Core Insights - The article posits that 2026 will mark the transition of AI from hype to practical application, focusing on deploying lightweight models in real-world scenarios and integrating AI into human workflows [3][4]. Group 1: AI Development Trends - The industry is shifting from large-scale model expansion to new architectural research, emphasizing targeted deployment and collaboration tools that enhance human work [4]. - Many researchers believe the AI industry is nearing the limits of Scaling Law, indicating a need for new approaches beyond merely increasing model size [9]. - Smaller, fine-tuned language models (SLMs) are expected to become standard tools for mature AI enterprises by 2026 due to their cost and performance advantages [10]. Group 2: World Models and Gaming - 2026 is anticipated to be a pivotal year for world models, which learn how objects interact in three-dimensional space, enabling predictive capabilities [14][15]. - The gaming industry is projected to see significant growth, with the world model market expected to increase from $1.2 billion in 2022 to $276 billion by 2030, driven by the technology's ability to create interactive environments [16]. Group 3: Agent Integration and Automation - The introduction of Model Context Protocol (MCP) is seen as a key development that will facilitate the integration of AI agents with real-world systems, potentially marking 2026 as the year these agents transition from demonstration to practical application [18][19]. - There is a belief that AI will enhance rather than replace human workflows, with new job opportunities emerging in AI governance, transparency, and data management [21]. Group 4: Physical AI and Market Adoption - Advances in small models, world models, and edge computing are expected to drive the adoption of physical AI applications, with wearable devices becoming a cost-effective entry point for consumers [24]. - The market for physical AI, including robotics and autonomous vehicles, is projected to grow, although training and deployment costs remain high [24].

复盘美股科技巨头过去三年股价走势，AI叙事不断递进 2023年OpenAI领先全球开启AI加速度，微软受益于OpenAI独家合作，估值抬升明显。2024年市场低估 模型进步空间，叙事转向推理侧，认为应用公司最优，Meta坐拥社交垄断生态(潜在Agent入口)和广告 场景，股价为除英伟达以外PE唯一抬升的巨头。(24年2月，英伟达业绩会估计数据中心收入约40%来自 推理。)同年云厂商由于大幅增加资本开支但供给受限，云收入传导有延迟，三大CSP估值略有所回 落。2025年模型差距与OpenAI明显收敛，谷歌后来居上，生态优势为市场追逐。26年该行认为Scaling Law将持续，模型厂商打开差异化应用市场，模型推理侧需求或进入放量拐点。模型和算力或为最优投 资方向。 2025年四家巨头Capex同比增长50%以上，26年该行测算Capex将持续实现30%以上增速 国信证券发布研报称，模型架构持续演化，多模态与长文本为Agent爆发奠定基础。当前大模型厂商商 业化路径分化，推理侧需求有望于2026年放量，并驱动SaaS市场格局重塑，编程、Agent等应用场景率 先实现商业化突破。 国信证券主要观点如下： 2025年 ...

2026年01月04日 证券研究报告 | 2026年01月05日 人工智能行业专题（14） 大模型发展趋势复盘与展望 行业研究 · 行业专题 互联网 · 互联网Ⅱ 投资评级：优于大市（维持） 0755-81982651 证券分析师：张伦可 证券分析师：张昊晨 zhanglunke@guosen.com.cn zhanghaochen1@guosen.com.cn S0980521120004 S0980525010001 请务必阅读正文之后的免责声明及其项下所有内容 1 核心观点 请务必阅读正文之后的免责声明及其项下所有内容 • 复盘美股科技巨头过去三年股价走势，AI叙事不断递进。23年OpenAI领先全球开启AI加速度，微软受益于OpenAI独家合作， 估值抬升明显。24年市场低估模型进步空间，叙事转向推理侧，认为应用公司最优，Meta坐拥社交垄断生态（潜在Agent入 口）和广告场景，股价为除英伟达以外PE唯一抬升的巨头。 （24年2月，英伟达业绩会估计数据中心收入约40%来自推理。） 同年云厂商由于大幅增加资本开支但供给受限，云收入传导有延迟，三大CSP估值略有所回落。25年模型差距与OpenAI明显 ...

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 ...