从规模竞赛到系统突围：2025年中国AI基础设施共识重塑

Core Insights - The investment scale and construction progress of AI and data infrastructure in China are still in an expansion phase as of 2025, with continuous establishment of computing centers and advancement of data platform projects across various regions [1] - Unlike previous growth phases, the current expansion is characterized by a shift in focus from mere scale to the importance of return on investment (ROI) as the primary evaluation criterion [1][2] - The year 2025 is seen as a critical point for differentiation in the industry, marking a transition from a phase driven by narrative and vision to one focused on engineering and system capabilities [1][7] Investment and Infrastructure Development - The core task of AI and data infrastructure construction in recent years has been to address resource shortages, such as computing power and data availability [2] - As companies move into 2025, the focus is shifting to whether these systems are worth integrating into core processes for the long term, with increasing emphasis on system stability, operational costs, and data governance [2][3] - In the biopharmaceutical sector, the requirements for AI and data systems differ fundamentally from those in internet or lightweight application scenarios, emphasizing the need for traceability and reliability in results [2][3] Project Failures and Common Issues - As more projects enter operational phases, failures are becoming more common and exhibit consistent structural characteristics, such as high reliance on manual processes and challenges in data quality [3][5] - Many projects that perform well in early validation stages face significant issues when scaling, often requiring complete system reconfiguration for different business scenarios [3][5] - The focus of the industry is shifting from whether the technology is advanced to whether the systems are fundamentally sound [3][5] Investment Reassessment - Investors are beginning to reassess which projects are viable, with a recognition that failed projects often share common traits rather than being random occurrences [4][5] - Successful projects are expected to possess structural integrity rather than relying on "light asset stories," indicating a preference for systems with hard constraints [5][6] Redefining Hard Assets - As the industry reflects on scale logic, previously overlooked areas such as data governance and industry-level AI infrastructure are being reevaluated for their high customer stickiness and replacement costs [6] - In the biopharmaceutical field, data systems integrated into core processes become part of compliance frameworks, highlighting their critical role beyond mere tools [6] - Key characteristics of resilient assets include compliance barriers, data closed-loop capabilities, and low long-term operational costs, which enhance their survival during industry differentiation [6] Industry Evolution - The year 2025 does not lead to a unified answer for the industry; instead, it accelerates differentiation, with some projects attempting to cover issues through scale while others focus on structural integrity [7] - This differentiation does not signify industry decline but rather marks the evolution of AI in China from experimental tools to foundational industrial components [7][8] - The true value of systems will begin to emerge as the focus shifts from scale to the ability to effectively implement and sustain these systems [8]