The AI Scaling Trap: Structural Limits of Capability Gain Through Resource Intensification in Artificial Intelligence Systems — A Zero Leap Theory Analysis

Can artificial intelligence capabilities be indefinitely improved by scaling computational resources? This paper applies Zero Leap Theory (ZLT) to AI scaling dynamics, demonstrating that resource intensification under closed internal gates produces structural outcomes isomorphic to those observed in other forced-improvement systems: diminishing returns, capability degradation, and eventual collapse. We introduce the AI Scaling Trap: any sustained attempt to improve AI system capabilities through increased compute, data, or parameters when internal structural gates (permeability, stability, data quality, alignment, hysteresis) fall below critical thresholds. Through formal analysis integrating scaling laws research (Kaplan et al. 2020), model collapse studies (Shumailov et al. 2023), and compute-efficient frontier analysis, we demonstrate that scaling failures are not resource limitations but structural inevitabilities. The paper proves the Bounded Scaling Theorem: under specified gate-closure conditions, there exists a compute-efficient frontier beyond which additional resource investment produces zero or negative capability returns. We develop IAS-SCALE (Audit Standard for Scaling Decisions) and establish the Data Quality Anchor Requirement as a necessary condition for scaling effectiveness.