1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The digital economy has become the main engine of global economic growth, driven by the rapid evolution of mobile communication technology and the accelerated development of AI. The deep integration of these two forces is giving rise to the era of Mobile AI, which has become a disruptive paradigm and a key driver of global digital and intelligent development [4][5]. - Mobile AI is based on the principle of two - way empowerment between the network and AI, with the core values of responsible AI, security, and trustworthiness. It forms an intelligent service system that provides wide - coverage, real - time response, and precise adaptation, aiming to achieve the goal of "AI everywhere, trustworthy, and easy to use" [3]. - From a global industrial perspective, Mobile AI will evolve from integrated applications to native symbiosis as 5G - A becomes more popular and 6G moves towards commercialization. This transformation will release continuous innovation power, empower large - scale industries, and become the cornerstone of high - quality development of the global digital economy [7]. 3. Summary According to Relevant Catalogs 3.1 Mobile AI's Value 3.1.1 Economic Value: Releasing Large - Scale Potential through Integration - The large - scale deployment of 5G/5G - A networks and the rapid spread of AI are pushing mobile communication and AI into a new stage of deep integration. Mobile AI is based on the core logic of "two - way empowerment between the network and AI", creating a new service system and reshaping industrial and social operations [10]. - The global mobile communication industry has entered the critical stage of 5G scale development. By the end of 2025, there were 384 commercial 5G networks globally, with over 3 billion 5G users. It is predicted that by 2030, global 5G connections will reach 8.8 billion, accounting for over 60% of the total global mobile connections [12][15]. - The global AI market is expanding. In the consumer sector, the adoption of GenAI is accelerating, with about 75% of respondents using GenAI applications. In the enterprise sector, by 2030, over 70% of the workload in global data centers will be for AI - related computing needs, and the GenAI software market is expected to reach $122.8 billion [19][24]. - Global mobile operators' revenue reached $10.8 trillion in 2024 and is expected to increase to $12.5 trillion by 2030. The total capital expenditure from 2024 to 2030 is expected to be $13 trillion to support Mobile AI development [27]. 3.1.2 Social Value: Responsible, Inclusive, and Safe AI Progress - Mobile AI can promote the intelligent upgrade of social governance, such as in urban governance, public services, and emergency response, by building a governance system with comprehensive perception, rapid response, and precise intervention [36]. - It can popularize AI technology, breaking down hardware barriers, technical complexity, and scenario limitations, and making AI a universal resource accessible to all [37]. - Mobile AI can ensure AI compliance, security, and controllability by providing remote monitoring and security takeover capabilities for intelligent devices and establishing a governance network [38]. 3.2 Connotation of Mobile AI 3.2.1 Three - layer and Four - dimension Architecture - The three - layer architecture includes the basic layer (providing core support for connection, computing, and collaboration), the execution layer (encapsulating functions and providing standardized interfaces), and the application layer (creating economic and social benefits in specific scenarios) [42][43][44]. - The four - dimension includes AI for Network (automating and intelligentizing network operations), Network for AI (providing intelligent connection support), Mobile AI Agents/Terminals (achieving intelligent service delivery through device - edge - cloud collaboration), and Mobile AI Applications (transforming technology into solutions for various industries) [45][48][51]. 3.2.2 Key Applications in Different Fields - In the field of network operation and management, Mobile AI can solve problems in network planning, maintenance, optimization, and security, such as promoting dynamic network planning, autonomous network maintenance, and collaborative network operation [47][52][54]. - For new intelligent terminals, Mobile AI can meet the high - demand network connection requirements of intelligent robots, AI phones, and AI glasses, and promote the development of new intelligent services [59]. - In the field of intelligent applications, Mobile AI can enhance the user experience in consumer scenarios, such as in digital consumption, immersive experience, and personalized services. In enterprise scenarios, it can promote the digital and intelligent transformation of industries such as intelligent manufacturing, transportation, and healthcare [90][91]. 3.3 Towards Mobile AI 3.3.1 Infrastructure Improvement - It aims to build a bottom - layer support system with "adaptive connection, sufficient computing capacity, and efficient collaboration" by improving basic communication network capabilities and optimizing computing resource allocation [113]. - Communication networks need to be upgraded in terms of high bandwidth, low latency, massive connections, and high reliability, and a dynamic adaptation mechanism should be established [115]. - A hierarchical and collaborative "device - edge - cloud" computing capacity supply system should be constructed to meet the different computing needs of Mobile AI [117]. 3.3.2 Spectrum Assurance - A dual - track evolution path should be adopted for spectrum strategy, including "new frequency band planning" and "existing spectrum optimization" [114]. - Technologies such as AI - driven dynamic spectrum management and intelligent spectrum aggregation can be introduced to improve spectrum utilization efficiency [116]. - Promote the use of mid - frequency bands (such as 6GHz) and explore the deployment of high - frequency bands (such as millimeter - wave) in hotspots [118]. 3.3.3 Technological Innovation - Mobile AI technology innovation is based on the native AI network architecture, developing two - way technology systems of AI to the network and the network to AI [119]. - Key technologies include AI for Network (integrating AI into the end - to - end network system), enhanced uplink technologies (breaking the uplink bottleneck), and terminal - edge - cloud collaboration (optimizing the balance between business needs and resources) [127][129][145]. 3.3.4 Terminal Evolution - Terminal hardware needs to evolve towards multi - modal perception, full - band reliable connection, and efficient local intelligent processing to meet the diverse requirements of Mobile AI applications [158]. - Terminal forms should be diverse, collaborative, and popularized to create an all - scenario adaptive terminal system [158]. 3.3.5 Standard Formulation - Industry - specific application standards should be formulated for key industries to guide the standardized development of Mobile AI applications [158]. - A global unified standardization technology system should be established to eliminate collaboration barriers and reduce R & D and adaptation costs [162]. - A quantifiable experience evaluation standard should be improved to ensure that the evaluation results are consistent with the real user perception [163]. 3.4 Recommendations and Calls to Action - Establish a unified global standardization system, including standardizing AI model calling methods, agent protocols, and data formats [181]. - Reserve and optimize spectrum resources, and accelerate the deployment of 6GHz and millimeter - wave bands [181]. - Activate data as a core production factor, promoting safe data flow and the development of data asset markets [181]. - Strengthen infrastructure construction, building a next - generation connectivity system suitable for Mobile AI [181]. - Promote industry innovation through measures such as encouraging the application of open APIs and starting pilot projects [182].