2025年新材料创业者大会——人工智能与新材料产业机遇圆桌论坛观点分享

Core Viewpoint - The article discusses the transformative impact of AI on the materials industry, emphasizing the shift from traditional knowledge-based advantages to data-driven competitive edges, and the need for individuals to adapt to this new landscape [2][3][9]. Group 1: Trends in the Industry - The traditional industrial competition model, which relied on a "knowledge gradient," is undergoing fundamental changes, with significant implications for materials and chemical industries [3]. - The "knowledge gradient" consists of three barriers: technological barriers (core formulas, patents), application barriers (deep understanding of downstream processes), and first-mover advantages (brand reputation and customer relationships) [3]. - Major industry players are experiencing a collapse of this knowledge gradient, as evidenced by the restructuring of companies like陶氏, 巴斯夫, and 杜邦, driven by the rapid democratization of industry knowledge through open patent databases and academic literature [4][5]. Group 2: Opportunities for Data Application - The current phase presents a critical opportunity for the materials industry to transition from "investment-driven" to "data application" models, leveraging existing digital infrastructure [6]. - The focus should be on breaking down data silos to enhance data flow and value creation across all stages of production, from R&D to supply chain integration [6][7]. - This transition represents a golden window for the industry to capitalize on data value, marking a significant shift in operational strategies [6]. Group 3: Personal Transformation in the Industry - Individuals in the industry are encouraged to evolve from "industry experts" to "industry AI experts," focusing on specific pain points where AI can be effectively applied [7][8]. - Two potential career paths are highlighted: becoming a "cross-disciplinary engineering prototype creator" or transitioning to roles such as "industry AI solution architect" or "data strategy and governance expert" [8]. - The article emphasizes the importance of leveraging deep industry knowledge to drive AI applications, suggesting that those who can effectively combine industry expertise with AI capabilities will be the most valuable in the future [9].