Group 1 - The core focus of the Chinese internet industry in 2026 will be on three main themes: growth in recurring revenue from cloud infrastructure, model stacks, and the use of inference tokens; competition among major internet companies for user traffic in AI chatbots for future ecosystem monetization; and vertical companies deploying proprietary data AI agents to maintain competitive advantages and enhance user engagement and monetization potential [1] - Most internet companies are expected to improve operational leverage through AI-assisted cost optimization, with some likely to accelerate revenue growth due to effective deployment of AI agents [1] - The rapid adoption of AI assistant tools is anticipated to enhance consumer work and life efficiency, allowing more time for leisure activities [1] Group 2 - Continued demand in tourism and online gaming is expected to support stable revenue growth for online travel and gaming companies, providing good diversification for investment portfolios to offset the higher risks associated with AI hype [1] - Citigroup's preferred stocks in the Chinese internet sector are Tencent Holdings (00700) and Alibaba-W (09988) as core AI investment concepts [1] - Citigroup maintains a "Buy" rating on Tencent Holdings with a target price of HKD 751, citing strong prospects for AI development in enterprise and user applications; similarly, Alibaba is given a "Buy" rating with a target price of HKD 223, focusing on growth in cloud revenue and efficiency improvements [1]

花旗预计，旅游和在线游戏的持续需求将支持在线旅游和游戏公司的稳定收益增长，可为投资组合提供 良好的多元化，抵销AI炒作的较高风险。 就中国互联网板块，花旗首选腾讯控股(00700)及阿里巴巴-W(09988)作为核心AI投资概念股。该行维持 腾讯控股"买入"评级，目标价751港元，认为该公司在企业和用户应用方面的AI发展前景良好。对于阿 里巴巴，花旗同样给予"买入"评级，目标价223港元，看好其在云端收入增长和效率提升方面的表现。 智通财经APP获悉，花旗发表研究报告指出，2026年内地互联网行业将聚焦于三大主题：一是云端基础 设施、模型堆栈和推理令牌使用的经常性收入增长；二是主要互联网企业竞相抢占AI聊天机器人的用 户流量，以期未来生态系统变现；三是垂直领域企业部署自训练专有数据AI代理，以保持竞争优势并 加强用户参与度和变现潜力。 报告指出，预计大多数互联网企业将通过AI辅助成本优化来改善营运杠杆，部分企业更可能因有效部 署AI代理而实现收入增长加速。随着AI助手工具的快速采用，消费者的工作和生活效率有望提升，从 而有更多时间投入休闲娱乐活动。 ...

Core Insights - The Ministry of Industry and Information Technology emphasizes the importance of integrating "Artificial Intelligence + Manufacturing" to accelerate smart upgrades in key industries [1][2] - The manufacturing sector is facing structural challenges and transformation pressures due to the AI wave, necessitating a shift from traditional processes to data-driven and intelligent systems [3][4] Group 1: Integration of AI in Manufacturing - The integration of AI is redefining the manufacturing landscape, moving from a hierarchical structure to a platform-based, decentralized system [6][7] - AI is becoming the core intelligence of manufacturing networks, facilitating real-time interaction and smart closed-loop operations [7][12] Group 2: Iterative Pathways of AI Implementation - The five iterative pathways for AI integration in manufacturing include: 1. Perception iteration: Enhancing data collection and understanding through AI [8] 2. Control iteration: Transitioning from rule-based to intelligent control systems [9] 3. Execution iteration: Evolving from automation to intelligent collaborative systems [10] 4. Operation iteration: Shifting from reactive management to predictive optimization [11] 5. Decision iteration: Advancing from delayed analysis to real-time intelligent decision-making [12] Group 3: Organizational Capabilities for AI - The need for a strategic approach to AI, viewing it as a core resource for business transformation rather than a one-time IT project [16][17] - The demand for a hybrid talent pool combining AI engineers and manufacturing experts to facilitate effective AI implementation [18][19] - The importance of establishing a unified AI and data platform to overcome fragmentation and enhance scalability [20] Group 4: Challenges in Data and Model Utilization - Manufacturing companies face significant challenges in data utilization, with only 44% of collected data being effectively used [27] - The complexity of industrial AI models requires a deep understanding of manufacturing processes, which cannot be achieved through generic models [31][34] - Companies must build a sustainable AI capability system, focusing on data governance, scenario modeling, and model fine-tuning [35] Group 5: Future Outlook - 95% of manufacturing enterprises plan to invest in AI over the next five years, indicating a shift towards a system-wide transformation [36] - The core capability of future manufacturing will be the ability to create self-optimizing, intelligent collaborative systems [36]