1）雅化集团 （幻方量化参与公司电话会议） 调研纪要：雅化集团坚持双主业发展战略，报告期内锂盐长协客户订单需求稳定，产线逐步建设投产， 民爆业务产能有效释放并积极拓展爆破业务。2024年全年实现营业收入77.16亿元，同比下降35.14%， 净利润2.57亿元，同比上升539.36%；2025年一季度实现营业收入15.37亿元，同比下降17.03%，净利润 8,246.44万元，同比上升452.32%。公司拥有津巴布韦卡玛蒂维锂矿68%控制权，年处理锂矿石230万 吨，现有氢氧化锂产能6.3万吨，碳酸锂产能3.6万吨，预计2025年将新增3万吨氢氧化锂产能。客户包括 特斯拉、松下、LGES等知名企业，海外客户订单占比较大。民爆业务覆盖四川、西藏、新疆等20余个 省市区，客户包括中铁建、中电建等大型央企。公司自2013年收购新西兰红牛公司后，加大在非洲和澳 洲区域民爆业务的拓展力度。 根据市场公开信息及4月30日披露的机构调研信息，知名私募幻方量化近期对1家上市公司进行了调研， 相关名单如下： 机构简介： 幻方量化(九章资产)是一家依靠数学与计算机科学进行量化投资的对冲基金公司。创始团队于2008年开 始致力于 ...

这一年，D.E. Shaw 为计算机行业做了两个贡献。一个副总裁带队，做出了当时罕见的免费电子邮件产 品 Juno，成功上市；另一个副总裁离职，带着自己和老板讨论产生的好点子开车去了西雅图，做出了全 世界的电商鼻祖、市值超过 20000 亿美元的亚马逊。 30 年后，又有一家量化公司的 "副业" 影响整个计算机行业：管理数百亿元的中国头部量化公司幻方， 推出大语言模型 DeepSeek R1，没花一分钱营销就震撼全球，用户涌来的速度甚至快过早年的抖音。 贝索斯创办亚马逊，或者梁文锋造出 DeepSeek 的主要原因自然不是因为他们做过量化，而是因为他们 骨子里都是创业者。但量化投资这个极度追求人才密度且极度保密的行业文化，确实提供了适合大模型 研发的环境。 招来一群聪明人不必然导致创新，叠加一个简单的环境才够。量化公司证明了这一点，DeepSeek 则证明 这也适用于大模型研发。 剥离主观因素，在数据里挖掘规律 从十万次交易到千亿参数的 AI 进化。 文 丨 孙海宁 编辑 丨 黄俊杰 1994 年，量化公司是当时最神秘最热门的技术公司，他们雇用数学家和物理学家，成批买来高性能计算 机做交易。这个行业里的标杆公 ...

Core Insights - The article discusses the challenges faced by large model companies in securing funding and reducing costs, highlighting the competitive landscape introduced by quantitative firms like Jiukun and their collaboration with Microsoft to replicate DeepSeek-R1 [1][2][13] - It emphasizes the growing interest of quantitative firms in the AI space, driven by the need for algorithmic support and the accumulation of high-performance chips [5][6][12] Group 1: Industry Dynamics - Quantitative firms are increasingly entering the large model sector, with Jiukun's successful replication of DeepSeek-R1 indicating a shift in competitive dynamics [1][12] - The article notes that the success of figures like Liang Wenfeng has led to a rush among investors to find similar opportunities, despite the inherent difficulties in replicating such success [13][16] - The fear of missing out (FOMO) is prevalent among investors, as they worry about missing investment opportunities in top-tier projects [14][15] Group 2: Company Strategies - Jiukun has established multiple internal laboratories to support its quantitative research, indicating a strong foundation for its entry into the AI field [5][6] - The accumulation of over 10,000 A100 chips by companies like Huansheng demonstrates the importance of computational power in the quantitative finance sector [7][8] - The article suggests that while many quantitative firms are entering the large model space, most are still in the early stages and may not achieve the same level of success as leading firms [16][17]

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 ...