AI-Native Autonomous Infrastructure (ANAI): A Formal Framework for the Next General-Purpose Technology
For researchers and policymakers assessing AI's role as a general-purpose technology, this framework provides a conceptual and mathematical foundation to evaluate structural transformation, but it remains theoretical with no empirical validation or concrete numbers.
The paper proposes a formal framework to evaluate AI as a systemic infrastructural transition rather than a computational breakthrough, introducing quantitative constructs (Autonomy Index, Infrastructure Coupling Coefficient, Technological Transition Potential) to model the transition. It claims that AI-driven transformation exhibits a recursive energy-computation feedback loop that accelerates infrastructural embedding, differentiating it from prior technological revolutions.
Artificial intelligence is increasingly described as a candidate next generation general purpose technology (GPT). However, existing interpretations predominantly emphasize performance scaling rather than structural transformation. This paper introduces a formal framework for evaluating AI as a systemic infrastructural transition rather than merely a computational breakthrough. We propose the concept of AI Native Autonomous Infrastructure (ANAI), defined as a regime in which decision autonomy becomes embedded within critical infrastructures. The framework operationalizes this transition through three quantitative constructs: the Autonomy Index (AIx), the Infrastructure Coupling Coefficient (ICC), and the Technological Transition Potential (TTP). We formalize the joint scaling dynamics of autonomy and infrastructural embedding, derive threshold conditions for paradigm transition, and introduce a phase-space representation of systemic transformation. A temporal transition model further illustrates how nonlinear coevolution between autonomy and infrastructure integration produces super linear growth in transition potential. Unlike prior GPT cycles, the ANAI regime exhibits a recursive energy computation feedback loop in which AI systems both increase computational demand and optimize the infrastructures that sustain them. This feedback mechanism accelerates infrastructural embedding and differentiates AI driven transformation from previous technological revolutions. By shifting analytical focus from model performance to infrastructural autonomy and coupling intensity, this study offers a conceptual and mathematical foundation for assessing whether artificial intelligence constitutes the next general purpose technology.