AI slop is digital content of low quality that is produced in quantity by means of generative artificial intelligence. The term carries a pejorative connotation, similar to spam. Merriam-Webster selected “slop” as its 2025 Word of the Year, and the American Dialect Society made the same choice, reflecting how the word entered mainstream vocabulary as AI-generated content flooded social media, search results, and online publishing.

Origin and definition

The word “slop” dates to the 1700s, originally meaning soft mud or something of little value. The AI-specific sense emerged in the early 2020s. A poet and technologist writing under the name “deepfates” is credited with popularizing the term in 2024, describing it as “the term for unwanted AI-generated content.”

Merriam-Webster’s official definition: “digital content of low quality that is produced usually in quantity by means of artificial intelligence.” The dictionary announced the selection on 15 December 2025, noting that the word captured “all that stuff dumped on our screens, captured in just four letters.”

What makes content “slop”

AI slop is defined by the intersection of three properties:

Low quality. The content lacks substance, accuracy, or creative merit. It may be technically fluent but reads as filler: generic, hedged, repetitive, or factually unreliable.

High volume. Slop is mass-produced. A single operator can generate thousands of articles, images, or videos per day at near-zero marginal cost.

Minimal curation. The creator invests little effort in reviewing, editing, or verifying the output before publication. The goal is attention capture or monetization, not quality.

Content that is AI-generated but carefully reviewed, edited, and verified is not typically called slop. The term targets the output of publish-first-verify-never workflows.

Where AI slop appears

  • Social media. AI-generated images of fake emergencies, sentimental stories, and engagement-bait posts designed to harvest likes and shares.
  • Search results. SEO-optimized articles generated at scale to capture traffic, often with thin or inaccurate information.
  • E-commerce. Fake product reviews, auto-generated listings, and synthetic customer testimonials.
  • Publishing. Books and articles written entirely by AI and sold without disclosure, sometimes containing hallucinated facts or plagiarized passages.
  • News and information. Fabricated news sites, AI-generated misinformation, and synthetic content designed to look authoritative.

Why it matters

AI slop degrades the information environment. When low-quality content floods platforms, it becomes harder for users to find reliable information and harder for legitimate creators to compete for attention. The problem compounds: as AI models train on web data that increasingly contains AI-generated slop, they risk model collapse , a progressive degradation of quality and diversity.

A June 2026 convening at Columbia University’s Institute for Global Politics documented how AI slop threatens research integrity and public discourse. Participants noted that the definitional traits converge at the intersection of low quality, mass scale, and varying creator intent.

Distinguishing slop from legitimate AI use

Not all AI-generated content is slop. The term specifically targets:

SlopNot slop
Unreviewed, mass-publishedHuman-edited and verified
Designed for engagement farmingDesigned to inform or create value
No disclosure of AI useTransparent about AI assistance
Factually unreliableFact-checked before publication

Using AI as a drafting tool, then reviewing and refining the output, is a workflow. Publishing thousands of AI-generated articles without reading them is slop.

Sources

Further reading

  • Model collapse : what happens when AI trains on AI-generated content — progressive quality degradation.
  • Dead internet theory confirmed : bots now generate 57% of web traffic, accelerating the slop problem.
  • AI washing : a related phenomenon where companies overclaim AI capabilities.
  • Hallucination : when AI generates plausible but false information.
  • Slopsquatting : when attackers exploit AI-hallucinated package names to distribute malware.
  • Synthetic data : the controlled use of AI-generated data in training.