Users mash LinkedIn's AI slop button 1M+ times in 3 weeks
Or: Shock as users of world's largest slop repository report lots of slop
Since LinkedIn launched its "Seems like AI slop" button at the end of July, over 1 million users have clicked it. According to Hari Srinivasan, LinkedIn's chief product officer, those who copy-paste AI-written posts are experiencing around 40 percent fewer views compared to before the button was introduced. Srinivasan emphasized that while progress has been made, there is still more work to be done to ensure LinkedIn remains a place where real people and perspectives can be found.
The button's impact on post distribution is determined by a variety of signals, and users will now receive messages in their analytics dashboards to understand how their posts are being received by viewers. If users deem there to be too much AI content, the platform will convey this feedback to them. Anyone with a logged-in account can report a post for suspected AI slop using the platform's three-dot option menu.
Srinivasan clarified the distinction between AI and "slop," stating that LinkedIn believes people are entitled to utilize AI to make their content appear more authentic. The company aims to provide white-collar social media users with human feedback instead of automated AI detection. LinkedIn is also developing automatic detection mechanisms for AI slop content.
Before the button's launch, the platform had already been using these detections to identify hundreds of thousands of AI-slop comments each day and billions of other automation attempts in the past few months alone. In July, LinkedIn removed its AI-driven "enhance your post" feature, replacing it with a proofreader. Merriam-Webster named "slop" as its word of the year for 2025, describing it as content that is lazily compiled, ranging from fake news to social media posts and even propaganda. AI slop has become a significant issue in various fields, including cybersecurity.
Written by urgent.news from The Register's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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