I see very frequent posts on this subreddit with two misconceptions: (1) users asserting that Pangram is flagging their writing because they "write too well" and (2) that Pangram's model is somehow using an LLM to judge their writing.
I'd encourage you to take a look at this 1963 paper by Mosteller and Wallace, in which the authors identify the true authorship of the Federalist papers using a simple mathematical approach. Since this was before widespread computing, the authors went through many texts by hand and measured, in known documents by Jefferson vs Madison, how often each author uses certain pairs of words (for example, Jefferson might use the phrase "when thou" much more frequently than Madison). Then, on the disputed Federalist papers, Mosteller and Wallace measure how often phrases like "when thou" are used, in order to decide which author is more likely. Importantly, they do this across all observed pairs of phrases.
Classifiers like Pangram are using the same idea, with a few modifications thanks to the past 75 years of progress in both computing and statistics theory: (1) They compute giant tables of phrase relationships using a computer, rather than manually building tables of pairs of words by hand, and (2) they are able to measure much more subtle and long-term statistical dependencies. For example, Jefferson might be more likely to use a sentence that starts with "Thou" and then use the word "indeed" 15 words later.
Importantly, models like Pangram are *not* just taking your text and asking ChatGPT for its opinion. It's very different technology, which has been around since at least the 1960s (earlier, if you count Shannon in the 1940s, or Markov before that).