From Frontier to Public Good: Governing the AI Stack as Economic Architecture for Inclusive Productivity, Trust, and Resilience

Abstract

Artificial intelligence is a general-purpose technology, and like the technologies before it, its value surfaces only through complementary investment in skills, data, organizational redesign, and institutions. History is suggestive here: the size and distribution of the gains turned at least as much on the institutions built around a technology as on the technology itself. AI governance, by contrast, is still treated largely as content moderation of generative outputs. This paper takes a different view. Governance is itself a critical complement, and the distribution of AI's productivity gains is, we hypothesize, partially endogenous to how governance is designed rather than fixed by capability alone. We treat AI as a full stack, from foundational machine learning, computer vision, and reinforcement learning through the foundation-model and agent layers down to compute, hardware, and a material base of minerals and energy; within that stack, compute may be the most governable layer under present institutional conditions. The paper sets out a fullstack operational definition of AI governance, a complementaryinvestment framing, a measurable enabling condition that weighs innovation lift against compliance drag, a three-layer architecture coupled through conformity assessment and mutual recognition, and an operational measurement framework. It is conceptual and formal rather than empirical: an analytically structured framework built for later empirical testing.