It seems to have a preference for speaking in poetic or highly expressively language, rather than precise and concise as most engineers like to talk.
The amount of times I have to ask "precisely what do you mean by x?".
It's kinda like that engineer that likes to throw around unnecessary technical jargon just to sound more inteligent, worse because at least you could kinda understand what the technical jargon dude was on about even if it was totally unnecessary.
It's not poetic or highly expressive; it's business cruft.
I don’t think it’s even that. It’s its own special flavor of bad writing.
And sometimes its not simply poorly written. Sometimes its just totally incoherent.
Claude writes like a guy at a firm I used to work with in the 90s; he was my employer's "visionary"; he'd worked at a whole lot of different companies on both sides of the Atlantic in inexplicably high-placed roles given that he was often bluffing, and was considered a lucky hire of a rising star. He'd be called into meetings with high end clients to spout off. He really needed you to know he understood, but very often he didn't.
I think it's likely that LLMs adopt the tone and style of their developers' communication culture. If you assume this is the case, you can infer quite a bit about the differences between OpenAI, Anthropic and Google DeepMind.
I am more and more clear about this given the way Muse Glimmer writes. Like a talented, slightly snarky guy who is maybe a bit of a dick but quite fun to be around.
Probably to a degree, I have found Gemini to be the least dis-likable of the models from the big 3 on that front. I wonder if the poor English comprehension of Deepseek-v4-pro and K3 is because of alleged distillation of Claude (speaking of why doesn't anyone distill openAI, are they just dramatically more competent at stopping API use that breaks their terms?).
V4-pro in particular seems very capable, but will just dramatically completely misunderstand user intent, it seems almost like it wasn't trained at all on non LLM generated instructions mid conversation.
I asked some AI-using compatriots a while back who were complaining about this, 'isn't it doubling down on bullshitting you?' and got some pushback along the lines of 'it isn't a person therefore doesn't have dark motives like that therefore can't be doing that to us'.
Didn't convince me. I think bullshitting like this can be a behavior, not just the intention of a human. If it's blowing a lot of smoke to use fancy words and phrasings (and semicolons! All the trimmings) it's fair to ask if it's systemically bullshitting you: i.e. the behavior is meant to have you shut up and trust it and not ask questions.
Who's driving that is still important: if the company's directing it to do that in system prompts that are adversarial to users, that's a big yikes. If it's an epiphenomenon of the company demanding it get ever smarter, maybe it's a sign that their demands are not having that result, rather they're making it bullshit more explicitly and mimic more 'smart' signifiers.
They have written like that when the models were much less capable, my hypothesis is this is an example of model collapse happening ever since LLM training leaned in heavily into RL and a result of training on model output the developers are uninterested in correcting since they want ASI not a somewhat useful AI coding tool that supplements humans without replacing them in the economic system.
> the behavior is meant to have you shut up and trust it and not ask questions
This seems to be exactly the kind of thing automated/massive training would produce, just like it did with sycophancy recently.
Claude users would just gave up after the word vomit and some classifier considered it a success and into the model it went.
Wrong incentive and nobody checking.