How did the Chinese labs catch up so fast if most of the early training work was

<!-- SC_OFF --><div class="md"><p>I’m trying to understand why models from major labs (GPT, Claude, etc.) dominate real-world usage? You might say it's due to the expensive

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t47qbw/deepseek_v4_pro_matches_gpt52_on_foodtruck_bench/"> <img alt="DeepSeek V4 Pro matches GPT-5.2 on

<!-- SC_OFF --><div class="md"><h1>It appears that the quantity and quality of foundation models developed in China and the United States are changing rapidly.</h1> <p>But it is

<!-- SC_OFF --><div class="md"><p>What is stopping the Chinese from catching up and overtaking? Is it just the difficulty in getting the most powerful GPUs or is there another

<!-- SC_OFF --><div class="md"><p>Xiaomi, yes the &quot;phone&quot; company, has two AI models that are turning heads. Pro (1T params) ranks right behind Claude Opus 4.6 on agent

<!-- SC_OFF --><div class="md"><p>Personally, I think the whole &quot;AI (LLMs) is going to take over&quot; is just BS marketing. They've been pushing this doom-and-gloom since

<!-- SC_OFF --><div class="md"><p>Chinese labs seem way more willing to release open-weight models while the big US labs keep everything closed.</p> <p>My theory is that if

<!-- SC_OFF --><div class="md"><p>Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize