Advances in Brain-Computer Interfaces and Neural Integration
Constraint Topology and the Economics of Deterministic AI Infrastructure a Four-Run NAGI Feasibility Study of Agentic, DeClawed, Hybrid, and Real-World AI Deployment Architectures
Abstract
Ean Mikale
We present a four-run empirical study of AI infrastructure cost economics using the NAGI (Non-Agentic General Intelligence) Feasibility Engine — a deterministic constraint processing system that identified and certified mathematically valid operating configurations for AI deployments. Across four sequential constraint runs spanning 10 to 17 operational parameters, 2,000 sampled states per run, and four distinct architectural scenarios (Agentic Baseline, DeClawed, Hybrid Enterprise, and Real-World Pricing), we demonstrate that AI infrastructure economics is expressible as a fully solvable constraint optimization problem with stable topology. Our central empirical finding is a constraint regime shift: agentic architectures are bound by financial constraints (infrastructure cost saturation at 89–92%), while deterministic architectures are bound by physical efficiency constraints (PUE and token throughput at 91–93%), and hybrid architectures are governed by traffic allocation identity constraints (94% saturation). We further show that under real- world market pricing conditions (AWS GPU rates $2–$20/hr, input token costs $1.00–$1.80/million, energy $60–$75/ MWh), deterministic architectures maintain linear cost scaling while agentic systems exhibit superlinear cost growth [1- 6]. The feasibility boundary exhibits a Hausdorff dimension of 1.000 (smooth) and entropy stability of HR = 3.9120 across all runs, certifying the absence of hidden instabilities or discontinuities. We argue that the perceived unpredictability of AI systems is not an intrinsic property of intelligence, but a consequence of unconstrained architecture.