What this is
On Reddit's r/LocalLLaMA, a user posted an output from GLM 5.3 Flash: asked "Why don't don't Chinese restaurants just call themselves 'Chinese food'?", the model returned an answer that took a regular reader two passes to parse — short sentences, cold vocabulary, and even an academic reference like "Baudrillard-style failure point." We've noticed this isn't a GLM-only problem. The new generation of open- and closed-source models — Claude, GPT, Gemini, DeepSeek, Kimi — are all converging on a similar style: word-economical, fond of obscure terms, syntactically tight. One plausible explanation: training objectives now reward token efficiency (packing more meaning into fewer tokens), so models have learned to compress expression and upgrade vocabulary.
Industry view
Supporters call this progress — closer to how senior practitioners write, density equals productivity. But the counter-view deserves more attention: this style is hostile to ordinary readers — non-specialists hit "Baudrillard-style" and give up entirely; it exposes a bias in RLHF (human-feedback fine-tuning) — the raters are likely highly educated researchers, and the models are picking up their speech patterns; "information density" is not the same as "useful information," and obscure terms sometimes just mask shallow logic; and there's a hidden risk: future human-AI collaboration will carry higher communication overhead — what AI writes has to be manually "translated" again before a team can actually use it.
Impact on regular people
For enterprise IT: selection testing should put "readability" on the evaluation checklist — don't just chase benchmark scores. For individual professionals: when using AI to draft emails or reports, actively add a prompt like "please rewrite in plain Chinese," or colleagues will think you're "showing off." For consumer markets: if customer-service and companion-style consumer AI pick up this style too, users will feel like they're chatting with a PhD — and the experience will actually get worse.