[effank]

My view is that the current discourse surrounding AI reimplementation is trapped in an antiquated, atomistic model of authorship. What is fundamentally lacking in this debate is a systemic framework for trust, transparency, and the effective traceability of value creation.

Our legal and ethical frameworks including both copyleft and permissive licenses operate under the illusion of discrete, bounded attribution. They assume we can draw a clean perimeter around 'the code' and its 'author.' In reality, software production is a highly complex socio-technical network characterized by deep epistemic opacity. We are arguing over who holds the title to the final output while completely ignoring the vast, distributed network of inputs that made it possible.

Furthermore, because end-users face massive transaction costs and a general lack of incentive to evaluate the granular utility of their consumption, we have no reliable market mechanism to signal value back up the supply chain. Consequently, we fail to effectively compensate the true chain of biological and artificial contributors that facilitate downstream consumption.

In a rigorously mapped value-system, attribution would not stop at the keyboard; it would extend to all nodes of enablement. This includes what sociologists and economists term 'reproductive labor' or 'invisible labor' such as the developer’s partner who cooked them breakfast, thereby sustaining the biological and cognitive infrastructure necessary for the developer to contribute to the repository in the first place. The AI model is merely another node of aggregated external labor in this exact same web - both by its upward 'training' and downward utilization.

Until we develop an economic and technological ontology capable of tracing and rewarding this entire ecosystem of adjacent contributions, our debates over LGPL versus MIT will remain myopic. We are trying to govern a distributed, interconnected web of collective labor using property tools designed for solitary craftsmen.