup and community operators have learned that bots can overwhelm products faster than they can be moderated. Digg, for example, shut down its Reddit competitor in March after saying it could not keep up with the number of automated accounts and posts hitting the service.

Cloudflare has also warned that bot traffic is now exceeding human-generated requests on the web, a milestone that arrived sooner than the company had previously projected. Together, these examples show the same pressure from different directions: AI content is no longer just a quality issue, but a scale issue that affects trust, moderation costs and product design.

What is “AI slop” and why do platforms care?

“AI slop” is a shorthand term for low-value content produced with generative tools, often at high volume. The phrase typically refers to posts that are grammatically sound but shallow, repetitive or obviously optimized for engagement rather than insight.

Platforms care because AI slop can distort recommendation systems, crowd out expert voices and reduce trust in the overall feed. On professional networks, the stakes are even higher: if users begin to assume that much of what they see is synthetic, they may spend less time engaging with the platform or treating its content as credible.

For LinkedIn, there is also a brand risk. Its business depends on serving professionals who expect authenticity, whether they are reading commentary from executives, evaluating candidates or promoting their own expertise. If the platform becomes cluttered with generic AI posts, its core value proposition weakens.

What makes LinkedIn’s challenge different from other social apps?

LinkedIn’s challenge is different because the feed is tied to identity in a way most social platforms are not. Posts are often attached to real names, job titles and companies, which means false polish can have consequences for reputation and business relationships.

That environment makes AI-generated filler feel more invasive. A generic motivational post may be annoying on one platform, but on LinkedIn it can directly interfere with the credibility users are trying to build.

It also creates a more complex moderation problem. Some users genuinely use AI to clean up grammar, organize thoughts or overcome writer’s block. Others use it to produce a high volume of indistinguishable posts. LinkedIn’s latest updates suggest the company is trying to separate those two behaviors without punishing legitimate use.

What else is changing on LinkedIn?

Alongside the anti-slop measures, LinkedIn is making a few other product adjustments aimed at improving the quality of interactions on the platform.

• It is expanding access to profile and page verification tools.
• It is adding the ability to block comments from company pages that users no longer want to see.
• It is strengthening automated defenses against spam and bot activity.
• It is replacing AI-based post generation with AI proofreading.

Each change points in the same direction: LinkedIn is trying to make the feed feel more controlled, more trustworthy and less noisy. Rather than letting AI shape the overall voice of the platform, the company seems intent on limiting AI to background support functions.

Timeline: how the anti-AI-content push has accelerated

LinkedIn’s move is part of a fast-moving industry reaction to the spread of generative content across the web. The table below highlights several recent developments that help explain why the company is acting now.

What the new policy says about LinkedIn’s future

LinkedIn’s latest changes suggest the company is trying to redefine what acceptable AI use looks like on a professional social network. That distinction may become increasingly important as generative tools spread through workplaces and personal branding becomes more tightly linked to automation.

The platform is not rejecting AI outright. Instead, it is narrowing the acceptable use case to behind-the-scenes support: proofreading, detection, filtering and fraud prevention. The message is that AI should help users communicate more clearly, not erase the individuality that makes communication worth reading.

That approach may prove difficult to enforce consistently. Detection tools can misfire, users can over-report content they simply dislike, and AI-generated text is becoming harder to distinguish from human writing. But LinkedIn appears to believe that combining user signals, automated classification and policy changes is the best path forward.

If successful, the company could offer a model for other platforms dealing with the same problem. If it fails, the feed may continue to drift toward the same generic content users are already complaining about across the web.

Why this matters for users, creators and businesses

The implications go beyond one new button. For everyday users, it could mean a cleaner feed with fewer formulaic posts. For creators, it may create more pressure to prove originality and expertise. For businesses and recruiters, it could improve confidence that the voices they see on LinkedIn are tied to actual judgment rather than AI-generated marketing.

It may also push professionals to think more carefully about how they use generative tools. AI-assisted drafting is likely to remain common, but LinkedIn’s move suggests that overtly synthetic, personality-free content may increasingly carry a penalty in visibility and perception.

As more platforms follow suit, the internet may be entering a phase where the main challenge is no longer generating content, but proving that it deserves an audience.

LinkedIn’s product team says the goal is not to ban AI entirely, but to stop low-quality automated material from overwhelming the human expertise the network was built to showcase.

That balance will be the real test. The company now has a reporting button, stronger classifiers and a revised AI writing feature. What it still needs to prove is whether those tools can meaningfully restore trust in a feed that, like much of the web, is increasingly crowded by machine-made noise.