Core Insights - The core argument emphasizes the importance of "model fine-tuning" and "model inference application" for companies to achieve high-quality development through AI technology [1][2]. Group 1: Model Development and Application - The AI model lifecycle includes data acquisition, preprocessing, training, fine-tuning, and inference, with model training being the most critical phase [1]. - Due to the lack of specialized domain data, foundational models require "model fine-tuning" to adapt to specific industry needs, transforming general capabilities into specialized applications for sectors like healthcare, finance, and manufacturing [1]. Group 2: Efficient Implementation Strategies - Companies are advised to leverage existing foundational models from specialized tech firms like DeepSeek and Huawei, rather than investing heavily in initial data acquisition and training [2]. - The architecture of AI PC, centered around GPUs, offers significant computational advantages, enabling the development of personalized AI assistants for individuals [2]. Group 3: AI's Role in Business Transformation - AI is positioned as a core infrastructure rather than a mere IT tool, with the next competitive battleground for companies being the integration of data algorithms and computational efficiency [3]. - AI serves as a second engine for growth, reshaping products, services, and operational models, thereby enhancing revenue and profit margins [3]. - By optimizing internal processes and reducing operational costs, AI creates significant competitive advantages and barriers to entry for businesses [3].