Crest-Null: The AGI Tipping Point

Abstract

The AGI Tipping Point investigates whether a measurable threshold exists at which one artificial intelligence system acquires a sustained competitive advantage that other frontier models can no longer overcome. Drawing on Crest-Null Philosophy and its examination of complexity, optimization, entropy, and systemic persistence, this paper develops a general mathematical criterion for identifying such a threshold without assuming which AI system, corporation, or technological architecture might achieve it.

The investigation considers how computational capacity, algorithmic efficiency, energy availability, industrial infrastructure, and thermodynamic costs interact to determine the relative capabilities of competing AI systems. Particular attention is given to the possibility that recursive technological improvement could create self-reinforcing advantages, while physical constraints, diminishing returns, and resource limitations impose boundaries on continued expansion.

The analysis establishes a conditional framework for evaluating competitive dominance over defined future periods and identifies the empirical measurements necessary to determine whether a particular frontier AI system is approaching such a threshold. Although the mathematics demonstrates how a sustained advantage could emerge, currently available evidence does not establish that any existing AI system has crossed, or is demonstrably approaching, an irreversible tipping point.

Situated within the broader Crest-Null research program, the paper raises a further question: whether the same processes of optimization and increasing interdependence that produce extraordinary technological capabilities might eventually create new forms of systemic fragility. The resulting framework offers a falsifiable approach to investigating the competitive evolution of artificial intelligence, the physical limits of computational growth, and the conditions under which technological dominance might emerge, persist, or collapse.