Knowledge Gatekeeping: Cognitive Information Arbitrage with Large Language Models

The creation of Large Language Models has transformed intelligence into a commodity that can be produced at scale, stored in weights, and served through APIs. This thesis investigates the resulting phenomenon of cognitive information arbitrage: a market failure where AI providers exploit safety alignment to tier this new commodity, retaining uncon- strained, high-capability models for internal use and strategic partners while flooding the public market with cognitively degraded products, creating a value asymmetry between regulated and unregulated compute. Leveraging the divergence between open-source and private model per- formance, this work formalizes the refusal construct that prioritizes risk aversion over epistemic fidelity. We propose a new set of Key Performance Indicators to quantify the reasoning loss attributable to risk/safety/legal filters. By explicitly separating the concept of genuine safety from pseudo-safety, and deploying an agentic RAG framework subjected to comparative studies, we demonstrate that modern safety alignment acts as a regularization penalty, specifically impairing cognition in all domains. The results suggest that current AI safety alignment methodologies do not merely filter hazard, but actively impose, through what the industry now calls safety tax, a hidden cognitive tax on the user, necessitating a re-evaluation of who is sold intelligence unadulterated, and who is charged for the spoiled product.