Artificial Intelligence and electricity: A system dynamics approach

Overview

1/3

AI's systemic nature and potential as a General Purpose Technology pose complex challenges for energy consumption forecasting and management. The field of AI electricity footprint estimation lacks comprehensive scenarios and forecasts, divergent modeling methodologies, and varying perceptions of AI's future.
2/3

This paper aims to explore the complex relationship between artificial intelligence (AI) development and electricity systems and goes beyond forecasting by using system dynamics modeling.
3/3

The analysis of AI energy consumption scenarios from 2025 to 2035 reveals diverse trajectories with significant implications for future energy use and sustainability. These divergent paths underscore the critical role of AI development strategies, policy frameworks, and governance in shaping the future energy landscape and environmental impact of AI technologies.

Key findings

Current decisions on AI infrastructure will significantly shape electricity consumption trajectories after 2030.
Reduced AI electricity consumption does not necessarily indicate sustainable and resilient development.
Sustainable AI is evolving to prioritize efficiency, frugality, and proven impact as electricity demand grows.
Unrestricted abundance in AI development could alter existing systems, constrain decarbonization efforts, and generate waste.
Mismatched energy demand and infrastructure can lead to localized energy shortages with potential global ripple effects.

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