Guoxin Securities·2025-07-23 13:20Investment Rating - The report maintains an "Outperform" rating for the AI application industry [1] Core Insights - The capabilities of AI models are rapidly improving, driven by open-source initiatives that lower costs. Large models have achieved new heights in knowledge Q&A, mathematics, and programming, surpassing human-level performance in various tasks. The introduction of high-performance open-source models like Llama 3.1 and DeepSeek R1 has narrowed the gap between open-source and closed-source models [2][5] - AI agents are becoming more sophisticated, with a surge in new product releases. These agents can perceive their environment, make decisions, and execute actions, enhancing their functionality through the integration of external tools and services [2][30] - The commercial use of AI is on the rise, with significant growth in usage and performance of domestic models. The gap between top models in China and the US is closing, supported by a continuous increase in global AI model traffic [2][50] - AI applications are reshaping traffic entry points, with traditional internet giants leveraging proprietary data and user engagement to integrate AI functionalities into existing applications [2][50] - The open-source movement is increasing investment willingness and accelerating cloud adoption among enterprises, as the proliferation of development tools lowers industry application barriers [2][50] Summary by Sections Model Layer: Rapid Capability Enhancement and Cost Reduction - The mainstream model architecture is shifting towards MoE, allowing for more efficient resource use while enhancing performance. Models like DeepSeek-V3 and Llama 4 have demonstrated low-cost, high-performance capabilities [8][9] - The multi-modal capabilities of models have significantly improved, enabling them to process various data types, thus expanding application scenarios [8][9] - The introduction of chain-of-thought reasoning techniques has improved the accuracy and reliability of model responses [8][9] Commercialization: Continuous Growth in Usage and Strong Performance of Domestic Models - The competition among vendors has led to a significant decrease in inference costs, benefiting application developers and end-users [21][22] - The API call prices for major models have dropped substantially, with some models seeing reductions of up to 88% [21][22] AI Agents: Technological Advancements and Product Releases - AI agents are evolving from traditional models to more autonomous entities capable of independent decision-making and task execution [30][31] - The introduction of protocols like MCP and A2A is enhancing the capabilities and interoperability of AI agents, facilitating complex task execution across different systems [38][39] C-end Applications: AI Empowering Business and Reshaping Traffic Entry - AI applications are expected to redefine traffic entry points, with major players actively positioning themselves in this space [2][50] B-end Applications: Open-source Enhancing Investment Willingness and Cloud Adoption - The development of open-source tools is significantly lowering the barriers for industry applications, accelerating the intelligent transformation of various sectors [2][50]