在 hacker news 的kimi2.6模型发布新闻下（
http://t.cn/AXxfSWjr），Redis之父留下了这样一段评论，多少能代表现在中美以外的人的一些看法（他是意大利人）：

This is not in antithesis. My limited personal experience is that I wrote code under OSS licenses primarily because of my past communist believes and current left-wing and redistribution of wealth point of view. This is not to provide the simple equation of: communist China is not interested in money, but also is hard to believe that there is no cultural connection among those things. Single Chine persons want to win, but also they have a different POV on what the collective means, compared to US. Also there is the obvious fact that in this moment China is more interested in winning technologically in AI, more than economically, since, I believe, they more collectively realized before many others that LLMs are eventually commoditized in the current form, in the long run. One could assume that a breakthrough could give some lab a decisive advantage, but so far we assisted to a different reality: it looks like AI is not architecture-bound (like LeCun and others want us to believe, but so far they mis-interpreted LLMs at every step) but GPU bound, and the data-boundness is both a common ground for all, and surpassable via RL in many domains. So, if this is true, it is not trivial for any single lab to do so much better. And indeed as far as we observed right now folks with enough engineers, GPUs, money, can ship frontier models, and in China even labs with a lot less GPUs can still do it at a SOTA level. For me, Italian, this is also a protective layer. After Trump the US looks like a very unstable partner from which to relay in an exclusive way for a decisive technology, and given that Europe is slow to put the money in this technology to have frontier things at home, China is a huge and shiny plan B for us.
这并非对立。就我个人有限的经验而言，我之所以主要基于开源软件许可编写代码，是因为我过去的共产主义信仰以及目前左翼和财富再分配的观点。这并非要简单地得出“共产主义中国对金钱不感兴趣”的结论，但也很难相信这两者之间没有文化联系。中国人渴望成功，但他们对集体意义的理解与美国人不同。此外，显而易见的是，目前中国更注重在人工智能领域的技术胜利，而非经济胜利，因为我相信，他们比许多其他国家更早地意识到，从长远来看，LLM（学习型硕士）最终会以目前的形式商品化。人们可能会认为，一项突破性进展能让某个实验室获得决定性优势，但到目前为止，我们看到的却是另一种现实：人工智能似乎并非受限于架构（正如勒昆等人试图让我们相信的那样，但他们迄今为止对 LLM 的解读都存在偏差），而是受限于 GPU，而数据限制既是所有实验室的共同点，又可以通过强化学习在许多领域超越。因此，如果这是真的，那么任何一个实验室想要取得如此巨大的进步都并非易事。事实上，就我们目前的观察来看，拥有足够工程师、GPU 和资金的实验室就能交付前沿模型，而在中国，即使是 GPU 数量少得多的实验室也能达到 SOTA 水平。对我这个意大利人来说，这也是一层保护。特朗普之后，美国似乎是一个非常不稳定的合作伙伴，无法在关键技术领域完全依赖它。鉴于欧洲在投资这项技术方面进展缓慢，难以在国内拥有前沿成果，中国对我们来说就是一个巨大而光明的备选方案。