近期北美算力行业发生了哪些重要变化？ 近期北美算力行业出现了显著变化，特别是在个人助理级的 Agent 领域。 Cloud Code 和 Cloud Boat 等产品火爆出圈，反映出 AI 进入了 Agent 时代。 2026 年预计将是 AI 应用真正爆发的一年，包括豆包手机等 AI 助理产品将在 2026 年第一至第二季度正式开售。这些 Agent 产品将带来大量 Token 调用， 对算力需求非常强烈。整个算力产业链各个方向都出现了不同程度的涨价情况， 这反映出需求端非常旺盛，而供给端相对紧张。 Cloud Code 在北美市场表现如何？ Cloud Code 在北美市场表现极为突出，其代码理解能力强、上下文长度长以 及写出来的代码像人类编写一样，使其成为核心能力之一。自 2025 年 12 月 推出以来，用户同比增长超过一倍，日活用户数环比快速增长，仅半年时间内 Agent 助推算力需求增长 20260128 摘要 AI 应用爆发在即：预计 2026 年 AI Agent 产品如豆包手机将大量调用 Token，驱动算力需求激增，算力产业链各环节普遍涨价，反映出供不 应求的紧张态势。 Cloud Cod ...

Clawdbot 和 Cowork 将如何引领应用落地的标准范式 20260128 摘要 AI 技术通过编程模型、视觉模型和强化学习，显著提升了工作流效率， 尤其在编程、医疗和金融等垂直领域，预计 2026 年这些领域的数据需 求将迎来爆发式增长。 2026 年 A 股市场预计将迎来 Agent 产品的大爆发，AGI 带来的用户量 增长将缓解市场对 AI 泡沫和 ROI 的担忧，从而强化对算力基础设施的投 资。 AI 技术对软件行业产生冲击，传统软件 UI 界面可能被替代，依赖标准化 功能和 UI 界面的公司如 ServiceNow、CRM、Adobe 等面临挑战，而 Data Infra 类公司受影响较小。 大模型通过改变工作流程，提高企业降本增效能力，并可能导致大规模 裁员，从而与传统软件公司共同竞争人力资源预算市场。 软件公司面临的主要挑战在于场景壁垒和商业逻辑的强弱，按人头收费 模式将逐步被按消费量收费模式取代，导致毛利率下降。 Q&A 今年（2026 年）AI 技术的发展趋势如何？有哪些值得关注的变化？ 今年（2026 年）AI 技术的发展呈现出几个显著的趋势。首先，AI 模型及 Agent 的应用 ...

杨晓峰 华福证券 AI 互联网传媒首席: 然后到了这个到了这个 Cloud Code 这一块，就是等他们把这个最新的 Cloud Code 这 个模型推出来之后，相当于说是大家突然发现，如果用 Cloud Code 来写代码或者写程序 的话。首先他对于整个环境里面那些东西，他的理解是非常完整的。然后就是他写出来代 码，可能你只需要花一些时间去 review 它就可以了，就是你只需要去稍微去判断一下， 说他这个写的对不对，或者说是怎么样就可以了。当然，就是你不需要说自己花很多的这 个精力再去到这个写代码上面去，就这样一个是一个超，就是一个节点，应该说一个节点 就是 AI 它这个能替代你的这部分，就是能替代写代码这个工作的这个超过了，就是你需 要去为它真正所花费的精力、花费的时间，就是确实是变成了一个节省精力的这样一个过 程。 那么这个是他的一个它的一个特征，然后它的主要能力，就是它可以当然它可，首先是最 简单的，它可以跟你对话对吧，然后它可以操作你的这个，操作你选定的这个范围内的这 个文件夹里面的文件。它可以创建或者是修改这些文件。然后，它还可以通过这个 MCP 的方式，就是去控制，比如说像你的这个浏览器，或者 ...

Summary of Key Points from Conference Call Industry Overview - The AI application landscape is experiencing a stark contrast between domestic and international markets, with increasing contradictions between models and applications [1] - The semiconductor industry is in a significant expansion phase, driven by TSMC's increased capital expenditure forecast of 30%-40%, indicating strong demand confidence for the next two to three years [1][4] - Storage prices are rising rapidly due to resource factors, while power equipment supply and capacity issues may become long-term constraints [1][5] Core Insights and Arguments - TSMC's capital expenditure is projected to exceed $50 billion, marking the largest increase in recent years, which alleviates concerns about a peak in capital spending [4] - The AI industry in the US and China shows a clear divergence in stock performance, attributed to differences in technological development paths and market demands [3] - Multi-modal models, such as Google's NanoBanana, are expected to transform from generative tools to productivity tools by 2025, significantly enhancing potential applications in programming and healthcare [1][6] Storage Demand Changes - There is a noticeable shift in storage demand from training to inference, driven by the development of reasoning models that require extensive context information [7][8] - The demand for SSDs is expected to grow in tandem with the Agent market stabilizing, reflecting a critical change in storage needs [8] AI Model Development - The leading companies in foundational models are Anthropic, OpenAI, and Gemini, with significant advancements in multi-modal models enhancing AI's ability to process visual information [6][9] - Reinforcement learning is being integrated into vertical models, allowing AI to mimic human problem-solving approaches, which is particularly beneficial in specialized fields [10][11] Market Focus Differences - The domestic market is more focused on consumer (C-end) development, with major players like Alibaba, ByteDance, and Tencent leading the competition, while the overseas market emphasizes business-to-business (B-end) development [12] - Alibaba's Tongyi Qianwen integrates various traffic sources into a single entry point, enhancing product parsing capabilities and potentially stabilizing stock price fluctuations [14] Competitive Strategies - ByteDance's approach involves consolidating AI functions within its operating system, while Alibaba's strategy focuses on integrating its ecosystem into a super app format [13] - Tencent is transforming mini-programs into Agents, distributing AI functionalities across applications [13] International AI Company Developments - OpenAI and Anthropic have reached valuations in the tens of billions, with Anthropic gaining significant market attention due to its focus on programming workflows [15][17] - Google's release of automated node editing tools is impacting traditional workflow tools, although its primary focus remains on consumer applications [16] Investment Considerations - Companies like Google, Tencent, Alibaba, and Kuaishou are seen as clear investment targets due to their self-owned traffic ecosystems and proprietary model capabilities [21] - In the B2B application space, companies like Figma and Adobe need to demonstrate resilience against AI disruptions, while those focused on vertical model development are less affected [21]

Memory Updating Mechanisms for Agents - Explicit memory updating involves directly instructing the agent to remember specific information, similar to how cloud code functions [2][5][6][29] - Implicit memory updating occurs through the agent learning from natural interactions with users, revealing preferences without explicit instructions [7][19][29] Deep Agent CLI and Memory Management - Deep agents have a configuration home directory with an `agent MD` file that stores global memory, similar to Claude's `cloud MD` [3][4][6] - The `agent MD` files are automatically loaded into the system prompt of deep agents, ensuring consistent memory access [6] - Deep agent CLI allows adding information to global memory using natural language commands, updating the `agent MD` file [5] Implicit Memory Updating and Reflection - Agents can reflect on past interactions (sessions or trajectories) to generate higher-level insights and update their memory [8][9][10][28] - Reflection involves summarizing session logs (diaries) and using these summaries to refine and update the agent's memory [11][12] - Accessing session logs is crucial for implicit memory updating; Langsmith can be used to store and manage deep agent traces [13][14][15] Practical Implementation and Workflow - A utility can be used to programmatically access threads and traces from Langsmith projects [21] - The deep agent can be instructed to read interaction threads, identify user preferences, and update global memory accordingly [24][25] - Reflecting on historical threads allows the agent to distill implicit preferences and add them to its global memory, improving future interactions [26][27][28]

Context Engineering Principles for AI Agents - The industry recognizes the increasing task length AI agents can perform, with task length doubling approximately every seven months [2] - The industry faces challenges related to context rot, where performance degrades with longer context lengths, impacting cost and latency [3][4] - Context engineering, involving offloading, reducing, and isolating context, is crucial for managing context rot in AI agents [8][9][10] Context Offloading - Giving agents access to a file system is beneficial for saving and recalling information during long-running tasks and across different agent invocations [11][15][18] - Offloading actions from tools to scripts in a file system expands the agent's action space while minimizing the number of tools and instructions [19][22] - Progressive disclosure of actions, such as with Claude skills, saves tokens by selectively loading skill information only when needed [26][30] Context Reduction - Compaction, summarization, and filtering are techniques used to reduce context size and prevent excessively large tool results from being passed to the language model [32][33][39] - Manis compacts old tool results by saving them to a file and referencing the file in the message history [34] - Deep agents package applies summarization after a threshold of 170,000 tokens [38] Context Isolation - Context isolation, using separate context windows or sub-agents for individual tasks, helps manage context and improve performance [10][39][40] - Sub-agents can have shared context with the parent agent, such as access to the same file system [42] Tool Usage - Agent harnesses often employ a minimal number of general, atomic tools to save tokens and minimize decision-making complexity [44] - Cloud code uses around a dozen tools, Manis uses less than 20, and the deep agent CLI uses 11 [24][25][44]

Summary of Key Points from Conference Call Industry and Company Overview - The conference discusses advancements in the AI application industry, particularly focusing on the Cloud Code tool developed by Anthropic, which has significantly impacted programming efficiency and company valuation, now estimated between $170 billion and $180 billion [1][2][3]. Core Insights and Arguments - **Cloud Code Tool**: This tool enhances programming efficiency through context engineering, utilizing a virtual machine-like approach for context management and sandbox technology for user experience optimization. It leverages user data accumulated over three years to improve product performance [1][3][4]. - **Cost Efficiency**: AI applications, particularly through tools like Cloud Code, allow teams to complete tasks at a fraction of the traditional cost, exemplified by the ability to create a company website for just $35 in one hour [1][5]. - **AIGC Applications**: The most active area in AI-generated content (AIGC) is text processing, while image generation growth has slowed. Multimedia generation, driven by models like Google Gemini 2.5, is rapidly expanding, especially in e-commerce and live streaming [1][8][9]. - **AI App Market**: The AI app market is growing quickly but remains in its infancy, lacking a dominant app. The business model is shifting from traditional subscriptions to usage-based billing, emphasizing high-quality data over ad revenue [1][10]. - **Context Management**: Scene intelligence addresses the limitations of large models in context management, enhancing the precision of information services, such as advanced meeting record systems [1][11][12]. - **Industry-Specific AI Apps**: Despite the capabilities of large models like ChatGPT, specialized industry AI apps are necessary due to the complexity of high-quality prompt writing and context management [1][6]. - **Development Stages of AI Apps**: Most AI apps are currently at the third stage of development, indicating maturity in cloud infrastructure and context management, with some companies exploring more advanced paradigms [1][7]. Additional Important Insights - **AIGC Forms**: AIGC primarily manifests in four forms: pure text, images, multimedia (video and audio). Text applications are the most competitive, while image generation has seen a decline in demand [1][8][9]. - **User Data Utilization**: The extensive user data collected allows Cloud Code to better understand user intent, further enhancing product performance [4]. - **Market Trends**: The AI app market is characterized by a lack of leading apps, with significant potential for new entrants. The shift to usage-based pricing models reflects a broader trend in the industry [1][10]. - **Challenges in Multimedia**: The multimedia segment faces challenges such as copyright issues and model alignment, but it remains one of the fastest-growing areas [1][9]. - **AI in Document Processing**: AI tools significantly improve document processing efficiency, converting unstructured documents into structured formats, enhancing speed and accuracy [1][22]. - **Future Outlook**: The next two to three years are expected to see a rise in agent-enabled apps, similar to the mobile internet boom in the early 2010s, with substantial investment interest [1][26]. This summary encapsulates the key points discussed in the conference call, highlighting the advancements and trends in the AI application industry, particularly focusing on the impact of the Cloud Code tool and the evolving market dynamics.