Yes, if you are using a generic LLM.

But you can tell it to use different styles. To be formal or in-formal, to insert colloquialisms or to remove.

People are depending on their own 'gut-sense' a lot, and not realizing they are really not correct.

If you think all it takes is paying attention, then you are missing it. It's both more widely used than assumed, and also now obscuring what is non-AI.

> But you can tell it to use different styles. To be formal or in-formal, to insert colloquialisms or to remove.

And when you get it right, the result doesn't get called AI generated.

> People are depending on their own 'gut-sense' a lot, and not realizing they are really not correct.

TFA is very obvious about it.

A human who writes like this should be ashamed to do so, and should endeavour to understand why the writing comes across as "generic LLM"-like and fix it.

We have reached a point where people can end up training their writing on generic LLM output. This is a bad thing, because it's bad output.

Even beyond any clues from writing style, the general presentation is bad. It presents far too many facts and figures without giving anyone a good reason to care about most of them. And then it ends with a section on a separate topic (how to choose a lab, rather than how they're distributed across the world).

Most importantly, though, the submission is presented with a different title that implies a different purpose to the article that is not elaborated in the article. I would have expected personal insight a) on why people should care about the FCC's action (there is no mention of that action at all); b) on what the process was like of collecting this data. And I would have expected, you know, mapping of the lab locations rather than bar charts giving geographic breakdowns.