Core Viewpoint - The article discusses the rapid development of AI in China, highlighting the importance of global cooperation in AI governance and the potential risks associated with technology misuse [1][3]. Group 1: AI Development in China - In the past two years, China's AI technologies, particularly large models like DeepSeek, Mini Max, and Kimi, have achieved remarkable global recognition [3][5]. - Chinese AI models are characterized by their open-weight approach, contrasting with the closed strategies of many leading models in the U.S. [5]. Group 2: Global Cooperation and Governance - Eric Schmidt emphasizes the necessity of open dialogue between China and the U.S. to navigate the challenges posed by AI and to foster a responsible and sustainable future [3][8]. - The establishment of a continuous dialogue mechanism is crucial for both sides to define issues clearly and seek collaborative solutions [8][10]. Group 3: Risks and Ethical Considerations - There are concerns regarding the potential misuse of AI technologies, including issues of deception and harmful behaviors that AI systems might learn [11]. - The need for a balance between open-source technology and regulatory measures is highlighted, as open-source can lead to rapid dissemination of technology, which may pose risks [10][11]. Group 4: Future Outlook - The next two years are expected to witness the emergence of intelligent agents that can perform tasks and interact within various workflows, significantly impacting businesses and governance [14][15]. - There is optimism about the potential for AI to bring about profound societal changes, provided that key concerns are addressed through dialogue and cooperation [15].

Core Viewpoint - The article discusses the challenges faced by the public fund industry, emphasizing the need for a more integrated and systematic investment research approach to enhance investor satisfaction and achieve sustainable excess returns [1][10]. Group 1: Pain Points and Solutions - The traditional investment research model in the asset management industry suffers from issues such as a lack of collaboration, fragmented processes, and insufficient quality control, which hinder the ability to deliver consistent performance [1][3]. - Tianhong Fund has introduced the TIRD platform, which aims to integrate investment research processes and leverage digital technology to transform subjective experience into quantifiable processes, thereby making investment decisions more scientific and replicable [1][4]. Group 2: TIRD Platform Features - The TIRD platform incorporates a closed-loop management process that includes documentation, feedback, review, and iteration across all stages of research, decision-making, investment, trading, and performance analysis [4]. - It provides an intelligent dashboard for fund managers, offering real-time insights into portfolio characteristics and suggesting adjustments based on various market scenarios, thus enhancing decision-making efficiency [4][6]. - The platform standardizes communication through a "pricing odds table," allowing for a more efficient and accurate interaction between researchers and fund managers, ultimately leading to a more predictable investment experience for clients [4][6]. Group 3: Impact on Research Efficiency - The TIRD platform has significantly improved research efficiency by automating the monitoring of key industry indicators and providing timely alerts to the research team, which helps in capturing critical market movements that may otherwise be overlooked [6][7]. - It enhances internal information exchange by ensuring that all research interactions are documented, making the process traceable and iterative, thus improving overall communication efficiency within the team [7]. Group 4: Future Directions - The TIRD platform is set to expand its capabilities beyond active equity strategies to include index-enhanced areas and fixed income, indicating a comprehensive digital transformation across all business lines [9]. - Future developments include the introduction of specialized AI-driven research assistants that can independently track multiple stocks, enhancing the research capabilities of human analysts [9]. - The platform aims to evolve into a digital assistant for fund managers, further integrating advanced technologies to maintain a competitive edge in the asset management industry [9].