🤖 AI Summary
This work addresses the inadequacy of traditional open-source licenses, which rely on auditable relationships between source and object code, in governing Artificial General Intelligence (AGI) systems composed of multidimensional components such as code, data, weights, and hyperparameters. To overcome this limitation, the paper proposes “reproducible builds” as a new Copyleft paradigm for the AGI era, replacing conventional code-sharing logic by ensuring models can be precisely reconstructed from declared inputs. It establishes reproducible builds as a cornerstone of AGI governance, articulates seven technical requirements, and identifies the Model Context Protocol (MCP) as a novel dynamic linking layer necessitating a “protocol-not-platform” governance framework. Integrating OSAID, MOF, OpenMDW, and deterministic inference, the study formulates a technically and legally aligned system enabling bit-accurate model reconstruction, thereby providing both theoretical grounding and practical pathways for AI open-source licensing.
📝 Abstract
Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying the availability of source code to every act of distribution. Its normative force rested on an implicit technical premise: that source code and object code stand in a well-defined, humanly auditable, and reproducible relationship. Large language models and, prospectively, Artificial General Intelligence (AGI) systems systematically violate this premise. The artifacts jointly required to reconstruct a model -- code, data, weights, hyperparameters, toolchain, and hardware configuration -- are each subject to independent legal, technical, and economic constraints that no current open-source framework fully resolves. Sufficiently capable AI systems can also rewrite licensed source into functionally equivalent derivatives stripped of their original obligations, a form of laundering against which copyleft has no effective defense. This paper argues that a functional analogue of copyleft for AGI must be grounded not in share-alike clauses over code, but in reproducible builds: a practice guaranteeing bit-exact reconstructability from declared inputs. We review the logic of copyleft, critically examine Maffulli's Second Liberation thesis according to which AI fulfills Stallman's dream, and show that the argument collapses unless AGI systems are themselves reproducible. Drawing on the Open Source AI Definition (OSAID), the Model Openness Framework (MOF), OpenMDW, and deterministic-inference research, we define seven requirements for AGI-oriented reproducible builds. We further argue that the Model Context Protocol (MCP) and analogous AI-to-AI coupling mechanisms constitute a new dynamic linking layer for which copyleft-style licensing is ill-suited, and that Masnick's "protocols, not platforms" framework offers a more promising governance template.