There is an important distinction to be made between base models, which are next token predictors (and write in a very human like manner) and the chatbots people normally use, despite both being called "LLMs". Those involve taking a base model and then subjecting it to further instruction tuning and Reinforcement Learning from Human Feedback.  My understanding is that a lot of the stylistic quirks of the latter arise from the fact that OpenAI (and other companies) either personally or through outsourcing hired Third World contractors. They were cheap and reasonably fluent in English. However, a large number were Nigerian, and this resulted in a model slightly biased towards Nigerian English. They're far more fond of "delve" than Western English speakers, but most people wouldn't know that, because how often were they reading Nigerian literature or media? It's particularly rampant in their business or corporate comms.  Then you have other issues stemming from RLHF. Certain aspects of the general chatbot persona were overly engrained, as preference feedback biased towards verbosity and enthusiasm. And now that the output of ChatGPT 3.5 and onwards is all over the wider internet, to an extent, new base models expect that that's how a chatbot talks. This likely bleeds into the chatbots that are built on the contaminated base model, since they have very strong priors that they're a chatbot in the first place.  That doesn't mean that there isn't significant variance in how current models speak. Claude has a rather distinct personality and innate voice, even within the various GPT models, o3 was highly terse and fond of dense jargon. With the availability of custom instructions, you can easily get them to talk you in just about any style you prefer. 

One possible contributor: posttraining involves chat transcripts in the desired style (often, nowadays, generated by an older LLM), and I suspect that in learning to imitate the format models also learn to imitate the tone (and to overfit, at that; perhaps it's due to having only a few examples relative to the size of the corpus, but this is merely idle speculation). (The consensus on twitter seemed to be that "delve" in particular was a consequence of human writing; it's used far more commonly in African English than in American, and OpenAI outsourced data labeling to save on costs.)  I haven't noticed nearly as much of a consistent flavor in my limited experimentation with base models, so I think posttraining must make it worse even if it's not the cause.