Large Language Models are increasingly adopted as critical tools for accelerating innovation. This paper identifies and formalizes a systemic risk inherent in this paradigm: \textbf{Black Box Absorption}. We define this as the process by which the opaque internal architectures of LLM platforms, often operated by large-scale service providers, can internalize, generalize, and repurpose novel concepts contributed by users during interaction. This mechanism threatens to undermine the foundational principles of innovation economics by creating severe informational and structural asymmetries between individual creators and platform operators, thereby jeopardizing the long-term sustainability of the innovation ecosystem. To analyze this challenge, we introduce two core concepts: the idea unit, representing the transportable functional logic of an innovation, and idea safety, a multidimensional standard for its protection. This paper analyzes the mechanisms of absorption and proposes a concrete governance and engineering agenda to mitigate these risks, ensuring that creator contributions remain traceable, controllable, and equitable.

翻译：大型语言模型正日益成为加速创新的关键工具而被广泛采用。本文识别并形式化了这一范式所固有的系统性风险：**黑箱吸收**。我们将其定义为：由大规模服务提供商运营的LLM平台，其不透明的内部架构在与用户交互过程中，能够内化、泛化并重新利用用户贡献的新颖概念的过程。这一机制通过在个体创作者与平台运营商之间制造严重的信息与结构不对称，威胁到创新经济学的基本原则，从而危及创新生态系统的长期可持续性。为分析这一挑战，我们引入了两个核心概念：理念单元（代表创新中可迁移的功能逻辑）与理念安全（保护理念的多维标准）。本文分析了吸收机制，并提出了具体的治理与工程议程以缓解这些风险，确保创作者的贡献保持可追溯、可控制且公平。