这份由腾讯研究院发布的《塑造自己的下一个版本 2026 前沿科技趋势》报告，以生命力、体力、脑力、创造力、追求五大维度为核心，眺望 2030 年前沿 科技对人类自身与社会的重塑，提出科技进步应以人为本，核心是通过技术助力人类塑造更好的 "下一个版本"，同时直面技术发展带来的机遇与挑战， 明确个人、企业和社会的应对方向。以下是核心内容总结： 生命力 2030：从 "活得更久" 到 "活得更好"，开启生命第三次转型 转型背景：人类预期寿命增长速度大幅放缓，单纯延长寿命的 "果实" 已被耗尽，全球转向追求健康寿命（无严重慢性病、维持良好生活质量的年限）， 这一转型对全球经济影响深远，延长 1 年健康寿命将产生 38 万亿美元全球经济价值。 核心技术 基因疗法：进入 "生命代码优化" 时代，CRISPR 技术 2.0、体内递送系统完善推动预防性基因疗法实现临床突破（如一次性治疗高血脂、高血压），RNA 疗法实现慢性病长效控制和重症治疗，表观遗传重编程更开启逆转衰老的曙光，2026 年初将有相关人体临床试验启动。 人工智能：成为医疗的 "操作系统"，生成式 AI 大幅压缩药物研发周期，AI 与多组学结合实现 75 种癌 ...

2025 年麦肯锡 AI 行业全球调研显示，企业正通过组织架构调整、流程重构等方式挖掘生成式 AI 价值，行业整体处于 AI 应用早期阶段，但大公司推进速度 显著快于中小企业，AI（含生成式 AI 与分析式 AI）的企业渗透率与应用广度持续提升，78% 的受访者表示所在企业至少在一个业务职能中应用 AI，生成 式 AI 应用率也达 71%，且应用场景从单一职能向多职能延伸，平均覆盖三个业务领域。 企业实现生成式 AI 商业价值的核心在于顶层设计与流程重塑，CEO 主导的 AI 治理与工作流重构是影响息税前利润的关键因素，28% 的 AI 应用企业由 CEO 负责 AI 治理，21% 的企业已基于生成式 AI 完成部分工作流的根本性重构。AI 部署架构呈现选择性集中特征，风险合规、数据治理多采用中心化模 式，技术人才与 AI 解决方案落地则以混合模式为主，且大企业的治理与部署体系更完善。 企业对生成式 AI 的风险管控力度持续加强，针对准确性、网络安全、知识产权侵权等核心风险的应对举措显著增加，大企业的风险应对覆盖面更广，尤其 在网络安全和隐私保护领域投入更多，但各企业对 AI 输出内容的审核力度差异较大，27 ...

无人机组在城市夜空辗转腾挪烘托节日气氛，元宇宙空间让游客秒变"造物者"穿越古今，AI设计和3D 打印订制个性化纪念品给足情绪价值……春节期间，全国各地旅游场景中，"文化+科技"的文旅项目正 以前所未有的辨识度，吸引着游客、活跃着经济、拉动着消费。 因此，各地上新"文化+科技"文旅项目，应将"尊重文化本体"作为优先议题，先把要"呈现哪些内涵""放 大哪些元素""再现哪些经典"这些问题梳理清楚。文化方面的思路理清了，再把"如何表达和呈现"交给 技术。这样做，才能在增强消费者体验感的同时，更好彰显文化温润心灵、陶冶情操的作用和价值。 当科技手段真正赋能于文化底蕴的深度挖掘、情感的精准传递、瑰宝的长久留存，相信更多"文化+科 技"的优秀融合成果，会在新兴技术持续迸发的时代次第绽放。 （文章来源：科技日报） 城市文旅项目中的此类现象较为典型。一些景区打着高科技旗号的沉浸式体验项目，游客花着高额票价 消费，体验完只记得"灯光很亮""音乐很响""脑袋很晕"，真正留在心里的文化记忆，却浅之又浅。一些 博物馆倾力打造的互动项目，同样缺乏特色表达。文物全息成像、裸眼3D、触屏翻转等方式，让很多 观众感慨"似曾相识"，初见倍感惊艳 ...

Core Insights - The AI landscape is rapidly evolving, with significant advancements in large models, intelligent agents, computing power, data centers, and application ecosystems, leading to a competitive environment where companies like OpenAI, Anthropic, and Google are vying for market leadership [2] - The focus for 2026 will shift from merely enhancing model capabilities to a comprehensive evaluation of cognitive direction, implementation pathways, and organizational capabilities [2] - The transformation of AI from a tool to a collaborative partner is crucial, emphasizing the need for governance and the establishment of boundaries for AI decision-making [4][5] Group 1: AI Evolution and Organizational Change - The current AI revolution differs from past technological waves as it transcends mere tool iteration, fundamentally altering the boundaries of productivity factors [3] - The concept of "Superintelligence" emerges as a new organizational form where companies operate as continuously learning systems, integrating machines, humans, and organizations [3] - The transition to a "human-centered" approach in AI is essential, where machines evolve to become proactive agents capable of executing complex tasks [4] Group 2: Governance and Human-Centric Focus - As AI becomes more autonomous, the governance of AI systems will be critical, necessitating clear boundaries for AI actions and human oversight [5] - The shift from mechanical tasks to human-centric roles will highlight the importance of human capabilities such as vision, judgment, and creativity in navigating AI's influence [6] - Companies must adapt to a new organizational structure that fosters human-machine collaboration, moving away from traditional hierarchical models [6] Group 3: Practical Application and Infrastructure Investment - The focus on "real-world" applications of AI will be paramount in 2026, requiring companies to demonstrate scalable and replicable AI solutions that yield tangible business results [7] - Investment in AI infrastructure is expected to surge, with major tech companies forecasting significant capital expenditures to support AI deployment [8][9] - The evolution of AI models will expand beyond language models to include architectures that can understand physical laws and facilitate real-world applications [8] Group 4: Future Outlook and Strategic Priorities - Despite skepticism regarding investment returns, leading companies are prioritizing AI as a key strategic initiative to drive measurable value [11] - The dual focus on enhancing human capabilities alongside AI deployment will be essential for maximizing productivity and ensuring effective organizational transformation [11]

Group 1 - The core viewpoint is that SenseTime's inclusion in the MSCI China Index highlights the growing recognition of Chinese AI companies by global capital markets, potentially attracting more international passive investment [1][2][4] - SenseTime's stock price surged by 11.39% to HKD 2.64 per share on the day the MSCI announcement was made, indicating strong market reaction [1] - The MSCI China Index serves as a significant benchmark for global capital allocation, with over USD 17 trillion in assets tracked by MSCI indices, including more than USD 2 trillion in passive funds [4] Group 2 - SenseTime's strategic advantage lies in its "big device - big model - application" integrated approach, which differentiates it from competitors that focus solely on models or applications [3] - The company's recent financial report indicates that by mid-2025, revenue from generative AI is expected to rise to 77%, with projections suggesting it could reach 91% by 2030 [3] - The inclusion of SenseTime in the MSCI index signifies a validation of its technological strength, business model, and governance standards, marking a milestone for Chinese AI companies in the international capital landscape [4]

Core Viewpoint - SenseTime's inclusion in the MSCI China Index marks a significant recognition of its value in the global AI industry, potentially attracting more international passive investment and validating the long-term value of Chinese AI companies [1][2][4]. Group 1: MSCI Index Inclusion - SenseTime's stock price surged by 11.39% to HKD 2.64 per share on the day MSCI announced its latest index review [1]. - The adjustment to include SenseTime in the MSCI China Index will take effect after the market closes on February 27 [1]. - The MSCI China Index is a key benchmark for global investors, with a significant amount of passive funds tracking it, making it one of the most referenced indices for Chinese investments [2]. Group 2: Value Logic Behind Inclusion - Inclusion in the MSCI China Index requires companies to meet strict criteria, including having clear governance structures and competitive advantages in their industry [2]. - SenseTime's recognition reflects its institutional and professional management, as well as its position in the AI technology landscape [2]. Group 3: Business Model and Financial Performance - SenseTime's core advantage lies in its "big device-big model-application" strategy, which differentiates it from competitors that focus solely on models or applications [3]. - The company's recent financial report indicates that its generative AI revenue is expected to rise significantly, reaching 77% by mid-2025, with projections of 91% by 2030 [3]. Group 4: Implications for the AI Industry - SenseTime's inclusion in the MSCI China Index signals its core position in the AI industry, potentially attracting a broader base of long-term value investors [4]. - The MSCI Index adjustment could lead to substantial capital reallocation, given that over $17 trillion in assets are benchmarked to MSCI indices, with more than $2 trillion in passive funds [4]. - The inclusion of SenseTime alongside major tech players like Tencent and CATL highlights its technological and governance strengths, providing a foothold for Chinese AI companies in the international capital market [4].

根据美国国家风险投资协会的预测，到2025年，AI和机器学习领域的风险投资交易额将达到2220亿美 元，占美国所有风投项目资金的65%以上。这一比例较2024年的47%和2015年的10%有显著大幅提升。 然而，尽管资金充裕，产品交付速度却难以匹配市场炒作，导致大量资本流向了实际上已不具备竞争力 的企业。 这种资本错配引发了投资界和经济学家的广泛担忧。Landmark Wealth Management创始人Joseph Favorito 指出，长期支持负债融资或资不抵债的AI公司可能会减缓创新，并造成广泛的经济连锁反应。这种"输 血"行为挤占了本应用于更具生产力领域的有限资金，导致资源和人才被错误地分配到业绩不佳的公司 中。 市场分析人士警告，当前的繁荣类似于互联网泡沫时期。随着补贴时代的结束和行业整合的加剧，那些 无法产生实际收益的公司将面临破产或被贱卖的命运，而能够预见这一趋势并拥有真实造血能力的企业 将成为最终的赢家。 资本狂热与僵尸化风险 随着大量资本涌入人工智能领域，市场正面临一种日益严峻的结构性风险：巨额资金正在支撑起一批虽 然功能齐全、但已失去商业生存能力的初创公司。这些企业虽然无法盈利或偿还 ...

Core Insights - Traditional companies can leverage their existing business while innovating, avoiding the pitfalls of solely relying on incremental innovation, which increases the risk of disruption [1][3] - Successful innovation processes in established firms involve three key stages: exploration, commitment, and scaling [3] Exploration: Finding Suitable Startups - Established companies often face challenges in innovation due to their focus on reliable products and services, leading to limited innovation strategies [6] - Many traditional firms have missed opportunities due to their reluctance to embrace disruptive innovations, as exemplified by Blockbuster's failure to acquire Netflix [6] - Successful innovators in established firms follow three practices during the exploration phase to avoid failure [6] Commitment: Leveraging Strengths - Once a breakthrough is achieved, established companies often reassess their roles and increase their commitments, typically through significant investments or acquisitions [10] - Companies should view commitment as a carefully managed upgrade process rather than a binary investment decision [10] - Key questions to assess the viability of an innovation project include the feasibility of the business model, the existence of a supportive ecosystem, customer readiness, and stakeholder support [11][12][13] Scaling: Rapid Action - When a new business model becomes viable, rapid resource mobilization and scaling are crucial for established companies [14] - Investment is often the only factor preventing new enterprises from realizing their full potential, and hesitation can lead to missed market leadership opportunities [14] - Leadership teams can overcome scaling obstacles by involving CFOs early, presenting conservative proposals to the board, avoiding pitfalls of differentiation, and fostering an entrepreneurial spirit [15][16]

Core Insights - The article highlights a critical issue regarding the impact of AI on professional development, specifically the potential loss of the "trial-and-error" learning process essential for developing judgment skills in the workplace [1][4][11] - It emphasizes that while AI can enhance efficiency, it may inadvertently hinder the growth of essential decision-making abilities among less experienced employees [3][10][19] Group 1: AI's Impact on Judgment Development - AI tools can assist experienced professionals in completing complex tasks more efficiently, but they may not provide the same benefits to novice employees, who struggle to assess the quality of AI-generated outputs [3][11] - The reliance on AI for basic tasks diminishes the opportunities for employees to engage in the hands-on experiences that traditionally foster judgment skills [10][18] - The paradox exists where AI increases the demand for judgment while simultaneously eroding the experiential foundation necessary for its development [4][11] Group 2: Definition and Importance of Judgment - Judgment is defined as the ability to make wise decisions in the absence of clear guidelines, encompassing various forms such as evaluative, situational, trade-off, predictive, and accountability judgments [6][7] - The development of judgment typically occurs through practical experience and feedback in the workplace, which is increasingly compromised by AI automation [8][10] Group 3: Organizational Risks and Challenges - Organizations face a leadership and succession risk as the pool of individuals capable of making sound decisions in uncertain situations shrinks due to the automation of foundational tasks [17][18] - The cultural shift towards managing upwards rather than making decisions may arise when employees are deprived of critical learning experiences early in their careers [19][20] Group 4: Solutions for Rebuilding Judgment Skills - Organizations need to redesign work processes to ensure that judgment skills can continue to develop in the AI era, focusing on identifying decision-making points and the necessary knowledge and skills for those roles [20][21] - Learning from fields like healthcare and military, which utilize case studies and simulations to build judgment skills, could provide valuable insights for creating alternative training mechanisms [20][21]

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：金十数据 瑞银全球股票策略团队近期发布了一份清单，列出了10个挑战市场共识的观点，并指出核心预测可能在 哪些方面偏离轨道。 在2025年，市场的共识被证明在"美国例外论"、中国股票、美元表现，以及人工智能（AI）对软件等行 业的颠覆性影响上表现得"非常错误"。现在，该团队概述了今年可能颠覆投资者预期的10个新情景—— 从潜在的市场泡沫到主权债务危机。 意外1：市场先暴涨后暴跌 虽然瑞银的核心观点是将年底的MSCI AC世界指数目标定在1130点，但该行警告称，形成泡沫的全部7 个先决条件现在都已经具备。 根据瑞银的报告，生成式AI的迅速普及已将季度年化生产率增长推高至4.9%。如果生产率从2028年起 增长2%，标普500指数的公允价值可能达到8600点。 然而，最终的崩溃可能由科技过度投资、财政担忧导致的债券收益率飙升，或移民减少导致的美国工资 增长加速触发。 意外2：主权债务危机与债券收益率再创新高 瑞银预计，到年底美国10年期国债收益率将处于4%的水平，但在意外情景中，收益率可能超过上一个 周期5.04%的高点。 报告指出， ...