Slop

Slop emerged as the word for a specific kind of AI output failure: technically competent but vapid. The posts with five bullet points all starting 'In conclusion...' The LinkedIn thought leadership that sounds like someone summarized a business book by skimming the table of contents. Images with that uncanny perfect-but-meaningless polish. The defining quality of slop isn't that it's wrong: it's that it's aggressively mediocre and obviously machine-produced.

The term spread fast because it named something many people were observing but didn't have a crisp word for. As AI content generation scaled up, the web started to fill with content that passed a surface-level check (it's grammatically correct, it's on-topic, it's formatted) but provided no real insight, contained no real judgment, and could have been written by nothing that understood what it was saying.

For builders, slop is a product risk. Any AI feature that generates content at volume without strong human review or quality filtering risks producing slop and damaging trust. The counter is curation, domain specificity, evals that measure quality beyond surface correctness, and genuine human judgment at key points in the workflow. Being aware of the slop pattern helps you build AI tools that add real value rather than just adding volume.