技术趋势2026 C Deloitte. Insights | 执行摘要 | 02 | | --- | --- | | 创新的复合效应 | 04 | | 物理AI：探索AI和机器人的融合 | 09 | | 未雨绸缪：为数字员工做好准备 | 21 | | 积极反思:优化AI基础设施策略 | 33 | | 脱胎换骨：重构一个AI原生技术组织 | 43 | | 走出困境：使用Al进行网络防御 | ਦੇਤੋ | | 拨开迷雾：Al进阶过程中值得追踪的技术趋势 | 62 | 盘 网 深 技术趋势2026 | 执行摘要 执行摘要 去年的《技术趋势》报告预测，Al将如同电力一 般，成为一种基础要素，无缝融入各类产品和服务 之中。今年这份《技术趋势》报告（第17版年度报 告）印证了这一假设。如今，企业技术的各个领域 都受到Al的影响，对智能运营的需求影响着从计算 硬件到实体机器人技术等方方面面的决策。去年, 企业的重点在于开展概念验证项目和探索技术的潜 创新的复舍效应聚焦于技术的规模化应用。 餐術领导智能曲酮在冷却骚疹的胸神拳密隔酒造 磨段圈興樓轉。 籍展終募争年歲式萊薩廟两족用劍帳劑自己纳化角 新和曲龟撸速揭到00万用户则 ...

随着技术发展速度不断加快，五个关键趋势正在推动企业从技术试验走向实际价值创造。这些趋势不仅反映了当前技术应用的深化，也揭示了未来企业运营 和竞争方式的变化方向。 人工智能正从概念验证阶段迈向规模化应用，成为驱动自动化、创新和业务增长的核心力量。企业领导者意识到，要实现差异化优势，必须通过智能技术重 新设计流程，而非仅仅进行简单的自动化改造。这一过程需要与业务成果紧密结合，并快速执行。 技术的复合效应显著增强。生成式AI的发展速度远超以往，其用户规模在短时间内迅速扩大，形成持续加速的飞轮效应。这种指数级增长依赖于技术、数 据、投资和基础设施的相互促进。传统模式已难以适应这种变化，企业必须重新设计流程，以应对快速的技术演进。 企业在构建数字员工体系时面临诸多挑战。尽管智能体技术被广泛采用，但许多企业尚未实现真正的业务变革。主要问题包括系统整合困难、数据架构限制 以及治理框架不完善。领先企业则通过流程重构、多智能体协同调度等方式，将智能体视为核心劳动力，推动人机混合模式的发展。 随着AI应用的深入，基础设施策略面临重大调整。云服务成本虽有所下降，但使用量激增导致支出持续上升。企业开始采用混合架构，结合本地部署与边 ...

Core Insights - The article discusses the transformation of AI from a mere tool to a "digital employee" capable of executing tasks and making decisions, which is becoming essential for businesses to overcome development constraints [1][3][5] Group 1: AI as a Digital Employee - Companies are optimizing their products, hardware, and services to create "digital employees" that can perform tasks effectively, relying on high-quality data accumulation and continuous training [1][6] - The focus of AI development has shifted from the quality of responses to the training of decision-making capabilities, with some AI models beginning to attempt automatic execution [3][4] - The integration of "digital employees" allows smaller companies to leverage numerous intelligent agents, creating significant energy and potential for growth [3][5] Group 2: Redefining Productivity and Organizational Structure - AI is redefining traditional productivity factors by merging the roles of tools, labor, and data, which represents a significant breakthrough not achieved by previous technologies [4][5] - The emergence of "digital employees" necessitates a reevaluation of management roles, as traditional divisions of labor in human resources, technology, and production become less effective [5][6] - AI's integration can enhance efficiency and promote a flatter organizational structure, potentially helping large enterprises overcome innovation bottlenecks [5][6] Group 3: Challenges and Future Directions - While AI can improve internal decision-making and execution systems, challenges arise in aligning AI capabilities with client needs, leading to potential disconnects [6] - Current AI capabilities are not yet at the level of being "qualified for duty," which is a key reason for the underwhelming market response despite significant investments in AI [6] - The future of AI involves automatic decision-making and execution, with a vision of AI becoming an interactive and capable partner in daily life and work, while human roles will shift towards overseeing AI outputs and resource allocation [6]

Core Perspective - SpaceX has shifted its strategic focus from Mars colonization to a lunar mission, aiming for a moon landing by March 2027, marking a significant change in direction for the company [2][3]. Group 1: Strategic Shift - The company has decided to prioritize building a self-expanding city on the Moon, with a goal to achieve this within 10 years, as opposed to the 20+ years required for Mars [3]. - The frequency of lunar missions is more favorable, with launches possible every 10 days compared to the 26-month window for Mars missions, allowing for faster development of lunar infrastructure [4]. Group 2: Financial and Market Implications - SpaceX has acquired xAI, leading to a combined valuation of $1.25 trillion, making it the most valuable private company globally. There are rumors of a potential IPO later this year, with fundraising efforts possibly reaching $50 billion [6]. Group 3: Technological Innovations - Musk envisions a new commercial landscape in space, proposing the establishment of AI data centers in space, which could operate at a cost of only 1/10th of that on Earth due to the absence of atmospheric interference [7]. - The company plans to launch approximately 100 gigawatts of solar and computational payloads annually into orbit, which Musk predicts will exceed the total AI computational power on Earth within five years [8]. Group 4: Industry Concerns - Musk has expressed concerns about the decline of U.S. manufacturing, proposing the integration of humanoid robots and AI to revitalize the sector. He refers to the Optimus robot as an "infinite money printer" due to its potential capabilities [10]. Group 5: Challenges and Timelines - The company faces significant challenges, including the need for frequent Starship launches and the development of in-orbit refueling capabilities to meet the 2027 lunar timeline. Recent delays in NASA's Artemis program serve as a warning for SpaceX [12]. - Musk emphasizes the urgency of developing AI and robotics to complete these goals before potential economic downturns [12].

Core Viewpoint - SpaceX has shifted its strategic focus from Mars colonization to lunar missions, aiming for a moon landing by March 2027, marking a significant change in direction for the company [2][3][4]. Group 1: Strategic Shift - Elon Musk has emphasized that the moon was previously seen as a distraction, but practical considerations have led to this new focus on lunar exploration [3][5]. - The construction of a self-sustaining city on the moon is now prioritized, with a projected timeline of achieving this within 10 years, compared to over 20 years for Mars [4][5]. Group 2: Technological and Financial Integration - SpaceX has acquired xAI, leading to a valuation of $1.25 trillion, making it the most valuable private company globally [6]. - There are rumors of a potential IPO later this year, with fundraising efforts expected to reach $50 billion [6]. Group 3: Space AI and Computing Power - Musk envisions space as the most cost-effective location for deploying AI, predicting that within 36 months, space will become the cheapest site for AI operations, costing only 10% of ground-based operations [6][7]. - The company plans to launch approximately 100 gigawatts of solar and computing payloads annually, which could surpass the total AI computing power on Earth within five years [7]. Group 4: Manufacturing and Robotics - Musk has raised concerns about the decline of U.S. manufacturing, proposing the integration of humanoid robots (Optimus) and AI (xAI) as a solution [9]. - He describes Optimus as an "infinite money printer," highlighting the exponential growth in digital intelligence, AI chip capabilities, and mechanical dexterity [9]. Group 5: Challenges and Urgency - The timeline for the lunar mission is under pressure, with recent delays in NASA's Artemis 2 program serving as a warning for SpaceX [10]. - Musk acknowledges the need for rapid advancements in AI and robotics to meet the 2027 deadline, emphasizing the urgency of the situation [10].

Group 1 - The core point of the news is that Zhenai Meijia has received regulatory approval from the Shenzhen Stock Exchange for a share transfer agreement, allowing Tongji Technology to acquire 44.99% of its shares through a combination of agreement transfer and partial tender offer [1] - The transaction involves a steady progress on the agreement transfer of 29.99% of shares, marking a significant step in the regulatory process [1] - Tongji Technology is recognized as a unicorn in the domestic AI large model sector, focusing on building a digital productivity platform with B2B and B2C agent solutions that enhance customer acquisition efficiency [1] Group 2 - Tongji Technology's proprietary platforms, "Taiqing" and "Kuanhu," support the development of digital employees, integrating high-quality data and industry knowledge to create expert-level large models [2] - The "Kuanhu" data cloud base utilizes a "lake-warehouse integration" architecture to gather, govern, and share vast amounts of multidimensional commercial data, addressing key challenges in real-time data access for large models [2] - The company aims to continue innovating in large model intelligent agents and expand the application boundaries of AI capabilities, positioning itself as a core driver of productivity transformation in the AI application explosion year [2]

Core Insights - The article discusses the transformative impact of AI on organizational structures and workflows, emphasizing the shift towards a flatter information flow within companies due to AI applications [2][3] - Satya Nadella highlights the importance of AI in enhancing productivity and efficiency across various sectors, asserting that the true value of AI lies in its widespread application rather than mere technological discussions [3][18] - The conversation also touches on the competitive landscape of the tech industry, suggesting that the continuous evolution of competitors is beneficial for maintaining innovation and growth [16][17] Group 1: AI Applications and Organizational Change - AI is breaking traditional hierarchical structures in companies, allowing for a more streamlined and efficient information flow [2] - Companies, regardless of size, face challenges in adapting to AI, requiring a shift in mindset, skill development, and data integration [2] - The leverage effect of AI is particularly pronounced in startups, which can build AI-adapted organizations more rapidly compared to larger firms with established workflows [2] Group 2: Talent and Global Competition - There is no significant difference in AI talent quality between regions; cities like Jakarta and Istanbul are on par with tech hubs like Seattle and San Francisco [3] - The key differentiator for AI success is the pace of large-scale application rather than the talent pool itself [3] - The U.S. technology stack's core advantage lies in its ecosystem effects, which generate more revenue from the ecosystem than from the company itself [4] Group 3: AI Integration and Future Workforce - Microsoft is implementing a new apprenticeship model where experienced engineers mentor new graduates, leveraging AI to accelerate their productivity [34] - The integration of digital employees (AI agents) into business processes is seen as a way to automate repetitive tasks and improve operational efficiency [31][11] - The future workforce will need to adapt to AI tools, which will significantly shorten the learning curve for new employees [34] Group 4: Market Dynamics and Ecosystem Effects - The article emphasizes that the technology industry is not a zero-sum game; rather, it is expanding, with the potential for significant growth in the tech sector [16][17] - The concept of "diffusion" is crucial for understanding how AI technologies can be effectively integrated across various industries, including healthcare and finance [18][19] - The U.S. must ensure that its technology stack is widely adopted globally, as this will create economic opportunities and enhance trust in the platform [20][21]

整理 | 褚杏娟 最近的达沃斯论坛上，科技领袖们纷纷出来发表观点。 当 Google 的 Demis Hassabis 和 Anthropic 的 Dario Amodei 在讨论更宏观的 AGI 话题时，微软 CEO Satya Nadella 与英国前首相 Rishi Sunak 的对话，更聚焦在了 AI 应用的话题。 Satya 以自己参加达沃斯的准备工作变化为例，来说明在企业内部，AI 正在打破传统层级架构，让信息 流实现扁平化。 "自从我 1992 年参加以来，直到几年前，流程都没什么变化：我的现场团队会准备笔记，然 后送到总部进一步提炼。但现在我直接找 Copilot 说，"我要见 xxx，给我一个简介"。它会 给我一个全方位的视角。""我做的是立即把这个简介分享给所有部门的同事。" 他指出，企业 AI 应用呈现出明显的 "杠杆效应"：初创公司能从零开始构建适配 AI 的组织，落地速度 更快；大型企业虽手握数据、资源优势，但传统工作流程与组织惯性带来的变革管理挑战更大。而无论 大小企业，都需经历 "思维转变 — 技能培养 — 数据整合" 的艰苦过程。 人才方面，他认为全球 AI 技术人才与初创公司 ...

Core Insights - The forum on January 9 focused on the integration of industry and education for digital talent cultivation, with over 200 leaders and experts from various sectors participating to discuss collaborative strategies and witness the establishment of a data analysis industry integration community [1][4]. Group 1: AI Application Trends - The Chinese market is transitioning from "pilot quick wins" to "full-scale promotion" of AI applications, but a significant value bottleneck persists, with 80% of companies deploying generative AI yet 80% reporting no substantial impact on financial statements [3][7]. - The traditional view of AI as a "super tool" is inadequate for addressing the complexities of real-world production scenarios, necessitating a shift in understanding [3][7]. Group 2: AI Evolution and Talent Development - Continuous technological breakthroughs are facilitating a fundamental shift in AI deployment, evolving from functional applications to "digital employees" capable of planning, decision-making, and executing tasks across end-to-end processes [3][7]. - The key challenges for successful AI implementation in enterprises now focus on organizational structure evolution and the cultivation of hybrid talent, moving from reliance on large models to the importance of prompt engineering, knowledge base construction, workflow design, and tool application [3][7]. Group 3: Collaborative Ecosystem - The establishment of the data analysis industry integration community marks the beginning of a collaborative innovation ecosystem connecting government, industry, enterprises, and educational institutions, aiming to address talent gaps in AI implementation [4][8]. - Future efforts will prioritize business practice-oriented approaches and resource co-construction to overcome challenges in AI deployment, moving beyond traditional talent supply-demand relationships [4][8].

Core Insights - Sequoia China and Unipat AI have launched a significant update to the xbench evaluation framework, introducing the BabyVision assessment to evaluate the pure visual understanding capabilities of large models, indicating substantial future potential in world models and visual multimodality [2] - The new AgentIF-OneDay evaluation system measures the ability of agents to solve complex long-term tasks, moving beyond simple knowledge assessment to evaluate performance in real-world scenarios [2][3] Evaluation Framework - The AgentIF-OneDay framework explores the transition from one-hour to one-day capabilities, revealing the true performance of mainstream agents in workflow execution, implicit inference, and iterative editing [3] - The evaluation aims to observe the evolution of industry technology routes and predict the upper limits of model capabilities, focusing on utility and economic value [7][8] Agent Capabilities - The evolution of agent capabilities is expected to follow two main lines: scaling context and scaling domain, which determine the complexity of tasks agents can handle [8][9] - Scaling context refers to the extension of tasks over time, requiring agents to maintain context and consistency over longer execution periods [8] - Scaling domain involves expanding the types of tasks agents can perform, moving beyond highly structured tasks to those that span multiple domains and contexts [9] Task Complexity - The AgentIF-OneDay evaluation uses the complexity of tasks that can be completed within a day as a benchmark, testing agents' abilities to complete tasks without human intervention across diverse domains [12] - Analysis of user work logs indicates that daily tasks can be categorized into three types: workflow execution, example reference, and iterative editing [13] Task Types - Workflow execution involves agents executing known processes accurately, while example reference requires agents to infer intent from provided examples [14][15] - Iterative editing tasks require agents to maintain context and adapt to changing requirements through multiple interactions [16] Evaluation Results - The AgentIF framework has tested existing mainstream agent systems, revealing that Manus, Genspark, and ChatGPT-Agent are currently the top performers, with overall task success rates between 0.62 and 0.65 [20] - ChatGPT is identified as the best productivity tool, Manus as the best life assistant, and Genspark as the best study partner, highlighting different strengths across various task domains [21][22] Future Directions - The development of the OneWeek evaluation set is underway, aiming to challenge agents with tasks that require a week’s worth of human work, indicating a significant step towards agents taking on real job responsibilities [24] - The transition to OneWeek tasks will necessitate more stringent evaluation criteria and the ability for agents to learn and adapt in real-world environments [25][26] - The accumulation of user data is crucial for enhancing agent reliability and performance in long-term tasks, similar to the evolution of autonomous driving technology [27]