Apple(US:AAPL) Hua Er Jie Jian Wen·2025-11-11 08:14Core Insights - Apple's AI strategy is evolving towards a revolutionary "edge-cloud collaborative" agent framework rather than merely pursuing larger language models [1][2] - The integration of a powerful cloud model, rumored to be Google's 1.2 trillion parameter model, is central to Apple's approach, which aims to efficiently and securely utilize user data [1][2] - This strategy, if successful, could signify the large-scale practical application of "edge AI," enabling highly personalized and context-aware tasks that current cloud-based LLMs cannot achieve [1][3] Group 1: Collaborative Agent Model - The framework combines a cloud-based "high-order reasoning agent" with multiple specialized "edge agents" running on devices, optimizing resource usage by compressing data for transmission [2][3] - A backup offline solution is designed to ensure basic functionality when the device is offline or handling simple queries [2] Group 2: CAMPHOR Model - The CAMPHOR model consists of a cloud-based high-order reasoning agent and five specialized edge agents, working together to perform tasks beyond the capabilities of traditional LLMs [3][6] - The five edge agents include: - Personal Context Agent: Searches user data for context [3] - Device Information Agent: Retrieves device-related data [3] - User Perception Agent: Accesses recent user activity [3] - External Knowledge Agent: Gathers data from external resources [3] - Task Completion Agent: Executes tasks using device applications [3] Group 3: Future Opportunities - The integration of external knowledge access positions the model as a frequently used daily tool, indicating the imminent application of "edge AI" in real-world scenarios [7] - Anticipated advancements in personalization and privacy protection will be crucial for utilizing personal data while ensuring user privacy [8] - Significant improvements in instant response performance will require enhancements in wireless communication, processing power (GPU), and memory bandwidth [9] - The expansion of personal data sources, including wearables, will broaden service applications into health and training recommendations [9] - The future winners in the AI space will be those who can achieve efficient, low-power, and secure computing on the edge while building a cohesive hardware-software ecosystem [9]