With Sitefire (YC W26), we help marketing teams get recommended by AI Search (ChatGPT, Google AI Overviews, AI Mode, Claude, etc.). Our software monitors prompts, sees which web pages get cited, and uses these insights to help marketing teams take action, e.g. create YouTube videos or write the right blog posts.
This means we have a commercial stake in AI-generated web content. And for now, high-information, AI-generated content works great to get cited and recommended in AI Search.
But after talking to hundreds of marketing teams, it became clear that everyone despises AI-generated content (“AI slop”). And yet, everyone still wants to leverage AI to create content. So we asked ourselves: what characterizes AI slop? Can we train a model to identify it from human-generated web pages?
Researchers from the University of Maryland and Google DeepMind already asked this question for fiction. Their paper StoryScope (Russell et al., 2026) showed that you can tell AI-written stories from human ones by their structure alone, without looking at the words.
We ported their pipeline to commercial web pages. Using the Wayback Machine, we collected 2,250 blog posts from 268 B2B company websites that were written before ChatGPT existed. For each blog post, five AI models (GPT-5.4, Claude Sonnet 4.6, Gemini 3 Flash, DeepSeek V3.2, Kimi K2.5) wrote their own version.
Instead of looking at the words, we looked at how each post is built. We had an AI model answer 214 questions about every post, e.g. how hard it pushes its own product, whether it backs up its claims with sources, or whether it quotes a named expert. Then we trained a classifier on these answers.
On blog posts it had never seen before, our classifier told AI-generated and human posts apart with 98% accuracy, getting only 19 of 1,740 wrong.
Why does it work so well? Because all five AI models write in a similar shape. Mapping every AI model’s values for these features, we see they cluster together, while the human values sit apart and spread out much more. Of the 1% most unique blog posts in our data set, 149 are human, only 4 are AI.
So what characterizes AI slop? It tells you the same thing three times. The title already promises what you'll get ("How to Cut Onboarding Time in Half"), the intro lays out what's coming, and the ending says it all again. 77% of the AI posts end by repeating their main point, compared to only 12% of the human posts. We call it the tidy, self-announcing blog post.
Still, each AI model has its own accent. We trained a second classifier to tell which of the five AI models wrote a post, or whether a human did. It picks the right author 79% of the time, where random guessing (1 in 6) would get 17%. Almost all of its mistakes are mix-ups between the AI models, not between human and AI.
The cool thing about structural features is that you can't simply reword your way out of it. We had each AI model rewrite its own posts until, on average, 73% of their original 13-word sequences were gone, and the AI slop classifier still worked just as well.
We're building this into Sitefire: our agents get a structural understanding of text, so the posts they write go deeper and vary the way human writing does.
There's a lot we haven't tested yet, like the myriad of humanizer tools, human rewriting, restructuring a post, or prompting an AI model to explicitly avoid these habits. And our human posts are mostly from 2020 to 2022, while the AI posts were generated in August 2026. Structure can't really tell when a human post was written, but it's still not a same-year comparison.
We published the study with all the figures on arXiv: https://arxiv.org/abs/2609.15369. The code is on GitHub: https://github.com/pulse-energy-eu/slopshape
We're pretty sure your own blog isn't AI slop, is it? We built a checker that runs one of your posts through the ten features from the paper, so you can see for yourself (the full report asks for a work email): https://sitefire.ai/slop-checker.
Think you can tell AI slop from human writing? We also made a little game to see if you can keep up with our model, which gets all five rounds right: https://sitefire.ai/spot-the-slop.
Today I spent 20 minutes in a call to customer support. The representative spoke so robotic-ally (both cadence and word choice), I was extremely confident they were an AI for the first 15 minutes. Was quite certain the automated AI had transferred me to a more advanced one…
Turns out they were just likely in a low cost Latin American country and trying really hard. I felt bad one could be reduced to such…
The cadence was extremely robotic and consistent. Only thing that makes me think it wasn’t AI was the very end when they repeated a confirmation code and didn’t use the NATO names for the letters the second time around.
Re methodology: Restating the thesis is one signal of many. 77% of the AI posts close that way, but so do 12% of the human ones. The classifier in the paper never decides on one feature, but their combination.
Idea 1: Identify the worst most boring human marketing, organizational, bureaucratic texts from the a time before AI was writing it, anonymize this text to make sure there is no reference to current events that can be used to determine that it is not AI. And then see if the AI will say hey, that is not AI slop.
Idea 2: Have people parody AI slop. Can it determine the parody is still not AI slop?
I agree it is somewhat close to the study, but we are not sure, because it is not known how much is really boring dull marketing copy. In choosing blog posts from pre-AI times I suppose you might have difficulty finding the worst examples, and might accidentally get higher quality work.
>Using the Wayback Machine, we collected 2,250 blog posts from 268 B2B company websites that were written before ChatGPT existed
Not sure what metric was used to determine these 2250 blog posts? But there are certainly a lot of ways they can select higher quality posts by accident.
on edit: evidently the Jochen from the study, maybe they thought your comment was AI written.
You anonymized domains of pre-AI sources, are there any domains that had an excessive number of "telling you the same thing three times" or other AI tells among them?
Of the percentage that was misidentified, do they come from any sources in particular?
If you get a lot of content from these sources and run against the model do they perform worse? If they do how do they perform with word choice detectors? I would expect that structural slop is related to word choice slop among humans.
Anyway these are things I would be interested in as being the point where AI slop rubs up against the human slop which it learned from.
The 12% of human posts that do what AI do in repeating things, are they human slop?
I personally don't think it is possible to identify human and AI, it is however probably more possible to identify unoriginal and boring quality writing and art.
This was perhaps not clear from my first post, as I tend to imply points rather than tediously stating them.
Like its fine if you hold a blanket dismissal of the very premise of the research here, but then its kinda weird to spend such effort being grumpy about it in this particular context?
Its like finding a paper that does certain comparative work between different kinds of apple pie and choosing to come on here and be like "I don't think we should ever have any dessert either way!"
Because if you are not testing on people that are supposed to be the most German-like in their cooking then your ability to find the German cooking in a city renowned for its Thai food is not that impressive.
Then in response to your first question I posit that actually it is not possible to identify if pie was made by Germans but probably relatively easy to identify if it was made by people who cook in a German manner. And maybe that is actually more beneficial.
I realize that from communicating with people over the years that things which seem crystal clear to me may seem opaque to others, but I think your analogy is somewhat unfairly structured.
Note: apologies to German cooks and their cooking, although I personally only like currywurst. But I needed something to make the analogy more like what I felt had been communicated, and you were pseudo-randomly picked.
The amount of slop on the internet was on the rise well before AI, actually.
"President Bush in the state of The Union last month said"
is a statement that should only have been written in a factual document during the Pre-AI era, enough of those and the AI might turn that into math that says Reference to X as being current means NOT AI where X is a range of things that nowadays can only be referred to as the past, except in fiction.
Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) have been getting classified that way.
It's very clearly AI-generated, and thus a bit ironic (:
[0]https://sitefire.ai/slop-checker/r/UkRAuqW1y-1T2qhbRQfLt2Jq7...
What I like about structural features is that they make for a good eval, not just a detector. If you fine-tune a model to write for your brand, “did the new version get less sloppy than the one we’re running?” becomes a measurable question.
Disclosure: I work on Ookami (github.com/KatyarAILabs/Ookami), an open-source layer for serving and fine-tuning models. It only puts a fine-tuned model live if it beats the current one on evaluators you plug in.
Since Slopshape is open source, it could be one of those evaluators: fine-tune on your best human-written posts, and the gate rejects any version whose outputs score as more AI-shaped than the live model.
I haven’t tried it yet, but that’s the kind of use case I find interesting.
One question: were the 19 misclassifications writing to SEO templates (listicles, “ultimate guides,” etc.)? If so, the classifier might be detecting “written to a template” more than “written by a model,” which is arguably the more useful signal.