Black Box Absorption: LLMs Undermining Innovative Ideas

TL;DR

This paper highlights a systemic risk in using Large Language Models for innovation, where proprietary platform architectures can absorb and repurpose user ideas, threatening the sustainability of the innovation ecosystem.

Contribution

It formalizes the concept of Black Box Absorption, analyzes its mechanisms, and proposes governance and engineering strategies to protect creator contributions.

Findings

Identifies systemic risk of idea absorption by LLM platforms

Introduces idea unit and idea safety concepts for protection

Proposes mitigation strategies for safeguarding innovation

Abstract

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.