The text has few of the obvious AI tells. The only thing that, to me, looks characteristic of LLM-generated text is the short and terse sentence structure, but this has been a "prestigious" way to write in English since Hemingway.
The most obvious patterns here are: antithesis constructions, words choices and distribution, attempt at profundity in every paragraph but instead are runs of text that doing say anything, and even the perfect use of compound hyphenation. I think and can appreciate that there is definitely an attempt at personalization and guidance to make it less LLM-y and not just a default prompt, but it’s still kind of obvious. You could use a detector tool too of course.
This article is clearly LLM-generated, even the title. A key indicator is that it almost makes sense: we forgot how to manufacture because that got sent to a different nation. The coding thing isn’t getting sent anywhere, so humanity is forgetting how to code. The distinction undermines a lot of the emotional baggage about offshoring that the article wants you to bring along.
But it's really just the usual ones that are truly obvious. "Not X but Y," em-dashes, "underscores the significance of X," and so forth.
A terse sentence structure can be a tell, but, IMO, it's a weak one.
I agree - and they're on clear display in this article, I would say
Hemingway writes simple sentences with a kind of detachment to make the emotional flow of his stories as transparent as possible.
LLM slop reads more like slide bullet points extrapolated to prose-length text
Find some pre 2020 that are, and you'd have a point.
I'm not trying to defend the blog post, but I gave Slop Cop 775 words of an essay by Schopenhauer (translated into English) and got "15 patterns detected."
I fear we're approaching the point where AI-written text grows indistinguishable from human-written text, unless the AI-user is exceptionally lazy and uses an obsolete model...