1. Background: AI Phones Enable On-Device Computing
1.1 Generative AI Transforms User Experiences via LLMs
1.1.1 Mainstream Generative AI Applications of: Image and Text Generation, Productivity Tools
1.1.2 LLMs: Utilizing Model Parameters, Tokens, and Penalty Mechanisms to Enhance Content Accuracy and User Relevance
1.2 Rise of Edge Computing Drives Mobile Chip Performance for Local AI
1.2.1 Emergence of Hybrid AI: Convergence of Cloud AI and Edge AI Models
1.2.2 Mobile Chips Redesigned for On-Device AI: Specification Upgrades and Configuration Changes
1.2.3 10 Billion Parameters: Mobile AI's Current Limit Due to Chip and Memory Constraints
2. Brands Focus on Developing LLM and Diverse AI Applications
2.1 Smartphone Brands Shifting from Hardware Specifications to Proprietary LLM Development
2.1.1 Android Camp: Pioneering AI Phone Launches
2.1.2 iOS Camp: Building Competitiveness with Proprietary LLMs as Market Followers
2.2 Software Services: Focusing on End-user Applications and Upgrading Proprietary OS
3. Opportunities and Challenges: Memory specs as a Bareeir to AI Development
3.1 Opportunities: AI Trends Boost Sales, Chip Supply Chain Benefits from Hardware Upgrades
3.1.1 Smartphone Brands: Generative AI Optimizes User Experience, Aligning with Social Interaction Needs of Users
3.1.2 Mobile Supply Chain: Suppliers Benefit from Specs Upgrades in Early AI Phone Development
3.2 Challenges: Memory Limits as Key Barrier for AI Phone Development
4. MIC Perspective
4.1 Mobile Brands Enhance Generative AI with LLM
4.2 Generative AI Spurs Phone Hardware Upgrades, but Memory Limits Future Development
Appendix
List of Companies