What is AI slop? The short definition, the examples, and how to spot it.

The definition

AI slop is low-quality digital content made with generative artificial intelligence, usually produced in high volume, that lacks meaningful human effort, originality, or accuracy. It covers text, images, audio and video. The defining feature is not that a machine was involved. It is that nobody cared enough to check, shape, or stand behind the result.

The term borrows from the older meaning of slop, the thin swill fed to pigs. The comparison is deliberate and unflattering: something produced cheaply, in bulk, for consumption by whoever is passing. It carries a connotation similar to spam, and it has largely replaced 'AI-generated content' as the phrase people actually use when they mean the bad kind.

Merriam-Webster chose 'slop' as its 2025 word of the year, defined as digital content of low quality that is produced usually in quantity by means of artificial intelligence. That is as good a one-line definition as exists.

Where the word came from

The word began circulating online in 2022, when generative image tools first went mainstream and feeds started filling with odd, glossy pictures. Early use was mostly among artists and forum users who needed a word for the flood. It stuck because it was short, a little rude, and exactly the right size.

It moved into general use through 2024 and 2025, as the volume of machine-made content on social platforms became hard to ignore. By the end of 2025 it was the word dictionaries and linguists reached for when summing up the year in language.

Examples of AI slop

Slop looks different by format, but the pattern underneath is the same: high volume, low specificity, and a purpose that is mostly about attention or revenue rather than the reader.

• Text: mass-produced articles, hollow listicles, generic social posts, and low-effort e-books that restate what any other page on the topic says, with no original source, number, or opinion.
• Images: surreal or deceptive pictures shared to bait engagement, such as the 'Shrimp Jesus' images, or fake disaster photographs that spread after real events like Hurricane Helene in 2024.
• Video: automated YouTube channels and short-form clips with synthetic voices, bizarre stock-like scenes that never happened, and clickbait hooks, attached to topics they know nothing about.
• Search and reviews: pages built to rank for a query rather than answer it, and product reviews written without the product.
• Code: whole projects generated and submitted without anyone understanding them.

Why it matters

The first cost is attention. Slop clogs search results and feeds, so finding something reliable and human-made takes more effort than it used to. The second is trust: believable fakes erode confidence in genuine material, and people begin discounting everything. The third is circular. When AI systems train on large amounts of AI-generated output, quality can degrade in a feedback loop, which is sometimes described as the model eating itself.

There is a fourth cost that rarely gets named. If you publish things yourself, being mistaken for slop is a reputational risk. A reader who suspects filler stops reading, and does not give you a second try.

Is all AI content slop?

No, and this is the part the word tends to flatten. Slop is a description of how something was made and what it is for, not of the tool used. A person who writes a careful post and uses a model to tighten the prose has not made slop. A person who prompts a model for fifty posts a day and publishes them unread has.

The useful test is whether a human is accountable for the result. Did someone decide this was worth publishing? Could they defend a specific claim in it? Is there a source underneath it that can be pointed to? Where the answer is yes, the output is closer to assisted work than slop, however much of it a machine produced.

How to spot AI slop

No single tell is reliable on its own. Look for several together. The list below works across text, image and video.

• It is generic: the same statements could sit on any page about the topic, with no specific name, figure, quote, or example.
• It is confidently vague: smooth sentences that say nothing checkable, with no sources.
• It repeats itself: restated points, formulaic structure, and the same rhythm throughout.
• Visuals have artifacts: warped hands or text, melted features, objects that do not make physical sense, backgrounds that drift.
• Footage shows things that did not happen: an office, a city or a crowd that has nothing to do with the subject.
• The volume is suspicious: an account or site publishing far more than one team could research.
• There is no person behind it: no author, no history, no way to ask a question.

How to avoid making it

If you publish with AI tools, the fix is upstream of the tool. Start from something real: a post you wrote, data you collected, a document you can cite. Generation about a topic produces slop. Work built from a source tends not to, because the source supplies the specifics the model cannot invent.

For video, the same principle applies. A video compiled from your actual content, inside a template a person designed, behaves very differently from footage generated from a prompt. We wrote the longer version of that argument separately, including a thirty-second way to tell the two kinds of tool apart.

Read: the AI video generators that are not slop

Turn something you already wrote into a video

If you have a post you are proud of, the most reliable way to stay out of slop territory is to build from it. Blog2Video takes a URL or a draft and follows your structure, headings and examples into a designed template, so every number and quote on screen traces back to your source.

Convert a blog post to video

Frequently Asked Questions