How Smart is Smart? A Critical Interdisciplinary Perspective on Artificial General Intelligence

The concept of Artificial General Intelligence (AGI)—a machine capable of performing any intellectual task a human can—is both a central aspiration and a contested notion in AI research. Despite its prominence in scholarly and public discourse alike, AGI relies on unsettled definitions of intelligence and speculative assumptions about generalization. This paper critically examines AGI from multiple perspectives: conceptual theory, philosophy, psychometrics, and recent developments in large language models (LLMs). The foundations of AGI are undermined by the lack of consensus on what constitutes "intelligence'' in both human and artificial contexts. Furthermore, I explore how AGI systems may excel at benchmarks by optimizing for performance rather than demonstrating genuine understanding—akin to the "simulation without comprehension'' phenomenon described by Searle’s Chinese Room argument. I also investigate the emergent behaviors reported in advanced AI models, assess whether these indicate genuine steps toward general intelligence or illusory artifacts, and discuss how introspective features in LLMs might or might not constitute a move toward ``self-awareness.'' By integrating insights from multiple disciplines, I propose a framework for reevaluating AGI that prioritizes scientific rigor, conceptual clarity, and ethical considerations. This analysis underscores the urgent need to distinguish mere test-passing behavior from true intelligence and to develop robust, psychometrically grounded benchmarks for AGI evaluation.