Jenny 是一个同时理解中美文化、创业、研究与投资的人。她从小在美国长大，2021 年加入 OpenAI，并在 ChatGPT 爆火一周后选择离开，合伙创立了自己的基 金。前一阵她回国，我们借机聊了聊中美 AI 创投之间的差异。 P.S. 本期节目录制于 2025.12.22。几天后，Manus 被微软收购的消息披露。回头再看，Jenny 分享的许多投资思路和对美国市场的判断，其实都有所映照。 本期播客原文约 23000 字，本文经过删减整理后约 7700 字。 曲凯 ：你这两年观察到的几个最主要的 milestones 是什么？ Jenny ：在 23 年，中美都有一个非常明确的共识：投大模型。在美国，就是持续给 OpenAI、Anthropic 这样的公司投钱。这些公司这两年发展得很快，也确实拿 走了行业里大部分的利润。 23 年的另一个共识是，很多人觉得应用只是「套壳」，很轻、很薄，没什么价值。 但到了 24、25 年，这个判断开始发生变化，因为很多应用层公司逐渐做出了自己的特色和护城河，比如 Cursor、Perplexity。 最近两年，Agent 很火。但在真实场景中，AI Agent 的落地依 ...

一水 发自 凹非寺 量子位 | 公众号 QbitAI 我并不认为Scaling Law已经完全结束了 。 正当学生Ilya为Scaling Law"泼下冷水"时，他的老师、AI教父Geoffrey Hinton却毅然发表了上述截然相反的观点。 这一场面一出，我们不禁回想起了两件有趣的事。 一是Ilya几乎从学生时代起就坚信Scaling Law，不仅一抓住机会就向身边人安利，而且还把这套理念带进了OpenAI。 可以说，Ilya算是Scaling Law最初的拥趸者。 二是Hinton后来在回顾和Ilya的相处时，曾大肆夸赞Ilya"具有惊人的直觉"，包括在Scaling Law这件事上，Hinton曾坦言： 当时的我错了，而Ilya基本上是对的。 比如Transformer确实是一种创新想法，但实际上起作用的还是规模，数据的规模和计算的规模。 但是现在，这对师徒的态度却来了个惊天大反转。 所以，这中间到底发生了什么？ Scaling Law不死派：Hinton、哈萨比斯 其中，最大的挑战无疑是数据缺失问题。 大部分高价值数据都锁在公司内部，免费互联网数据已基本耗尽。 而这个问题将由AI自行解决，即模型通过推 ...

Core Insights - The year 2025 marks a significant turning point for AI, transitioning from curiosity in 2024 to profound societal impact [1] - Predictions from industry leaders suggest that advancements in AI will continue to accelerate, with Sam Altman forecasting the emergence of systems capable of original insights by 2026 [1][3] - The debate around the Scaling Law continues, with some experts asserting its ongoing relevance and potential for further evolution [12][13] Group 1: Scaling Law and Computational Power - The Scaling Law has shown resilience, with computational power for training AI models growing at an exponential rate of four to five times annually over the past fifteen years [12][13] - Research indicates a clear power-law relationship between performance and computational power, suggesting that a tenfold increase in computational resources can yield approximately three times the performance gain [13][15] - The concept of "AI factories" is emerging, emphasizing the need for substantial computational resources and infrastructure to support AI advancements [27][31] Group 2: Breakthroughs in AI Capabilities - The SIMA 2 project at DeepMind demonstrates a leap from understanding to action, showcasing a general embodied intelligence capable of operating in complex 3D environments [35][39] - The ability of AI models to exhibit emergent capabilities, such as logical reasoning and complex instruction following, is linked to increased computational power [16][24] - By the end of 2025, AI's ability to complete tasks has significantly improved, with projections indicating that by 2028, AI may independently handle tasks that currently require weeks of human expertise [41] Group 3: Future Challenges and Considerations - The establishment of the Post-AGI team at DeepMind reflects the anticipation of challenges that will arise once AGI is achieved, particularly regarding the management of autonomous, self-evolving intelligent agents [43][46] - The ongoing discussion about the implications of AI's rapid advancement highlights the need for society to rethink human value in a world where intelligent systems may operate at near-zero costs [43][46] - The physical limitations of power consumption and cooling solutions are becoming critical considerations for the future of AI infrastructure [31][32]

Core Insights - The founder and CEO of Kimi, Yang Zhilin, announced that the company currently holds over 10 billion yuan in cash and is not in a hurry to go public [1][2] - Kimi recently completed a $500 million Series C funding round, led by IDG with a $150 million investment, and the post-money valuation reached $4.3 billion [1][2] - Kimi's paid user base saw a month-over-month growth rate of 170% from September to November 2025, potentially reaching around 1.7 million users by the end of the year [2][5] Financial Performance - Assuming an initial paid user count of 100,000 at the beginning of 2025, the estimated monthly revenue could reach approximately 85 million yuan by year-end, with API revenue potentially bringing total monthly revenue close to 100 million yuan [2][5] - The company has a significant cash reserve, which allows it to avoid rushing into an IPO, indicating a strong financial position to face competition in 2026 [2][5] Product Development - Kimi plans to launch the K2 and K2 Thinking models in September and November 2025, focusing on explainability in reasoning processes and complex logical reasoning [1][2] - The company has been actively releasing new agent functionalities since May 2025, contributing to a substantial increase in commercial performance [5][6] Strategic Goals - Kimi aims to surpass leading companies like Anthropic to become a world leader in AGI, with plans to enhance the K3 model's capabilities significantly [6][7] - The company is focusing on vertical integration of model training and agent products, aiming for a unique user experience and substantial revenue growth [7][8] Future Plans - A reward scheme for the K2 Thinking model and subsequent products is expected to be established before the 2026 Spring Festival, with average incentives projected to be 200% of 2025 levels [2][6] - The company intends to utilize the Series C funding to aggressively expand GPU resources and accelerate the training and development of the K3 model [6][7]

2025年的尾声，智能体初创公司Manus宣布被Meta收购，智谱华章与MiniMax前后脚赴港上市，其中智 谱最快明年1月8日在港交所挂牌。 热度裹挟之下，大模型参赛选手正经历一轮剧烈的"适者生存"筛选：创业公司或冲刺上市抢滩资本市 场，或收缩战线聚焦垂直场景深耕，或黯然退出基座竞赛；大厂则凭借算力、数据与生态优势全面压 境，在技术迭代与场景落地中加速收割市场份额。 看向未来，旧的规模化竞赛已见顶，新范式的探索之路刚刚启程，谁能率先突破Scaling Law的瓶颈， 谁就能在下一轮格局中占据先发优势。还有从业者对记者表示：如果说2025年关注的是AI模型能做什 么，那么到2026年更关注AI到底该怎么样去赚钱，并且是产业化地赚钱。 创业赛道"生死分化" 国内市场中，"六小龙"概念已成为过去时，基础模型创业赛道彻底分化，互联网大厂正式发力；国外市 场中，包括OpenAI、Google、Anthropic等头部厂商在基础模型领域交替领先，同步寻找在应用端的机 会，顶级研究人员在Scaling Law陷入增长瓶颈期后，尝试探索新的技术范式。 红杉资本中国基金合伙人郑庆生认为，2025年的AI赛道已带有转折的意味。 ...

机器之心发布 本文作者为摩尔线程天使投资人、中国初代 AI 投资人王捷。他于今年 8 月发表了《浮现中的 AI 经济》一文，对即将到来的 AI 经济进行了 展望和解读。本篇文章是他近期对当前 AI 的思考的小结。 关于 AI 经济的四十个问题 《浮现中的 AI 经济》（以下简称 "文章"）发表以来，AI 行业继续发生了众多大事，OpenAI 牵头的千亿美金 "循环交易" 引发 "AI 泡沫论" 大讨论，模型公司估值 来到数千亿美金级别，而 Gemini 3 和 GPT5.2 等新发布模型版本又持续体现了能力进步，中国模型也持续在开源领域保持全球领先。 我们看到，与 AI 相关的历史事实，正继续以 " 非 线 性、非均匀 " 的特征往前发展：Scaling Law 并未收敛，AI 行业继续呈现加速发展的特点，与 AI 相关的经济 活动规模来到了前所未有的量级；同时，历史进程呈现出 "非均匀" 的面貌，虽然人们是在同一个时空下，但是与 AI 有关的经济社会活动，和与 AI 无关的经济社 会活动，看起来不在同一个历史进程中，前者正以强大的动能迅猛往前发展，而后者维持着我们所熟悉和习惯的、传统工业经济的节奏和特点。 ...

文｜王欣 编辑｜苏建勋 在2025年火热的具身智能创业潮中，"它石智航"有着绝对吸睛的实力。 这是一个由国内智驾黄埔军校核心高管组成的"梦之队"。它石智航首席执行官陈亦伦曾在华为车BU担 任自动驾驶系统CTO；首席科学家丁文超曾是华为"天才少年"。董事长李震宇则担任过百度智能驾驶事 业群原总裁，打造过全球最大的Robotaxi出行平台"萝卜快跑"。 在自动驾驶行业，陈亦伦、李震宇均是带过千人团队、打过胜仗的"名将"，两人的合作创业，也让它石 智航迅速成为资本的宠儿。在今年3月，它石智航以1.2 亿美元的融资额，创下中国具身智能行业天使轮 最大融资额纪录。 资本看重它石智航的技术积累和人才储备。线性资本创始人兼 CEO 王淮曾这样评价它石智航："他们能 将之前在华为做自动驾驶的很多软硬件打磨的经验，结合大模型的思考和推理能力，落实在具身机器人 身上。" 可在天使轮融资破纪录，创始团队如此豪华的状况下，不同于其他具身智能公司高频地披露出货量与技 术突破，2025年一年，它石智航鲜少公布进展。 12月19日，它石智航办了一场线上发布会，持续时间只有短短40分钟，展示的成果，是"全球首个完成 刺绣的机器人"。 为什么 ...

Core Insights - AI is transitioning from merely providing answers to actively thinking, generating data, and executing tasks, indicating a fundamental shift in its capabilities [1][4][11] Group 1: AI's Evolving Capabilities - AI is developing reasoning abilities, leading to a reduction in hallucinations, which were a significant issue in chatbots [5][11] - The reasoning process involves understanding language contextually rather than converting sentences into logical symbols, allowing for more natural connections between words [6][9] - AI is moving from passive response to active execution, creating a new collaborative relationship between humans and machines [4][11] Group 2: Self-Learning and Data Generation - The next generation of AI will not rely on human-provided data but will generate its own training data through self-learning [15][20] - Hinton cites AlphaZero as an example of AI that learns through self-play, suggesting that AI could excel in fields like mathematics by generating infinite training data [16][17] - This shift represents a fundamental change in training paradigms, moving from external data input to internal self-driven learning [20][21] Group 3: Role Transformation in Collaboration - The concept of "agents" is emerging, where AI can understand tasks, break down processes, and execute them autonomously [23][29] - In fields like healthcare and education, AI is beginning to take over roles traditionally held by humans, enhancing efficiency and accuracy [25][26] - As AI becomes more proactive, the human role shifts from execution to decision-making, requiring a redesign of collaborative frameworks [31][32]

Group 1 - The concept of "Vibe Coding" has emerged, suggesting a new programming approach that emphasizes feeling and embracing exponential growth, leading to a broader trend of "Vibe Everything" in AI [2] - There is a divide in perception regarding "Vibe," with some viewing it as a refreshing product philosophy while others criticize it as a superficial trend that obscures the true essence of AI products [2] - The term "Vibe" reflects a strong narrative appeal, resonating with the desire for transformative change in the AI landscape, indicating its continued relevance in the future [2] Group 2 - The humanoid robot sector is experiencing a valuation surge despite discussions about a potential bubble, with significant capital inflow and a shift towards more conservative funding strategies among companies [6] - The focus on "scene" applications for humanoid robots has intensified, with education and performance being the most viable commercial scenarios, while the pursuit of commercial viability may not be the primary goal for the sector [6] Group 3 - The phrase "Prompt Engineering is Dead" has gained traction, suggesting a shift towards "Context Engineering," which encompasses a broader range of information and tools necessary for AI tasks [8][9] - Context Engineering is seen as a significant advancement, attracting investment and fostering the development of various AI tools, indicating a potential shift in the industry narrative [9] Group 4 - Huang Renxun's assertion that "China will win the AI race" highlights the competitive landscape between China and the U.S., emphasizing China's advantages in developer scale, market size, and infrastructure [12][13] - Huang's comments may also serve as a strategic move to influence U.S. policy regarding AI, aiming to maintain Nvidia's leadership position in the global market [12] Group 5 - Elon Musk and Satya Nadella predict the disappearance of traditional smartphones and apps, suggesting a transition to intelligent agents that could replace conventional software applications [15][16] - The emergence of new devices like the "Doubao phone" indicates a shift in how technology is being approached, with a focus on user interface and system control [16] Group 6 - Sam Altman's response to skepticism about OpenAI reflects a broader divide in opinions regarding the AI bubble, with concerns about the company's ability to deliver on its ambitious revenue projections [19][20] - OpenAI's projected revenue growth and the potential economic implications of AI's impact on employment and inflation are critical factors in assessing the sustainability of the AI market [21] Group 7 - The U.S. faces a potential electricity shortage that could impact AI infrastructure, with projections indicating a significant power gap by 2028 if supply does not keep pace with demand [23][24] - Major tech companies are exploring nuclear energy as a solution to their power needs, highlighting the intersection of AI development and energy infrastructure challenges [24] Group 8 - The debate surrounding the limitations of large language models (LLMs) continues, with experts arguing that scaling may not yield significant advancements and calling for a return to foundational research [27][28] - Despite criticisms, the push for larger models persists, indicating ongoing investment and interest in scaling within the AI community [28] Group 9 - The term "Slop" has been designated as the word of the year, representing the proliferation of low-quality AI-generated content, which poses challenges for content ecosystems [31][32] - The rise of AI-generated adult content is projected to grow significantly, raising questions about the implications for traditional content creation and quality standards [32]

Investment Rating - The report maintains a "Recommended" investment rating for the computing chip industry, particularly focusing on domestic GPU manufacturers [2]. Core Insights - The report emphasizes that the development of large models follows the "Scaling Law," indicating a rigid expansion of computing power demand. This is supported by quantifiable data on AI application deployment and computing consumption, establishing a commercial link where "computing power is production material" [6]. - The GPU industry is characterized by a concentrated market structure, with major players like NVIDIA dominating the landscape. The report highlights the ongoing strategic partnerships between cloud giants and NVIDIA, reinforcing the latter's core position in AI infrastructure [6][7]. - The report analyzes the domestic GPU manufacturers' response to U.S. export restrictions, detailing their technological advancements and market strategies. Companies like Cambricon, Haiguang Information, Moore Threads, and Muxi are highlighted for their efforts to catch up with international standards [6][7]. Summary by Sections 1. GPU's Role in AI - GPUs excel in parallel computing, making them suitable for AI acceleration. The architecture of GPUs allows for simultaneous processing of vast amounts of data, significantly reducing training times for AI models [11][12]. - The GPU industry value chain is primarily concentrated in the midstream, where AI chip demand drives market growth. The report notes that the global GPU market is expected to reach 1,051.54 billion yuan by 2024, with a significant portion attributed to AI computing GPUs [24][29]. 2. Global AI Investment Trends - Major global tech companies are increasing their investments in AI, with NVIDIA maintaining a dominant position. The report cites that NVIDIA holds a 98% market share in the data center GPU segment, underscoring its competitive edge [21][35]. - The report indicates that the AI investment cycle is achieving a closed loop, with companies like Google and Microsoft ramping up their capital expenditures significantly to support AI infrastructure [46][50]. 3. Domestic GPU Development - The report discusses the urgency for domestic GPU manufacturers to achieve self-sufficiency in light of U.S. export controls. Companies are making strides in product development and market entry, with varying degrees of commercial success [6][7]. - The report highlights the financial trajectories of domestic firms, noting that Haiguang Information achieved profitability in 2021, while Cambricon is expected to reach profitability by Q4 2024 [6][7]. 4. Market Projections - The report forecasts that the global GPU market will grow to 3,611.97 billion yuan by 2029, with China's share increasing from 15.6% in 2024 to 37.8% by 2029. AI computing GPUs are projected to be the core growth driver [24][29]. - The report anticipates that the demand for data center GPUs will continue to surge, with a projected market size of 663.92 billion yuan by 2029, reflecting a compound annual growth rate of 70.1% [29][31].