The Thermodynamic Barrier to Artificial General Intelligence: A Comprehensive Analysis of Energy Efficiency, Neuromorphic Computing, and the Path to Sustainable AGI

The rapid advancement of artificial intelligence has brought unprecedented capabilities in language processing, reasoning, and multimodal understanding. However, the path to Artificial General Intelligence (AGI) faces a fundamental constraint that transcends algorithmic innovation: thermodynamic efficiency. This paper presents a comprehensive analysis of the energy consumption disparity between current AI systems and biological intelligence, examining why the human brain's 20-watt operation represents not merely an engineering benchmark but a physical necessity for scalable, ubiquitous AGI. We analyze the limitations of von Neumann architecture, explore emerging neuromorphic computing paradigms including spiking neural networks and memristive systems, and provide a rigorous timeline for convergence toward biologically-efficient artificial intelligence. Our findings suggest that while algorithmic AGI may emerge by 2027-2029, truly sustainable and democratized AGI requires a fundamental shift in computing substrates, unlikely before 2032-2035.