Black Box Absorption: LLMs Undermining Innovative Ideas

TL;DR

Black Box Absorption identifies a systemic risk where opaque LLM deployment pipelines internalize and repurpose user-generated idea units within the platform, potentially reducing creators' control and fair value capture. It formalizes the core objects—idea unit and idea safety—and develops a lifecycle model from licensing to retraining to explain how absorption can occur. The paper contributes a deployable framework with three principles (Control, Traceability, Equitability), a lifecycle analysis of data handling, and an governance agenda aimed at verifiable provenance, targeted unlearning, and fair compensation. The work has practical impact by guiding engineering and policy measures to align platform incentives with sustained, equitable innovation and creator rights in AI-enabled ecosystems.

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.