There's a whole population out there that is fundamentally convinced that humans have some innate superiority over LLMs in terms of reasoning that will buy into this because they believe there will always be a difference for any text of meaningful length.

My main counter to that is that any such detector can only keep working if it's inaccessible to people directly or indirectly without huge consequences, because if it's accessible, it'll be probed and used to produce test cases to improve LLMs with and/or to train models to "spin"/postprocess LLM output. The latter can be done much "cheaper", e.g. consider the article:

> Real science papers also use more equivocal language – like "however", "but", "although" as well as "this" and "because".

Even if you can't circumvent this just with careful prompting (and you probably can in this specific case), this kind of information alone is enough to know you need to adjust the frequency of those words to better match scientific papers, whether by fine-tuning on some science papers or training a small model to know when it's ok to interject them in a text.

At a minimum you need to host and rate limit access to such a detector, or you can just automate working around it.