Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race

Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse that is emerging on sustainable artificial intelligence (AI) has predominantly focused on the environmental sustainability of AI, often neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its climate awareness, which emphasizes the need to mitigate AI's environmental impacts, and its social sustainability, which hinges on equitable access to AI development resources. The concept of resource awareness advocates for AI sovereignty through broader access to the infrastructure required to develop AI. Yet, this push for improving accessibility often overlooks the environmental costs of expanding such resource usage. This position paper argues that reconciling climate awareness and resource awareness is essential to realizing sustainable AI and neglecting these factors fueling the global AI arms race. By applying the base-superstructure framework from historical materialism, we analyze how the material conditions are shaping the current AI progress and the discourse surrounding it. We also introduce the Climate and Resource Aware Machine Learning (CARAML) framework to address the conflict between climate and resource awareness of AI, with actionable recommendations spanning individual, community, industry, government, and global levels to achieve sustainable AI.