长三角议事厅·周报｜打通AI商业化梗阻，长三角如何破题

Core Insights - The global AI competition is intensifying, shifting from a focus on "parameters and performance" to "ecosystem and implementation," with an emphasis on sustainable revenue and real demand [1] - The Yangtze River Delta (YRD) region is well-positioned to leverage its complete industrial chain and digital economy foundation, but faces challenges in translating technology into measurable business value [1][4] - The YRD's AI development strategy is evolving towards a systematic ecosystem that integrates chips, models, platforms, and applications, moving away from isolated technological advancements [2] Group 1: AI Industry Development in the Yangtze River Delta - The YRD is transitioning from a focus on individual technological breakthroughs to building a comprehensive ecosystem that supports AI commercialization [2] - Shanghai aims to lead in smart terminal development, targeting a market scale of 300 billion yuan by 2027, integrating chips, algorithms, and consumer-grade AI products [2] - Zhejiang is focusing on healthcare AI applications, planning to establish a national AI medical application base by 2027 [3] - Jiangsu is leveraging its manufacturing advantages to establish over 70 industry standards by 2027, enhancing the usability of industrial AI [3] - Anhui is targeting a revenue of 100 billion yuan in AI by 2027, focusing on embodied intelligence and future forms of AI [3] Group 2: Barriers to AI Commercialization - The first barrier is high costs associated with model training and deployment, which limits the ability of small developers to iterate from prototypes to products [4][5] - The second barrier involves regulatory uncertainties, where changing compliance requirements can deter investment in AI projects [5] - The third barrier is the lack of compatibility between domestic computing power and mainstream models, leading to inefficiencies and increased costs [5][6] Group 3: Strategies for Overcoming Barriers - The YRD must shift from isolated technological advancements to a full-chain ecological approach to make AI a sustainable productivity tool for businesses [6][7] - Establishing mechanisms for regional collaboration is essential, allowing resources to be shared and utilized effectively across the region [7] - Infrastructure needs to be more accessible and productized, reducing the initial costs for businesses to adopt AI technologies [7][8] - Expanding high-quality scenario offerings and fostering long-term collaborations between AI companies and traditional industries will help transition from pilot projects to sustainable implementations [8]