Core Insights - The rapid development of AI large models is becoming a key factor in the new round of technological competition, with a belief that the number of foundational large models will converge to a single-digit figure, while numerous specialized models and applications will emerge across various industries [1] - Physical AI is highlighted as a significant area of focus, accelerating advancements in embodied intelligence and autonomous driving, which are expected to profoundly change societal operations [1] - The transition to the "Agent era" presents challenges in integrating AI technology into the real economy, particularly in terms of legal, compliance, and ethical considerations [1] Physical AI Debate - The emergence of Sora in early 2025 has sparked discussions about "world models" and the competition between two core routes of physical AI: world models and VLA (Visual Language Models) [2] - Sora's development signifies AI's evolution from a "predictor" to a "simulator," marking a paradigm shift necessary for applications like autonomous driving and embodied intelligence [2] - Current models like Sora are criticized for being mere "visual simulators" lacking true physical world modeling capabilities, as they often fail to maintain physical logic [2][3] Model Differentiation - The world model route has diverged into "generative" and "representational" factions, with generative models like Sora focusing on empirical learning from vast sensory data, while representational models emphasize rational deduction through structured internal representations [3] - Generative models are suited for data factories or simulation training, whereas representational models excel in decision-making processes [3] Industry Trends - There is a trend towards the integration of VLA and world models, utilizing VLA for high-level strategy planning and world models for low-level action validation [4] - The evolution of network architecture is shifting from "cloud-native" to "AI-native," necessitating networks to achieve extreme performance and seamless integration of computing and networking [5][6] AI Native Applications - AI applications are transitioning from content generation to autonomous action, with a focus on restructuring entire value chains rather than merely enhancing efficiency in isolated processes [7] - The challenges of deploying agents in critical industries like telecommunications and finance include reconciling the randomness of models with deterministic business needs and ensuring stability in long-term tasks [8] Deep Water Practices - Industries that are likely to achieve scalable AI value realization include education, healthcare, software development, intelligent manufacturing, and urban governance, characterized by high data structuring and rapid feedback mechanisms [9][11] - The transition from "shallow water" to "deep water" signifies AI's deeper integration into core business processes, facing complexities such as multi-modal data and new security threats [12] Hybrid Approaches - The development paths for AI integration may involve a hybrid approach combining "general foundational models + industry fine-tuning" and building industry-specific small models from scratch [12][13] - General models trained on human language may introduce noise in industrial applications, necessitating the creation of specialized models for non-natural language data [13]