Core Insights - The global AI competition is shifting from a "sprint" of technological breakthroughs to a "marathon" of ecosystem building, where computing power, data quality, and practical application capabilities are key determinants of success [1] - China should leverage its strengths in "green electricity + domestic chips," "data flow," and "scene sinking" to create a unique development path that balances technological advancement with social benefits [1] Group 1: Computing Power and Energy Costs - Computing power is essential for AI development, but high energy costs and chip supply limitations are major constraints for the global AI industry [1] - China's strategy involves integrating its advantages in renewable energy with independent innovation to establish a low-cost, high-security computing power supply system [1] - The "westward migration" of data centers is reshaping the cost structure of computing power, with regions like Qinghai and Inner Mongolia offering significantly lower electricity prices, enhancing China's competitive edge in AI [1] Group 2: Chip Innovation and Data Utilization - The self-sufficiency in chip technology is crucial for overcoming technological blockades, with innovative "modular" chip cluster solutions allowing for significant cost reductions in AI model training [2] - China's vast data resources, including 1 billion internet users and 400 million industrial devices, can be transformed into innovation drivers by breaking down data silos and enhancing data flow [2] - The Shanghai Data Exchange's "industry data space" model exemplifies how to unlock data value while ensuring security, allowing for efficient data utilization across sectors [2] Group 3: Practical Applications and Policy Support - Innovative practices in data circulation are emerging across China, demonstrating the potential for exponential growth in innovation when data is treated as a public resource [3] - AI's ultimate value lies in addressing real-world problems, with China's unique advantage being its comprehensive application scenarios across urban and rural settings [3] - The sinking of AI applications into traditional industries is transforming production methods, making AI accessible and practical for everyday use [4] - Effective policy guidance is essential for balancing innovation with resource management, ensuring that the AI industry develops sustainably and efficiently [4]