已出手近 10 家公司。 文 丨 孙海宁 编辑 丨 程曼祺 本周二（3 月 25 日），具身智能公司原力灵机宣布完成 2 亿元天使轮融资、投资方包括九坤创投后，中国头 部量化投资公司 "九坤投资" 两位创始人的一级市场科技投资计划浮出水面。 我们独家获悉，王琛和姚齐聪，九坤投资的两位创始人已联合设立早期风险投资平台 "九坤创投"，和从事量化 投资的 "九坤投资" 相互独立。该投资平台已在水下运行一年多，期间出手近 10 个项目，包括人工智能、机器 人与硬件、航空航天等领域，投资轮次均为 A 轮及 A 轮以前。筛选被投公司时，九坤创投没有设置明确的赛 道限制。 2019 年开始，九坤投资使用人工智能和大数据技术投资，建设 "北溟" 超算集群，其中包括高性能、高扩展 性的 GPU 集群。近日，九坤投资与微软亚洲研究院，联合探索基于规则的强化学习在大型推理模型中的潜 力，全面深入地分析类 R1 强化学习模型训练动态过程。 量化投资和大模型的部分技术存在交集——在强化学习方法被奉为训练 AI Agent 的关键路径前，它已经被 用来训练量化投资策略。量化行业偏好招募数学、计算机背景的研究型人才，用人需求也与 AI 公 ...

风评转向很快，量化从股市敌对势力到掀起科技国运，仅用了一年时间。 DeepSeek R1的开源，几乎拉齐了中美大模型的代差，也重塑了量化行业的公众形象。对于过去两年深 陷某种道德困境的量化私募行业而言，AI 实验室成为了当下一个不可忽视的风口。在巨大的社会价值面 前，扩招的消息一个接着一个。 宽德Will Lab招募AI工程师，鸣石创世纪AI Lab招募AI科学家，蒙玺AI Lab招募机器学习实习生，黑 翼、磐松、正定、启林、世纪前沿近期也加入AI抢人之争。 而头部量化之间，这场AI军备竞赛其实早已暗流涌动。 众所不周知的是，明汯已囤了数千张GPU卡，数万张CPU核，在金融数据的应用场景下AI算力可以达到 400P Flops；九坤更是与微软亚洲研究院复现了DeepSeek R1模型，在此之前低调建立了Data Lab、AI Lab、水滴等多个AI实验室。 看起来DeepSeek已不仅是资本市场信心重启的重要催化剂，更变成了一些量化私募的OKR，激励他们以 更重要的角色参与到时代进程里。 只是在与多位量化管理人交流后，笔者感受到一种温差：如火如荼的投产之下，各大建立AI实验室的量 化在大语言模型能力上距离De ...

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 Viewpoint - The recent surge in the Hong Kong stock market, particularly the Hang Seng Technology Index, is largely driven by a short squeeze, where short sellers are forced to cover their positions as prices rise, leading to further price increases [2][5]. Group 1: Market Dynamics - The Hang Seng Index and the proportion of short positions in the Hong Kong market have shown significant fluctuations over the past five years, with short positions dropping from 19% to 14% recently [3][4]. - At one point, short positions exceeded 21%, indicating a high level of bearish sentiment in the market [4]. - The mechanism of short selling involves borrowing stocks to sell them, with the intention of buying them back at a lower price, but the recent strong performance of the Hong Kong market has forced many short sellers to buy back at higher prices, resulting in losses [4][5]. Group 2: Sector Performance - The surge in the Hong Kong market is not limited to technology stocks; almost all sectors, including consumer, healthcare, finance, real estate, and industrials, have experienced significant gains [6]. - Specific sector performance includes: - Non-essential consumer index up by 4.64% - Essential consumer index up by 3.55% - Information technology index up by 3.54% - Real estate index up by 3.34% - Financial index up by 2.82% - Healthcare index up by 2.50% - Industrial index up by 1.97% [6]. Group 3: Broader Market Implications - The phenomenon of short covering in key sectors can create a liquidity effect that leads to a market-wide revaluation, resulting in a spiral effect of rising prices and further short covering [7]. - Investors who previously exited the Hong Kong market in favor of Indian or Southeast Asian stocks are now facing losses as the Hong Kong market rebounds [7][10]. - The article argues that the perception of A-shares being perpetually stagnant is misleading, as the underlying indices have shown consistent upward trends over the long term [11][18][20].

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].

这几天到处都能刷到关于deepseek的话题，这是国产的一个ai，经过测试目前已经达到了开源模型里的一线水平，除了高性能，它还有一个标签就是经济 实惠，它的训练费用不到600万美元，运算成本比同行低了90%以上，人送外号"ai拼多多"。 便宜又厉害的模型是怎么做出来的呢？我稀里糊涂看了不少文章，过于专业，我没看懂，这题pass。 关于deepseek的第二个新闻点就有意思了，出资研发它的竟然是国内著名量化私募机构幻方，幻方的老板梁文峰持有deepseek公司83%的股权。也正是由 于这一层关系，这几天圈子里有不少人在哀嚎，说怪不得量化私募持续不断的在a股收割，原来人家早就用ai炒股，用机枪扫射还在用长矛弓箭的散户。 对此幻方倒是回应过，说deepseek团队就是专门做通用大模型的，并没有用它来炒股。话是这么说，但研究deepseek的资金和人才很多都是从幻方量化输 送过去的，能研究出成功ai模型的团队，在股票模型的开发上肯定也不弱。毕竟ai模型现在没有一个挣钱，但炒股是真挣钱。 坊间一直传闻幻方量化曾对超算集群系统投入 10 亿元，搭载了超 1 万张英伟达 A100 显卡。 然后就是今天又爆出一个热搜，说雷军 ...

"如果每天和开发者打交道，你不会感觉这个行业停滞或变冷。" 文丨程曼祺 贺乾明 扫描图中右下角二维码，可收听播客。* 这是《晚点聊 LateTalk 的第 80 期节目，欢迎在小宇宙、喜马拉雅、苹果 Podcast 等渠道关注、收听我们。 《晚点聊 LateTalk》是《晚点 LatePost》 推出的播客节目，在文字报道之外，用音频访谈形式捕捉商业世界变化的潮流和不变的逻辑，与这 其中的人和故事。 OpenAI 发布新模型 o1 后的第二天，我们邀请了硅基流动创始人袁进辉与我们分享了 o1 的技术意义，也讨论了今年 1 月至今，袁进辉观察 到的 AI 开发者社区变化。 o1 的一个重要变化就是增加了分配给推理（inference，即大模型的使用）阶段的算力，推理阶段计算（test-time compute）重要性提升。 而袁进辉今年初创立的硅基流动（SiliconFlow）就是一家做推理加速优化的 AI Infra（中间层软件）公司。他是一位连续创业者，曾在 2017 年创立一流科技（OneFlow），在 2023 年加入王慧文组建的大模型创业公司光年之外，成为联合创始人。（袁进辉的上两段创业故事，可 听 ...