The Hexad Methodology: Toward an Environmentally Sustainable Pathway to AGI

Abstract The pursuit of Artificial General Intelligence (AGI) has historically been characterized by escalating computational and energetic demands, raising profound concerns about ecological sustainability. This paper introduces the Hexad Methodology—a six■principle framework for developing AGI within planetary boundaries. Drawing on thermodynamics, resource economics, embodied cognition, and ecological governance, the Hexad proposes that environmental constraint is not an impediment to AGI but a necessary condition for its ethical and practical realization. We argue that the most resource■efficient pathway—leveraging neuromorphic computing, sparse architectures, renewable integration, and circular hardware economies—is also epistemologically superior, as it forces a disciplined alignment between intelligence and its material substrate. Unlike existing “Green AI” efforts that treat efficiency as a post■hoc optimization, the Hexad embeds ecological constraint as a first■order architectural and governance principle. We conclude that indefinite exponential scaling is ecologically unsustainable, and that constraint■driven design may yield more general, robust, and legitimate forms of intelligence.