GF Securities' most recent research report highlights that AI memory is emerging as a foundational capability essential for maintaining contextual continuity, enabling personalized services, and facilitating the reuse of historical data. This development is propelling AI models from being mere "one-time reasoning tools" to evolving into intelligent systems capable of long-term value accumulation. With the systematic integration of both short-term and long-term memory mechanisms, the value proposition of AI memory is undergoing a transformation from being perceived as a "cost center" to being recognized as an "asset."

The commercial significance of AI memory manifests across three key dimensions: At the application level, it fosters sustained growth by enhancing user retention rates and boosting Average Revenue Per User (ARPU) values. Within the computing power domain, it reduces redundant reasoning expenses by enabling the reuse of computational resources across different timeframes. At the enterprise level, it constructs competitive advantages through the development of proprietary knowledge bases.

From a technical architecture standpoint, the "three-tier memory system" outlined in the research encompasses the formal layer (comprising short-term context, model parameters, and latent features), the functional layer (including factual memory, experiential memory, and working memory), and the dynamic layer (encompassing memory evolution and retrieval mechanisms). This framework signifies the AI industry's transition into a "phase of long-term value accumulation."

In terms of industrial prospects, upstream infrastructure components such as memory chips, in-memory computing chips, and vector databases are poised to directly capitalize on this trend. Additionally, the rapid deployment of AI Agents and similar applications is expected to further unlock substantial market potential.