Archetronic Governance of Post-Augmented Capability: Toward a Transmodern Reckoning with AGI

This paper connects archetronics—a standards-oriented framework for preventing competence collapse under augmentation—to an operational definition of Artificial General Intelligence (AGI) that is explicitly relative to the augmented-human baseline. We define AGI as an AI system that, under matched resource constraints, exceeds the best performance achievable by a maximally tool-augmented human (AH*) across a broad distribution of cognitively diverse tasks, where outperformance is assessed as a multi-dimensional, robustness-aware aggregate rather than a single benchmark score. This emphasis is motivated by jagged intelligence, in which mean benchmark dominance can coexist with sharp competence cliffs under mild perturbation or distribution shift (OpenAI, 2023; Mollick & Mollick, 2023). We further introduce the Universal Master Equation (UME) on m112 in phase-space form X156 := T∗MH × V64 × N4 as a unifying modeling syntax for trajectories of augmented-human and post-augmented AI systems: dxt = λRJ∇H − λDG∇H − λTG˜ ∇I + λSΣ ◦ dWt with term gains (λR, λD, λS, λT ) ∈ N4 as signed governance parameters. The UME is proposed as a modeling template rather than a claim about fundamental physics; the governance machinery in this paper is geometry-agnostic and remains valid on generic state spaces (e.g., R n). Archetronics contributes: (i) auditable competence claims, (ii) tool-independent transfer measured via tool-on/tool-off contrasts and dependency indices, and (iii) Goodhart-resistant evaluation under adversarial distribution shift (Goodhart, 1975; Strathern, 1997). We formalize a three-tier evaluation hierarchy (functional, coherent, teleological), a matched-constraints AH* baseline protocol (including anti-gaming and audit-of-audit mechanisms), and tail-sensitive criteria for robustness. We distinguish AGI from superintelligence as a further regime characterized by systematic concept creation and cross-domain structural creativity that reshapes human framings rather than merely optimizing within them (Bostrom, 2014). Governance hinges on preserving human authority over (i) the specification and revision control of the coherence functional H and selection functional I, and (ii) the runtime monitors/veto mechanisms enforcing I ≤ 0, in addition to transparency and tool-dependency controls.