The Leveling Theory of AI: A Framework for Capability Boundaries

This paper introduces the Leveling Theory as a framework for understanding the capability boundaries of contemporary large language models. The theory argues that current AI systems primarily function by flattening humanity's existing knowledge stock into a form that is instantly searchable and combinable, rather than by creating new knowledge or supplying new forms of energy. Within closed rule spaces AI can exhaustively search (as demonstrated by AlphaGo Zero); outside those spaces it remains constrained by the boundaries of its training distribution. Leveling itself constitutes a productivity leap, yet genuine qualitative change depends on subsequent cross-domain recombination and the writing of new accounts by human scientists. The paper distinguishes three roles (leveler, accelerator, and energy source) and situates present-day AI firmly in the first category while examining its limited comparability with the steam engine and electrification. AI disclosure: This paper was written with the assistance of AI tools. The author is responsible for its content.