On Thursday, I ranted on LinkedIn about how I’m tired of reading “AI Slop” everywhere.
AI slop is the flood of low-quality AI-generated content, like text, images, audio, and videos, often created for clickbait, ad revenue, or mass production rather than real value.
53.7% of posts written on LinkedIn in 2025 were AI. Yes, more than half of the stuff we’re reading is AI slop. No wonder that the term was added to the Cambridge dictionary as more and more internet users kept complaining about it.
It took me some time to understand what was happening. And once I did, it was clear.
What I was noticing was AI’s fingerprint. And once you understand why that fingerprint exists, you’ll spot it everywhere — LinkedIn posts, comments, Instagram Reels. In about 10 seconds.
First, why does AI have a fingerprint at all?
When an AI model like ChatGPT or Claude is trained, it learns by consuming billions of pieces of human-written text — articles, books, social media posts, forum threads, everything. It then learns to predict: "given what came before, what word or sentence is most likely to come next?"
It doesn’t actually think. It looks at what you asked, cross-references everything it was trained on, and produces the statistically most likely response. It's just completing a pattern — very, very convincingly.
Think of it like your phone's autocomplete, but on steroids. When you type "Happy" on your keyboard, your phone suggests "Birthday". Because that's what billions of people type after "Happy." It's not thinking. It's just repeating the most common pattern it's seen. AI writing works the same way, just at a scale that can produce 500 words instead of two. Ask it for a "motivational LinkedIn post" and it reaches for the most common patterns it's seen for that phrase — because statistically, that's what works. Every time.
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Now that you know that there are tells, let me tell you how to identify them.
On LinkedIn posts
You’ll see one of more patterns when someone has written a post with AI.
Lot of high-impact words: Posts will have words like “boasts a, exemplified, terrible, quietly’ etc.
Rule of three: This is a powerful concept in storytelling, where sentences are structured using groups of 3 to have higher psychological impact. For example, “He lost his job. His money. And the respect that came with it”.
Phrases that show exclusivity to the writer: Things like “Here’s what nobody’s telling you”, or “Here’s why nobody talks about it”. These phrases are used because it makes the reader feel like they’re reading exclusive information. The problem is, every second writer uses them :D
Antithesis in structure: Sentences like “It’s not A. It’s B” or some variant of them
Overuse of em dashes (—) in the post
I could go on and on. The list is endless. But these are the most common AI patterns you’ll see in LinkedIn posts.
One thing to keep in mind though: Not everyone who writes using these patterns is using AI. Some people genuinely write well. A lot of people use em dashes naturally (I’m one of those). The key is to see if it’s overdone. If it is, it’s mostly AI.
On LinkedIn comments
This is where it gets sad.
AI comments don’t engage. They validate. “Such a valuable perspective!” “This really resonates.” “Thanks for sharing this insight!” These aren’t thoughts. They’re the text equivalent of a “I agree”.
A real comment reacts to something specific in the post. An AI comment could be copy-pasted onto any post on any topic and still make sense. If it fits everywhere, it came from nowhere.
Interesting fact: There are “engagement pods” on LinkedIn. These are tools like Crosslike.club, where you sign up, login with your LinkedIn credentials, and become part of a community of thousands of people. Every time anyone from that community posts on LinkedIn, randomly selected profiles of real humans from that community write comments on the post All automated in the background using AI, they don’t even know it’s being done. Here’s an example I saw yesterday (the guy shouldn’t have tagged me :))
If an engagement pod is being used, you’ll see another pattern: Timing. AI- generated comments come in consistently, especially in the first one hour of the post going live. Because that’s what the algorithm rewards.
Essentially, if it’s all praises in the comments section of the post, it’s most likely an AI.
On Instagram reels
I had written an entire newsletter on this. There are fake AI profiles on Instagram with more than 200,000 followers. They even get “paid subscribers” and make money off of it.
The images you see above, are all AI profiles. And here’s how you can identify them.
Skin looks too perfect. Real skin has pores, uneven texture, asymmetry. AI skin often feels polished or “designed”
Lighting and backgrounds don’t fully match. Faces lit differently from the scene, or subtly changing locations across posts
Hands, ears, teeth still glitch. Finger counts, mismatched earrings, unnatural teeth are common tells
Very few videos. Most AI influencers rely on photos because motion and expressions are harder to fake convincingly
Digital footprint feels empty. No old tagged photos, interviews, random friend uploads, or history outside the account itself
You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.
Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.
So where does it leave you?
The AI fingerprint isn't going away. If anything, it's going to get harder to spot as models get better. But AI can’t replace a real human experience. Nor can I replace real human writing.
So after reading this, you’ll know if something you read/watch is AI or not.
If you found this useful, please share it with a friend so that they don’t get fooled (or worse, scammed) by an AI.
I’ll see you next time..
Cheers,
Ankur