Real talk: if you’ve ever read something online and thought, “Hold up… a human did not write this,” you’re not crazy.
Spotting AI writing used to feel like chasing ghosts — one minute people were yelling about the word “delve,” the next minute the bots had already moved on.
But here’s the plot twist: Wikipedia — yes, the same website your teacher told you never to cite — has quietly built the most accurate field guide for catching AI writing anywhere on the internet. And it’s shockingly good.
So here’s the tea.
Since 2023, Wikipedia editors have been running this behind-the-scenes mission called Project AI Cleanup, because thousands of AI-generated edits were slipping into the site every single day. Instead of complaining, they built receipts — a full, evidence-packed guide for spotting AI prose in the wild. And honestly? The patterns they found are way more consistent than you’d expect.
Let’s break down the eight dead giveaways they use to catch AI writing instantly:
The “This Is Deeply Important” Vibe: AI loves to hype up things that absolutely do not need hyping. Suddenly everything is a “pivotal moment,” “a broader movement,” or some vague cosmic significance. But Humans on Wikipedia? Way more chill.
Over-obsession with tiny media mentions: AI will list every micro-appearance like it's trying to bulk up a résumé. A good example: “Featured in Local Gardening Newsletter Weekly.” Wikipedia editors see that and go: nope.
The Vague-But-Profound Clause: According to Wikipedia, AI loves dropping lines with vague claims of importance. Models will say an event or detail is “emphasizing the significance” of something or “reflecting the continued relevance” of some general idea— which sound deep but basically mean nothing.
Marketing Adjective Overload: When every place is “scenic,” every moment is “breathtaking,” and every building is “modern,” that’s not human written — that’s AI. I mean, encyclopedia entries don’t sound like Airbnb listings.
The Rhythm Problem: Language models have this habit of stacking present participles like: “highlighting,” “emphasizing,” “showing,” “signaling.” It creates this oddly smooth, artificial cadence that editors catch instantly.
Didactic, Editorializing Disclaimers: AI loves telling you what’s important to remember. Suddenly every sentence comes with a disclaimer: “It’s critical to note…” or “Consider that…” Sometimes it’s safety advice, sometimes it’s disambiguating different regions.
Section Summaries: LLMs love to hit you with “In summary,” “In conclusion,” or “Overall…”, basically restating the obvious at the end of every paragraph. It’s like reading a tiny cliff notes version of itself, over and over.
Outline-Like Conclusions About Challenges & Future Prospects: Many AI-generated articles drop a “Challenges” or “Future Outlook” section, often starting with “Despite its [great things], [subject] faces challenges…” and ending on a vaguely hopeful note. It’s neat, rigid, and structured — almost like a robot checking off a to-do list.
And here’s the big unlock:
These patterns don’t disappear, even as AI models get sharper. You can tweak the surface, but the underlying habits — the hype, the padding, the vague grandiosity — are baked into how these systems learn.
And yeah, Wikipedia’s guide basically proves the point: when you train models on oceans of generic internet text… they start writing like the internet.
And here’s the fun twist on our end:
Yes — we use AI tools too. A little for research, a little for editing, because we’re an AI-focused newsletter and it would be absolutely unhinged not to.
But that’s exactly why we test them, poke them, break them, study them, and figure out how to use them responsibly, so we can tell you which ones are worth your time.
Oh, and plot twist: we’ve even written an entire book with AI. If you’re curious, grab a copy and compare for yourself — see if Wikipedia nailed its guide or…
If you prefer, the full Wikipedia guide is absolutely worth a read. It’s easily the most savvy, no-nonsense breakdown of AI writing quirks I’ve seen anywhere.
