Show HN: Training a model to identify AI web content from structure alone

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Show HN: Training a model to identify AI web content from structure alone

by jochenmadler

Hey HN! We’re Vincent and Jochen from Sitefire (https://sitefire.ai). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM).

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.

katyarailabs
01d
The “tells you the same thing three times” finding really rings true. That’s the structure every content-marketing course taught long before LLMs, and the models seem to have learned the most average version of it.
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.
asdff
13d
The idea is interesting but in looking at methods and github I feel the tooling leaves me wanting. I mean you are trusting LLMs here to establish, vet, and detect your various thresholds that were then used to train the classifier. I'd rather see this sort of thing done deterministically with actual code vs lossy human english prompts and a dependency on token spend to a single third party (who will probably pull the underlying model used in what a few short years probably) to replicate the results or try and use different training data.
1. jochenmadler • OP
  02d
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BobbyTables2
53d
Some people are just very robotic.
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…
1. userbinator
  13d
  Probably more like "some jobs are just very robotic". CSRs aren't paid to think for themselves, but rather to follow a script. I suspect that human would behave a lot more like a human outside of work.
evantbyrne
23d
Immediately saw false positives on content written before ChatGPT. Not difficult to see how the methodology is wrong when it considers restating the thesis in the conclusion to be signal.
1. jochenmadler • OP
  12d
  Can you elaborate on the false positives?
  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.
DylanMerigaud
03d
Structure alone can differentiate AI content, interesting approach.
[deleted]
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bryanrasmussen
103d
The idea is that AI slop is non-creative boring crap, to really determine if you are able to identify AI slop then it should be determined if you misidentify human slop as AI slop.
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?
1. bryanrasmussen
  12d
eshaanpm
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Brenndalampe1
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sofiarossi98
02d
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[deleted]
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pooploop64
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1. tomhow
  02d
  We've banned this account.
blackboxdev
33d
[flagged]
1. dang
  23d
  Can you please not post AI-generated or AI-edited comments to HN? It's not allowed here - see https://news.ycombinator.com/newsguidelines.html#generated and https://news.ycombinator.com/item?id=47340079.
  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.