Abstract

Artificial General Intelligence (AGI) is commonly discussed as a binary achievement or a sudden technological threshold—a framing that fuels both exaggerated expectations and disproportionate regulatory responses. This paper argues that such a conception is conceptually flawed and proposes a fundamental reframing. Drawing on insights from philosophy of mind, embodied cognition, developmental psychology, and systems engineering, it advances a capability-based maturity model for AGI that treats intelligence as a graded, developmental, and integrative phenomenon rather than a monolithic property that systems either possess or lack. The proposed AGI Capability Maturity Model™ (AGI-CMM™) distinguishes seven levels of cognitive and functional capacity: generative-semantic capability, reasoning and constraint handling, learning and memory, planning and simulation, perception-action coupling, integrated embodied cognition, and reflexive-normative intelligence with imagination. Crucially, the model emphasizes that progression through these levels depends not merely on improving individual capabilities but on achieving coherent integration across them. Intelligence, on this view, emerges from the coordination and mutual constraint of multiple capacities, not from the amplification of any single dimension. The model makes important distinctions between related cognitive capacities. Simulation—the instrumental modeling of world dynamics to predict action outcomes—belongs to planning (Level 4), grounding deliberation in predictive cognition. Imagination—the generative, counterfactual, and normatively creative capacity to envision what does not yet exist—belongs to reflexive-normative intelligence (Level 7), enabling the self-transcendence that genuine autonomy requires. This distinction clarifies that planning without simulation is blind, while normativity without imagination is static. By mapping contemporary AI systems onto this maturity spectrum, the paper demonstrates that current technologies—including large language models, autonomous agents, and robotics systems—remain fragmented across capability space and fall substantially short of general intelligence. The framework dissolves the misleading binary of 'AGI achieved' versus 'AGI distant' by revealing that we possess islands of impressive capability without the integrative architecture that would constitute genuine generality. This diagnosis has significant implications for AI governance, suggesting that regulation should be tied to capability integration and autonomy rather than speculative thresholds or marketing claims. The paper engages critically with existing AGI taxonomies, addresses potential objections including arguments from emergence and recursive self-improvement, and provides operationalizable criteria for assessing system maturity. It concludes that AGI, if it emerges, will do so not as an abrupt event but as the gradual integration of capabilities into coherent, adaptive, and ultimately self-regulating systems capable of imagining and pursuing genuine normative goods. The AGI Capability Maturity Model offers researchers, developers, policymakers, and governance bodies a shared conceptual vocabulary for reasoning responsibly about AI progress, risks, and realistic expectations.