What Is AI Slop? Definition and Examples

Read three paragraphs and nothing is wrong with them. The grammar holds. The transitions connect. The register suits the venue, and every claim in them is defensible. Read a fourth paragraph and the actual problem arrives, which is that you couldn't repeat a single thing you've just read to somebody else.

AI slop is the name that stuck for that experience. What is AI slop, in one sentence: content produced at very low cost that meets every surface requirement of its genre while carrying almost no information a reader can use. The word started life as a description of images and search spam. It has since become the most precise term available for a particular failure in prose, and the definition is worth getting right, because a term that means only "writing I dislike" is no use to an editor holding a deadline.

What Is AI Slop? A Working Definition

Three conditions have to hold at once. The text was produced at very low cost per word, whether by a model or by a person writing on autopilot. It is fluent enough to survive a skim, so the fault stays invisible at sentence level. And its information density is low relative to its length, meaning a reader who finishes it holds no more than a reader who stopped after the opening paragraph. Remove any one of the three and the word stops applying. Ungrammatical writing fails the second condition. A dense, difficult research paper fails the third.

The same definition separates slop from two things it gets confused with. Misinformation is false, while slop can be entirely accurate and still be slop, because accuracy is a different property from information. Spam is deceptive by intent, while slop is often written in complete sincerity by someone who believed the draft was finished. The sentence-level habits underneath it, the vocabulary and rhythm that make a paragraph feel machine-shaped, are catalogued in TextPulse's breakdown of AI writing patterns. This piece is about what those habits add up to.

Where the Word Came From

The sense of slop as low-grade machine output appeared in online communities soon after image generators became widely available in 2022, and Wikipedia's article on the term traces that early use to forum and comment threads rather than to any publication. The British programmer Simon Willison argued publicly for the word on his blog in May 2024, and has said since that it was already circulating before he pushed it. Usage climbed through the second quarter of that year, alongside the arrival of machine-generated summaries at the top of search results.

By the end of 2025 the word had travelled well past the forums. Merriam-Webster named slop its word of the year for 2025, and the American Dialect Society chose the same word. The metaphor underneath it does real analytical work and is worth keeping in mind while reading anything suspect. Slop is cheap feed, produced in volume, filling without nourishing. Every part of that maps onto text: cost per unit near zero, output effectively unlimited, and a reader who feels full and has learned nothing.

The Four Symptoms That Make Text Read as Slop

Padding comes first because it's the easiest to see once you know to look. A sentence of thirty-four words reduces to nine without losing a proposition. Twenty-five of them went and they were never carrying anything. There are whole constructions for this work: a phrase that announces the topic before naming it, a clause reporting that a range of factors are at play without saying which factors. Padding is the symptom a spell-checker will never catch, since every padded sentence is a correct sentence.

The second is stacked hedging. Research writing hedges for good reason. A careful methods section is full of qualified claims because the uncertainty in it is real. But slop hedges where nothing is uncertain, and it hedges more than once per claim. Three qualifiers on one finding leave the reader with no finding at all, only the shape of one.

Third is the summary that restates the paragraph above it. It usually opens by pointing backwards, then delivers the previous paragraph's content with different nouns attached. Nothing in it is false and nothing in it is new. In a piece under a thousand words there is no reader anywhere who needs a recap four hundred words in, which is what makes this symptom such a reliable signal about how the draft was assembled.

Fourth is the closing paragraph that adds nothing. It gestures at a future in which the field will continue to develop, or it restates the introduction with the tense changed. A real conclusion names what follows from the argument, says what would change the writer's mind, or hands the reader something to do. A closing paragraph that does none of those is the last place slop hides, because readers rarely read closings carefully enough to notice.

Slop and Its Repair, Side by Side

The table below takes each symptom, shows how it reads in a draft, and says what an editor does with it. None of the repairs is a synonym swap. Each one either states the proposition the sentence was circling or deletes the sentence for failing to have one.

Symptom	How it reads in a draft	What an editor does with it
Padding	In order to utilize a comprehensive framework, it is first necessary to consider the wide range of factors that may be at play.	Name the factors, say which the framework covers, and delete the run-up
Stacked hedging	The results may potentially suggest a possible effect of some degree.	Keep one qualifier at most, then state the number the qualifier is about
Vague authority	Studies show this approach is widely regarded as effective.	Name the studies, the year and the size of the effect, or drop the claim
The restating summary	As outlined above, these findings underscore the importance of the factors previously discussed.	Delete the sentence. The findings were stated once already
The empty close	Overall, this remains an evolving area that will continue to develop.	Delete the paragraph and end on the last finding that earned its place
Borrowed register	This piece will leverage a range of perspectives to unpack the topic.	Say what the piece argues, in vocabulary the writer would use out loud

Two of those repairs are word-level, and software handles them faster than a person does. TextPulse's free AI word cleaner marks the formal, low-information vocabulary that clusters inside padded paragraphs, which clears the surface so the structural symptoms underneath become visible. The other four need a reader who knows what the piece was supposed to argue.

Fluff: The Paragraph-Level Version

The Throat-Clearing Opener

The throat-clearing opener is the first paragraph of a section, and its entire content is that the topic exists, that people have studied it, and that it matters. It often begins with a time reference that names no date. It reaches the actual subject in its final clause, if it reaches it at all. A reader who skipped the paragraph would arrive at the second one holding everything they needed.

It survives editing because it feels like context, and context is a real thing a reader sometimes needs. The difference between the two is testable. Real context narrows the question or tells the reader something they did not already know. Throat-clearing announces the subject of the section, which the heading did first and did better. To cut it, delete the paragraph and read from the next one. If a term genuinely needed defining, define it in a clause where that term first appears, and the paragraph still goes.

The Restatement, and Why Editors Miss It

A restatement paragraph says the paragraph above it a second time with different nouns. Sometimes it arrives as a mid-section recap, summarizing four hundred words for a reader who read them ninety seconds ago. Sometimes it's subtler: the same claim moved from the abstract to the concrete, or back the other way, with no new proposition attached to the move.

Editors miss it because every sentence in it's fine. There's no grammar to correct, no ambiguity to query, nothing a proofreading pass was built to catch. The check that works is mechanical. Put the two paragraphs side by side and mark every proposition in each, meaning every statement that could be true or false. If the second paragraph contains no proposition the first one lacked, delete it and keep whichever clause did carry something new.

Scaffolding That Was Never Taken Down

Scaffold sentences announce what the writer is about to do instead of doing it. They open with a frame, deliver the frame, and reach the claim in a subordinate clause or in the sentence after. Common frames include "It should be noted that", "This section will discuss" and "In terms of", each of which pushes the claim one grammatical step further away from the reader. A whole paragraph of them describes a paragraph that was never written.

Deleting the frame is only half the repair, because the sentence was built around the announcement rather than around the claim. Promote the claim to the front, then check whether the sentence still needs the rest of itself. A word-level pass finds the frames quickly, and TextPulse's free AI word cleaner marks the formal connective vocabulary they are assembled from, which brings the structural version of the problem into view underneath.

The Paragraph Written to Reach a Word Count

This one is recognisable by what it introduces and never returns to. A new noun phrase appears, receives one sentence of respectful attention, and is never mentioned again. The paragraph lists considerations instead of making one. It hedges the whole way through, because a claim that was never made cannot be defended, and the hedging is standing in for the defence.

The test here is falsifiability, applied one sentence at a time. Ask which sentence in the paragraph could be false. If none of them could be false, nothing in it was asserted and length is the only thing it contributed. This is the fluff type most likely to appear in any piece written to a target, whoever wrote it, and it's why word counts and quality pull against each other so reliably in a first draft.

How Do You Cut Fluff Without Cutting the Argument?

Work top down. Delete whole paragraphs first using the deletion test, then re-read for continuity and repair the seams where two surviving paragraphs now sit together. Only after that should you go sentence by sentence. Running it the other way round spends the sentence work on paragraphs that were about to be cut, which is the most common way a fluff pass takes three hours and returns a draft the same length it started. Sentence-level work still has to happen afterwards, and TextPulse's AI humanizer handles that layer, reporting an estimated Human Score computed from the text rather than a verdict from any detector.

Fluff type	How it shows up	The test	What to do
Throat-clearing opener	A first paragraph establishing that the topic exists and has been studied	Delete it and read from the next paragraph	Delete it, and define any needed term where that term first appears
Restatement	A paragraph repeating the one above it with different nouns	Mark every proposition in both, then compare	Delete the second, keeping any clause that carried a new proposition
Scaffold sentence	A frame announcing the claim before the claim arrives	Remove the frame and see whether a claim is left standing	Promote the claim to the front and cut the frame
Word-count paragraph	Considerations listed, nothing asserted, hedged throughout	Ask which sentence in it could be false	Delete it, or write the one claim it was avoiding
Deferred explanation	A promise to explain something later in the piece	Search for the place where the promise is kept	Move the explanation to the term's first appearance and cut the promise

Cutting properly takes a piece under its target, which is the moment most writers put the fluff back. Spend the recovered words on the claim the fluff was standing in for instead: the number, the counter-example, the study that disagrees with the one already cited. TextPulse's guide to reducing word count in a thesis works through the same trade at chapter scale, where the pressure to restore deleted material is strongest.

Applying this to someone else's page is one skill and applying it to your own is another. Telling whether something was written by AI is set out on its own page, and so is the more uncomfortable question of whether your own writing sounds like it.

Take the last section you drafted and delete its opening paragraph without reading it again first. If you cannot say what went missing by the time you reach the end of the next paragraph, that opener was fluff, and so, most likely, is the opener of every other section in the file.

Is Slop the Same as Bad Writing?

No. Bad writing fails at the surface: unclear, ungrammatical, or organised so poorly that a reader loses the thread. Slop passes the surface and fails underneath. The practical consequence matters more than the definition does. A proofreader working sentence by sentence will hand back a slop draft almost untouched, because at sentence level there is nothing to correct, and that is exactly how slop survives editing passes that would tear apart genuinely bad prose. Structural editing catches it instead, and TextPulse's guide to editing an essay works at that level.

Slop is also distinct from AI-assisted writing in general. A researcher who drafts with a model, then cuts what the model padded, checks every citation and argues a position the model would never have taken, ends up with something that has an argument inside it. Slop is what arrives when the second half of that process never happens. For a writer whose own work has been mistaken for it, TextPulse's AI humanizer reports an estimated Human Score computed from the text itself rather than a verdict from any detector.

The narrower term for the same problem is fluff, which is defined on a separate page. If you want a working method rather than a label, telling whether something was written by AI sets one out.

Cover any paragraph with your hand and say aloud what it added to the argument. If nothing comes, that paragraph was slop, whoever or whatever produced it. The test takes ten seconds, it costs nothing, and it has never once cared which model was involved.

Related research: the findings above are examined at scale in The Vocabulary Fingerprint of AI Rewriting, a TextPulse Research working paper with open data, code and a citable DOI.