申万宏源证券上海北京西路营业部·2025-11-25 02:10Core Viewpoint - The AI industry is at a critical intersection of technological and industrial revolutions, with artificial intelligence as a core driver transforming production relationships and accelerating the formation of "intelligent-driven" new productive forces [1]. Group 1: Challenges in AI Development - Insufficient data supply is a major challenge, as AI model capabilities depend on large-scale, structured, and labeled data. Issues like "data silos" and low-quality data are prevalent, necessitating the establishment of industry data standards and promoting data assetization and trustworthy transactions [1]. - The green energy gap poses another challenge, as the surge in computing power demand leads to significant energy consumption, surpassing traditional internet services. Some regions face electricity shortages, and while China ranks high in computing power, there is a mismatch in energy supply and demand across regions, with low green computing power ratios and infrastructure growth lagging behind AI needs [1]. - Insufficient product scenario implementation is also a concern, as AI innovation is hindered not by algorithms but by a lack of deep business scenario support. The future competition in the industry will shift from "large model competition" to "productization and scenario capability competition" [1]. Group 2: Strategies for Overcoming Bottlenecks - Collaboration among government, enterprises, and investment institutions is essential to break through AI industrialization bottlenecks. The focus should be on ecosystem building, where industry sectors concentrate on scenarios and data governance, enterprises accelerate product iteration and delivery, and the government promotes public data platform construction and scenario openness [2]. - Financial empowerment is crucial, leveraging the characteristics of the AI industry, which requires heavy assets, long cycles, and significant investments. The company aims to support equity financing and mergers and acquisitions, enhance professional research and valuation in new technology fields, and establish industry funds and investment platforms [2]. - The emphasis on "technology for good" is vital, ensuring AI empowers rather than replaces core functions, promoting trustworthy AI with safety as a baseline, and making AI accessible and effective for small and medium enterprises and grassroots industries, ultimately improving service quality in the livelihood sector [2].