The words and phrases that make LinkedIn posts sound like AI (2026 list)

Readers on LinkedIn got good at spotting AI writing in 2025 and 2026 — not because they read a guide about it, but because they saw thousands of posts that all sounded the same. The tells are specific enough that most active LinkedIn users can now flag a generated post within the first two lines, before they've even processed what it's about.

Here's the actual list, organized by why each one happens, plus what to do about it beyond "delete and hope."

Words that are almost always AI, not you

These show up constantly in model output and rarely in how people actually talk. If you wouldn't say a word out loud to a colleague, it probably shouldn't be in your post:

• Delve — nobody says "let's delve into this" in conversation.
• Leverage (as a verb for anything other than actual financial leverage)
• Robust
• Tapestry — "a rich tapestry of experiences" is a dead giveaway.
• Realm — "in the realm of marketing" instead of just "in marketing."
• Elevate, unlock, supercharge — vague intensifiers that promise more than the sentence delivers.
• Navigate the complexities of — an entire phrase that means "deal with."

None of these words are wrong in isolation. The problem is frequency: a model reaches for them constantly because they sound authoritative and fit almost any topic, so when three or four show up in one 150-word post, the pattern becomes obvious.

The sentence structures, not just the words

Word-swapping only gets you so far, because the deeper tells are structural:

• "It's not just X, it's Y." This construction is everywhere in AI output because it's a cheap way to manufacture a turn that sounds insightful. Used once in a hundred posts, it works. Used in every third AI-assisted post on the platform, it's become the single most recognizable AI fingerprint of 2026.
• The hollow opener. "In today's fast-paced world," "In the ever-evolving landscape of X" — these exist to sound like an introduction without committing to a specific claim yet. A real opener commits immediately: a number, a scene, a disagreement.
• The tidy three-to-five-item list. Ask a model for a post about almost anything and it defaults to organizing the answer into a clean numbered or bulleted structure. Real thinking is messier — it has one point it actually cares about and spends most of the post on that one point, not five equally-weighted ones.
• The hedge. "While there are pros and cons to both approaches..." AI is trained to avoid controversy, so it defaults to presenting both sides evenly even when a real person writing about the same topic would just pick a side. Posts that hedge get less engagement, not more — readers scroll past content that isn't willing to disagree with anything.
• The over-resolved ending. AI writing arrives at its conclusion without showing any of the thinking that got there — no false start, no moment of changing its mind, no friction. Real writing usually has at least one visible seam.
• The em dash, everywhere. Language models are trained on text that overuses em dashes for asides and transitions. One or two per post is normal for a human writer with that habit. Five or six in a 150-word post is a strong tell, mechanically fixable by rewriting each one as a comma, a period, or parentheses.

Why editing for these doesn't fix the actual problem

Here's the uncomfortable part: you can strip every word on this list, break up every em dash, and cut every hedge — and the post can still read as generic, because the tells above are symptoms, not the disease. The disease is that the post was written from a topic prompt with no specific input about how you actually think or write.

This is the distinction that matters. Two different fixes get confused constantly:

• Surface editing: find-and-replace the words on this list, vary sentence length, cut hedges. This helps, and takes about ninety seconds per draft, but it's treating the symptom.
• Voice-matched generation: giving the model your actual past writing as a constraint before it generates anything, so the first draft doesn't need this kind of editing pass at all — because it was never generated in "generic LinkedIn voice" to begin with.

The second one is the harder problem, and it's the one Verbatrum's Voice Match Score is built to solve — it doesn't just check whether the draft avoids em dashes, it measures the structural distance between the draft and your real writing samples, so "sounds generic" becomes something you can catch and quantify before you post, not something a reader flags in the comments after.

A quick self-audit

Before you publish anything AI-assisted, run this check:

1. Read it out loud. Any sentence you'd never actually say, cut or rewrite.
2. Count the em dashes. More than two in a short post, fix them.
3. Check the opening two lines and closing two lines specifically — these are where AI is weakest and where your voice needs to show up even if a model wrote the middle.
4. Ask: does this post take a position anyone could disagree with? If not, it's probably hedging.
5. Search for the words on the list above. If you find three or more, that's your generic-AI signal.

If you're using Verbatrum's YouTube-to-LinkedIn tool, the Voice Match Score does most of this check automatically and tells you when a draft has drifted from your actual writing pattern — but the manual version above works with any tool, including a blank ChatGPT window.