Scaling Risks in Probabilistic AI Systems

This document constitutes a Whitepaper / foundational paper. It establishes the mechanical basis of drift, defines the structural limits of probabilistic AI systems, and provides the deterministic comparison model required for governance, auditability, and stability. Probabilistic AI systems exhibit structural instability because their outputs emerge from non‑deterministic transitions rather than reproducible computational pathways. As models scale, parameter growth, parallel signal processing, and expanding error landscapes amplify drift exponentially, creating a scaling risk that increases faster than any available technical or institutional control capacity. Beyond a critical complexity threshold, internal transitions can no longer be traced and auditability collapses: the origin, mechanics, and reliability of individual model decisions become irreconstructable. Parallel computation intensifies this instability because parallel signals have no deterministic sequence; each signal opens its own transition space, multiplying divergence and accelerating drift. The analysis shows that probabilistic AI systems do not become more stable with size. Instead, scaling multiplies non‑determinism, expands instability, and produces system behavior that is incompatible with environments requiring reproducibility, traceability, and governance control. The paper establishes the mechanical foundations of this instability and demonstrates why the limits of probabilistic AI are structural rather than infrastructural — and why scaling transforms probabilistic systems into systemic risks across technical, administrative, and political domains.