How to spot AI-generated LinkedIn copy (and why it's starting to backfire)

Published August 31, 2026 · 6 min read

The usual advice is to hunt for words like "delve," "unlock," or "game-changer" and delete them. That's not wrong, exactly, but it's a weak signal — plenty of sharp human writers use those words too. The real tell isn't the vocabulary. It's what's missing underneath it.

The word-list approach is overrated

Search "AI writing tells" and you'll find long lists of supposedly robotic phrases: "seamlessly integrated," "cutting-edge," "at the forefront," "firstly, secondly, lastly." Writers have pushed back on this for good reason — skilled people used this vocabulary long before language models existed, and banning a word doesn't make a sentence any more specific. Swapping "leverage" for "use" doesn't fix a bio that still says nothing a stranger could check.

So instead of policing vocabulary, check for the thing AI-smoothed writing almost always lacks: a claim that could be wrong.

What actually gives it away

1. Nothing is falsifiable

"Led cross-functional teams to drive impactful results" can't be checked, because it doesn't say which teams, how many people, or what the result was. "Led a 6-person team that cut our onboarding time from 9 days to 3" can be checked — by a hiring manager, a reference, or you, a year from now, when you have to defend it in an interview.

2. It would describe anyone in the role

Read a sentence and ask: could this describe literally any product manager, or specifically the work you did? "Passionate about building great products that delight users" fits ten thousand LinkedIn profiles. "Owned pricing for the mid-market tier after our free-to-paid conversion rate stalled at 2.1%" fits one person's actual year.

3. Everything comes in tidy threes

AI-smoothed writing loves rhythmic triads — "strategic, data-driven, and collaborative" — because they sound polished without committing to anything. Real work is messier than three adjectives in a row.

4. The "not just X, but Y" construction, repeatedly

One of these is a stylistic choice. Four of them in one About section is a pattern — the sentence structure doing the work that a specific example should be doing.

5. No rough edges

Real careers have a project that didn't ship, a pivot, a metric that went the wrong direction before it went the right one. Copy that's uniformly glowing, with zero texture, tends to be either heavily massaged or written by something that has no stake in sounding human.

A five-minute self-check

Open your About section and your last three experience bullets. For each sentence, ask: could a stranger fact-check this? If the honest answer is no, it's not lying exactly — it's just not doing any work. Replace it with the actual number, the actual tool, the actual team size, or the actual before-and-after. If you genuinely don't have a number for something, say what you did in plain terms instead of reaching for a superlative to cover the gap. "Improved the checkout flow" is weaker copy than a real metric, but it's still more honest — and more specific — than "drove significant improvements to the user experience."

Where this gets tedious to do yourself

Going bullet-by-bullet and asking "is this specific or just smooth" is genuinely useful, and also genuinely tedious to do for an entire profile, especially your own — it's hard to see your own vague sentences as vague. That's the actual mechanism behind Valueatorpro's audit: every claim it keeps has to trace back to a verbatim quote already on your profile, and anything unsupported gets flagged instead of quietly reworded into something that sounds better but says less.

This piece draws on general, publicly discussed patterns in AI-generated writing, not on any Valueatorpro user data. Further reading: a pushback on the AI-buzzword-list approach, and on why LinkedIn's own register is easy for language models to replicate.