Research on the construction and risk identification of Large Language Model industrial innovation ecosystem

大模型产业创新生态系统构建及风险识别研究

Abstract

This paper investigates the construction logic and risk profile of the Large Language Model (LLM) industrial innovation ecosystem in the context of accelerated AI-driven transformation. Drawing on innovation ecosystem and platform economy theory, we first decompose the LLM ecosystem into four core subsystems: foundational layer (computing power, data, basic models), application layer (vertical fine-tuned models and scenario solutions), support layer (tools, MLOps, evaluation and security services) and governance layer (standards, regulation, ethics and governance bodies). On this basis, we construct an ecosystem framework featuring “platform–complementor–user–governor” multi-actor collaboration, and clarify the key mechanisms of value co-creation, data feedback, and iterative innovation. Further, we develop a risk identification scheme covering five categories: technological risk (hallucination, robustness, controllability), data and security risk (privacy, leakage, abuse), economic risk (platform monopoly and crowding-out of SMEs), social and ethical risk (bias, misinformation, labour displacement), and systemic governance risk (regulatory lag and fragmented supervision). The paper argues that high-intensity coupling among actors amplifies both innovation efficiency and systemic vulnerability, requiring a shift from “single-firm risk control” to “ecosystem-level resilient governance”. Finally, policy and managerial implications are proposed for building a safe, open and sustainable LLM industrial innovation ecosystem.