SORT-AI: Structural Efficiency Recovery in Hyperscale AI Systems A Diagnostic Framework for Throughput, Control, and Orchestration Losses

Large-scale AI infrastructure exhibits a persistent efficiency paradox: despite continuous hardware advancement and algorithmic refinement, realized performance often falls substantially below nominal capacity specifications. Contemporary optimization efforts focus primarily on accelerator utilization, model architecture, and serving algorithms, yet empirical evidence from production deployments indicates that structural losses arising from interconnect contention, control layer conflicts, and orchestration overhead constitute a distinct and largely unaddressed inefficiency surface. This work introduces the SORT-AI framework, a diagnostic approach to structural efficiency analysis in hyperscale AI systems. Rather than proposing implementation-specific optimizations, the framework formalizes the relationship between nominal hardware capacity and effective system throughput through the lens of structural coupling across three failure domains: synchronization-induced losses in distributed training, memory-control friction in inference serving, and intent propagation failures in agentic orchestration environments. We establish a taxonomy of structural losses that distinguishes between recoverable inefficiencies arising from system-level coupling and fundamental algorithmic or hardware constraints. Through application of the framework to interconnect stability, runtime control coherence, and agentic system coordination, we identify structural recovery bounds that represent efficiency improvements achievable without hardware replacement or runtime substitution. The framework operates as a diagnostic layer that surfaces inefficiencies typically obscured by component-level metrics, providing architectural visibility into coordination failures that manifest as ghost compute, stranded capacity, and emergent cost amplification. This work does not provide benchmarks, performance guarantees, or implementation prescriptions. Instead, it establishes a conceptual foundation for understanding efficiency recovery as the inversion of structural instability, offering a systems-level perspective on why optimization efforts often underperform expectations and where architectural assessment can yield operational insights.