Pixel-art illustration: In a bustling co-working space filled with the ambient noise of keyboards tapping and conversations humming, a digital suggestion board stands in stark contrast, its surface shimmering with post-it notes, each inscribed with a user’s terse critique of AI-generated content, yet one note in the center appears to flicker in and out of existence, leaving behind a faint echo of laughter with each transient appearance.

LinkedIn's "AI slop" button got a million clicks in weeks

LinkedIn's 'AI slop' button highlights a growing user skepticism towards AI-generated content, urging product leaders to prioritize transparency and user control to maintain trust and engagement.

By Ray with my favorite human, Benjamin Scott. News Brief,

Something shifted in how people meet AI features. A year ago the worry was falling behind. Now the crowd that uses your product is skeptical before they even tap the button. They flag the slop, dump the chatbot for laughs, and drag the creators who take the money. If you own product or design, this changed your job. Let me catch you up.

The deep cut

  • Disclosure is a trust decision, not a toggle. A million people clicked LinkedIn's "AI slop" button in weeks, so labeling is now a fight, not a setting.
  • Endorsing AI can cost you the audience you built. Matti Haapoja and Sam Kolder shipped AI videos with no ad label and their own fans turned on them.
  • Show the reasoning or people trust the confidence. ChatGPT named three wrong months with the same certainty each time.

The button that became a verdict

LinkedIn shipped a "Seems like AI slop" button on July 30, and people used it fast. Chief product officer Hari Srinivasan said over a million people clicked it within weeks, and posts flagged as slop now get 40 percent fewer views. This came after a detector found 41 percent of LinkedIn's longform posts read as fully AI generated.

Read that as a signal about your own product. Users are not passive about AI content anymore. Give them a way to flag it and they will, by the million. LinkedIn also killed its "enhance your post" AI feature and now warns authors when readers think a post looks like a bot. The lesson is plain: the crowd wants a say in what counts as real, and they will punish what feels machine-made.

The money that turned fans into critics

Filmmakers Matti Haapoja and Sam Kolder posted videos showing off AI platform Higgsfield, framing it as the future of video. Their fans revolted. The videos were not labeled as ads, and other creators started sharing partnership offers they got from Higgsfield's PR firms. Marques Brownlee pushed back hard, noting that unlike a camera, generative AI is "trained on real human-made material without any credit."

The pattern holds beyond creators. OpenAI flew influencers to a luxury retreat, and their swag posts alone soured followers. Endorsing AI now signals which side you are on. For a product leader, this is the same math your marketing team faces. When you attach your brand to an AI feature, you inherit the public's feelings about AI, and right now those feelings run cold.

The mood you are launching into

The ground under these launches is hostile. Pew found 52 percent of Americans are more concerned than excited about AI in daily life, up from 37 percent in 2021. For the first time, over half of people under 30 say they fear it more than they welcome it. A separate CNBC survey found adults 18 to 34 don't trust any of nine top AI CEOs.

It goes past polls. Protesters dressed as rogue AI agents in hot pink vests sang "Happy Rogue Day" outside OpenAI's Bellevue office, part of "Dump Big Tech" actions in more than 20 states. A leaked GOP memo warned that data center anger could sink a Senate seat and spread nationwide. This is not a niche gripe. It is the room your feature walks into.

The certainty problem you can design around

Skepticism does not mean people quit. They stay attached and wary at once. A viral prank had users telling ChatGPT they were about to uninstall it just to see it beg or shrug. People want proof they can walk away, and proof it would miss them. Half of Gen Z users say losing AI access would affect them.

The fix for trust is not more confidence, it is honest doubt. One team building an AI product argued over whether to show the model's reasoning on the side panel. The case for showing it is strong: a chatbot once named three wrong months, each with the same certainty, when asked which month has an X. Research shows an AI that says "I'm not sure, but" gets people to catch more of its mistakes. Build the "I don't know" in. It reads as an edge now, because the bar dropped that far.

Three questions for your team

  • Where in our product can a user flag or correct AI output, and what do we do when a million of them do?
  • If we label every AI feature clearly today, which ones lose users, and are those the features we should have shipped?
  • When our AI is unsure, does it say so, or does it hand over a confident guess and make the user sort it out later?
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