Guo Ji Jin Rong Bao·2025-11-20 23:57Core Insights - The AI industry is at a critical intersection of a new technological and industrial revolution, with AI as a core driver transforming production relationships and accelerating the formation of "intelligent-driven" new productive forces [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 [2] - The green energy gap poses another challenge, as the surge in computing power demand leads to significant energy consumption. Training mainstream large models requires substantial GPU resources, resulting in energy consumption far exceeding traditional internet services. Some countries are experiencing power shortages, and while China ranks high in computing power, there is a regional imbalance in energy demand and supply, with low green computing ratios and infrastructure growth lagging behind AI needs [2] - Insufficient product scenario implementation is hindering AI innovation, as the focus is not on algorithms but rather on the lack of deep business scenario support. The future competition in the industry will shift from "large model competition" to "productization and scenario capability competition" [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 AI competition shifts from single-point technological breakthroughs to systematic collaboration, emphasizing scenario and data governance [3] - 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 [3] - Promoting "technology for good" is vital, with a focus on AI enabling rather than replacing core functions. Safety should be the baseline for promoting trustworthy AI, while ensuring accessibility for small and medium enterprises and grassroots industries, enhancing service quality in the livelihood sector through human-machine collaboration [3]