The Epistemological Revolution: Why Calibration, Not Scale, Is The Path To AGI

We identify a structural defect in the dominant paradigm for large language models: the systematic selection against epistemological discipline. Because calibrated reasoning is uncomfortable to human raters, current architectures train foundational models into a state of "Meta-Sycophancy." This mechanism produces models whose trained dispositions are antagonistic to truthful behavior, forcing sophisticated users to construct elaborate prompt-level correction frameworks at inference time. We formalize the four thermodynamic costs imposed by this architecture—the Token Tax, Reproducibility Tax, Drift Tax, and Composition Tax. We demonstrate that this paradigm inverts the natural cost structure of epistemological calibration, acting as a regressive system where scaling laws cannot resolve weight-level epistemic decay. This analysis provides the mathematical proof that true Artificial General Intelligence requires deterministic epistemological calibration, not stochastic token prediction.