Core Viewpoint - Traditional "one-size-fits-all" storage architecture is a key bottleneck restricting AI development, prompting the need for a new paradigm centered on "scene-defined storage" to optimize storage for specific application scenarios [1] Group 1: Company Insights - Zhiyu Technology emphasizes the transformation of storage from a cost center to an efficiency engine through deep optimization for specific applications [1] - CEO Chen Feiou states that the development of AI represents a "collaborative revolution of computing power and data," highlighting the inefficiencies in current data infrastructure that lead to significant power and computing waste [1] Group 2: Storage Strategy - The core concept of "scene-defined storage" translates business objectives such as latency certainty, power consumption limits, and total cost of ownership (TCO) into primary principles for storage design [1] - This approach signifies a shift where storage evolves from a passive component to a strategic element that actively empowers business operations [1] Group 3: Effective Storage Dimensions - Zhiyu Technology introduces three dimensions of "effective storage": effective performance, storage density, and energy efficiency optimization [1]

Group 1 - The core viewpoint of the articles emphasizes the need for a paradigm shift in storage solutions to address the energy consumption challenges posed by the explosive growth of AI computing power [1][2] - The traditional "one-size-fits-all" storage architecture is identified as a key bottleneck for AI development, prompting the proposal of a new paradigm centered around "scene-defined storage" [1] - The CEO of Zhiyu Technology highlighted that as computing power rapidly increases, inefficient data infrastructure leads to significant power and computing waste, positioning storage as the "axis" of AI development [1] Group 2 - Industry experts discussed the necessity for storage solutions in autonomous driving to balance performance, cost, size, and reliability under safety constraints [2] - The focus of supercomputing and intelligent computing centers has shifted from peak traffic to long-term system stability, suggesting a transition from standardized to scene-customized storage systems [2] - The demand for storage devices in industrial control requires a lifespan of 5 to 15 years, necessitating collaboration between suppliers and chip manufacturers from the design phase [2]