以下文章来源于路边消息社 ，作者雷教授 导 语：技术创新很难复制，否则，拥有技术的大模型六虎，怎么还在挣扎融资和降低成本中不 可自拔？ 这帮做量化的，捅了大模型的老巢了？ 笔者了解到一个最新消息，梁文锋的幻方量化老对手、量化巨头九坤，携手微软团队成功复现 DeepSeek-R1，还首次发现了语言混合（例如中英文夹杂）会显著降低推理能力等问题。 怎么又是做量化的？ 路边消息社 . 分享你不知道的行业八卦 作者 | 雷教授 来源 | 路边消息社 按照笔者的了解，在云算力端，当时除了几家互联网公司（商汤、百度、腾讯、字节、阿里），就 只有幻方有超过1万张A100芯片储备。 这还没完，另一量化巨头宽则在2月24日发布了智能学习实验室人才招聘通知，宣称该实验室将朝 着整个人工智能启航。 这真是量化人才捅了大模型的老巢？ 笔者问了身边的投资人朋友，"难道以后要去量化机构投资大模型团队了？"一些关注AI领域的投资 人的第一反应都是：什么？难道我们要找关系拜访王琛了？ 一个评论一针见血：见不到梁文锋，还见不到王琛吗？ 这个评论提及的王琛，就是九坤的创始人。 介绍王琛之前，分享一个趣事。其实在梁文锋（浙大毕业）之前，大多数做大 ...

Core Viewpoint - The emergence of DeepSeek challenges traditional beliefs about AI model development, demonstrating that a financial startup from China can innovate in AI, contrary to the notion that only large tech companies or research institutions can do so [1][4][5]. Group 1: AI Economics: Scaling Laws vs. Scale Effects - DeepSeek's success indicates a shift in understanding the barriers to AI model development, particularly reducing the constraints of computational power through algorithm optimization [8][9]. - Scaling laws suggest that increasing model parameters, training data, and computational resources leads to diminishing returns in AI performance, while scale effects highlight that larger scales can reduce unit costs and improve efficiency [10][11]. - The interplay between scaling laws and scale effects is crucial for understanding DeepSeek's breakthrough, as algorithmic advancements can enhance the marginal returns of computational investments [12][14]. Group 2: Latecomer Advantage vs. First-Mover Advantage - The distinction between scaling laws and scale effects provides insights into the competitive landscape of AI, where latecomers like China can potentially catch up due to higher marginal returns on resource investments [16][22]. - The AI development index shows that the U.S. and China dominate the global AI landscape, with both countries possessing significant scale advantages, albeit in different areas [18][22]. - The competition between the U.S. and China in AI is characterized by differing strengths, with the U.S. focusing on computational resources and China leveraging its talent pool and application scenarios [19][22]. Group 3: Open Source Promoting External Scale Economies - DeepSeek's open-source model reduces commercial barriers, facilitating broader adoption and innovation in AI applications, which can accelerate the "AI+" process [24][26]. - The open-source approach allows for greater external scale economies, benefiting a wider range of participants compared to closed-source models, which tend to concentrate profits among fewer entities [25][28]. - The potential market size for AI applications is estimated to be about twice that of the computational and model layers combined, indicating significant growth opportunities [27]. Group 4: Innovation Development: From Supply and Assets to Demand and Talent - The success of DeepSeek raises questions about the role of traditional research institutions in innovation, suggesting that market-driven demands may lead to more successful outcomes in technology development [30][31]. - The integration of technological and industrial innovation is essential for sustainable growth, emphasizing the need for a shift from a supply-side focus to a demand-side approach that values talent and market needs [32][33]. - The importance of talent incentives and a diverse innovation ecosystem is highlighted, as smaller firms may be more agile in pursuing disruptive innovations compared to larger corporations [34][36]. Group 5: From Fintech to Tech Finance - The relationship between finance and technology is re-evaluated, with the success of DeepSeek illustrating how financial firms can leverage technological advancements to enhance their competitive edge [36][39]. - The role of capital markets in fostering innovation ecosystems is emphasized, suggesting that a diverse range of participants is necessary for achieving external scale economies [38][39].

聪明投资者 . 聚焦优秀投资人和企业家，甄选高质量的内容，追求可累进的成长。更多内容可下载"聪明投资 者"APP，官网：www.cmtzz.cn 一名爱好量化投资的程序员。 排版 | 关鹤九 责编 | 艾暄 来源 | 聪明投资者 （ID：Capital-nature） 01 第一次见梁文锋，是2018年的6月份，幻方量化杭州总部。 以下文章来源于聪明投资者 ，作者永远好奇的 2018年的量化市场刚刚显露今天的格局，机构调研时挂在嘴边的那些公司的名字，包括 幻方、九坤、明汯们，背后承载的规模也仅仅是今天的零头。 当时成立3年的幻方管理客户资金约45亿，自营盘约10个亿。已经是量化的第一梯队。 作为掌门人的梁文锋一直隐形在幕后，很长一段时间业界都以为公司核心高管是另外两 位。 托一个朋友的福，围观了这次罕见的深度调研。 梁文锋走进小会议室面对面坐下时，捧着一个保温杯，穿着深蓝色的工装绒棉衬衫。很 瘦削，有点拘谨，活脱是上个世纪90年代的工程师模样。 瞬间有了跟一则趣闻对号入座的即视感：据说他买了新房却一直醉心于开发策略而无心 装修，所以在房间里支了帐篷睡觉。 同事说他除了编程，没有什么其他的爱好。 提前被安利了梁 ...