THE ECONOMICS OF ENERGY EFFICIENCY: HUMAN COGNITION VS. AI LARGE LANGUAGE MODELS
THE ECONOMICS OF ENERGY EFFICIENCY: HUMAN COGNITION VS. AI LARGE LANGUAGE MODELS

Summary/Abstract: The human brain and Large Language Models (LLMs) exemplify two profoundly different information-processing systems, with a significant imbalance in energy consumption. This article provides a comparative analysis of the energy requirements of biological cognition and machine-based models, linking these differences to their economic consequences. The operational costs of LLMs, driven by energy-intensive computations, present financial and environmental sustainability challenges, influencing the scalability of AI adoption across industries. In contrast, the human brain’s remarkable energy efficiency highlights the economic advantages of biological intelligence, which operates with minimal resource investment. We explore the economic trade-offs of AI’s growing energy demands, including cost implications for firms, policy responses such as carbon regulations, and the potential labor market disruptions arising from energy-efficient automation. By integrating insights from neuroscience, engineering, and economics, we argue that sustainable AI development hinges on a fusion of brain-inspired paradigms, eco-efficient hardware solutions, and strategic policy frameworks that balance innovation with economic feasibility.

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