The Metabolic Discount As Poison/Remedy: Large Language Models as Semantic Infrastructure

The struggle over Large Language Models is not a technical debate; it is a political war over

who governs the infrastructure of meaning and for whose benefit. Your next query delivers a

seamless answer—a metabolic subsidy for your nervous system. The bill is paid elsewhere: as

PTSD in a Nairobi content moderator, as carbon debt in a heating sky, as the quiet erasure of a

thousand ways of knowing that could not be scraped. The interface glows softly, apologizing

for the war it conceals. Every token cuts both ways.

This paper analyzes this metabolic discount through the lens of Derrida’s pharmakon—a

substance intrinsically both remedy and poison. It argues that the discount’s emancipatory

potential is inseparable from a metabolic shadow of exploited labor and ecological cost, and

that its design under platform capitalism structurally favors cognitive capture. We examine

how Reinforcement Learning from Human Feedback (RLHF) enacts epistemic closure, how

the recursive contamination of training data threatens irreversible epistemicide, and how the

dynamics of platform capitalism push the pharmakon toward its poison-face. Distinguishing

between scaffold deployment and infrastructure capture, the paper proposes the principle

of “the body votes last” as a navigational practice and identifies endogenous feedback loops

through which organized resistance could contest the default trajectory toward capture.