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

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

Problem Statement
The paper identifies a critical gap in the current discourse on sustainable artificial intelligence (AI). While sustainability is traditionally defined by three pillars—environmental, economic, and social—the nascent field of "sustainable AI" predominantly focuses on environmental sustainability (specifically climate impact and carbon emissions). This narrow focus neglects economic and social dimensions, particularly resource awareness: the infrastructural and economic barriers that restrict equitable access to AI development.

The authors argue that this neglect creates a fundamental tension. Efforts to mitigate climate impact (e.g., "Green AI" or efficient models) often inadvertently fuel a global AI arms race by lowering barriers to entry for resource-intensive development without addressing the underlying concentration of power. Conversely, the push for "AI sovereignty" and democratization (increasing resource access) often ignores the environmental costs of expanding computational infrastructure. This dynamic is exacerbated by the "rebound effect" (Jevons' paradox), where efficiency gains lead to increased aggregate consumption rather than reduction. The result is a short-sighted global arms race driven by a handful of corporations and nations, externalizing environmental and financial costs while entrenching social divides and excluding low- and middle-income countries (LMICs).

Methodology and Theoretical Framework
Rather than proposing new machine learning algorithms or conducting empirical experiments, this position paper employs a critical theoretical analysis grounded in historical materialism, specifically utilizing Karl Marx's base-superstructure framework.

• The Base: Defined as the material conditions of AI production, including raw materials (rare-earth minerals), energy, water, specialized hardware (GPUs), labor, capital investment, and data. The paper posits that these material conditions are currently monopolized by a few entities in the Global North.
• The Superstructure: Defined as the norms, values, regulatory frameworks, ethics, and cultural narratives that govern AI. The authors argue that the superstructure is shaped by the base to maintain the status quo of resource concentration, legitimizing the current power dynamics.

The authors analyze the interplay between these layers to demonstrate how the current material concentration drives the "AI arms race" and how the superstructure (e.g., narratives of AI sovereignty or efficiency) fails to address the structural inequities. They support this analysis with data visualizations regarding:

• The geographic concentration of supercomputing capacity (TOP500 data).
• Disparities in AI investment (US vs. China vs. others).
• The dominance of corporate entities in LLM leaderboards.
• The under-representation of LMICs in top AI conference publications (ICML, ICLR, NeurIPS) from 2006–2025.
• The disparity in renewable energy access globally.

Key Contributions
The paper's primary contributions are conceptual and prescriptive:

1. Reframing Sustainable AI: The authors introduce a two-axis framework (Climate Awareness vs. Resource Awareness) to contextualize AI paradigms:

   • SOTA AI: Low climate awareness, low resource awareness (high resource consumption, centralized).
   • Green AI: High climate awareness, low resource awareness (focus on efficiency, but often ignores access/equity).
   • Inclusive AI: Low climate awareness, high resource awareness (focus on access/equity, but often ignores environmental costs).
   • Sustainable AI (The Goal): High climate awareness AND high resource awareness. The paper argues that true sustainability requires bridging the divide between these two axes.

2. The CARAML Framework: The authors propose the Climate and Resource Aware Machine Learning (CARAML) framework. This is a multi-level call to action designed to reconcile climate and resource awareness. It outlines specific interventions across five levels of agency and scope:

   • Individual: Redefine success metrics to include resource costs (e.g., performance per resource unit) and adopt Pareto optimization for sustainability.
   • Community (ML): Pre-register large-scale experiments to prevent waste; enforce transparency and self-reporting of carbon footprints; adopt hybrid conferences to reduce travel emissions.
   • Industry: Mandate sustainability disclosures (including embodied emissions); implement regulatory carbon caps for AI.
   • Government: Require AI Impact Assessments (similar to Environmental Impact Assessments) covering social and environmental impacts; promote "Reverse Innovation" from LMICs to HICs.
   • Global Action: Establish coordinated global standards for ethics and safety; address the digital divide by ensuring a "Right to Sustainable Compute"; form international consortia for global challenges.

Results and Evidence
As a position paper, the results are analytical rather than experimental. The paper synthesizes existing data to demonstrate:

• Geographic Inequality: The US and China dominate global AI investment and supercomputing capacity, while Africa and other regions are largely unrepresented.
• Corporate Dominance: The majority of entries in LLM benchmark leaderboards are from corporations that have invested in massive compute resources.
• The Rebound Effect: The paper cites the rise of efficient models (e.g., DeepSeek) as a case study where efficiency led to a surge in usage and a shift toward "reasoning models" with higher token counts, resulting in no net reduction in energy use.
• The Double Penalty: LMICs face a "double penalty": they lack access to the resources to develop AI, and they are further excluded by the push for low-carbon datacenters which require renewable energy infrastructure they often lack.

Significance and Claims
The paper claims that neglecting the dual aspects of sustainability (climate and resource) is not merely an oversight but a driver of a dangerous global AI arms race. By applying the base-superstructure framework, the authors argue that the current trajectory is structurally reinforced by material conditions that prioritize capital accumulation over social and environmental well-being.

The significance of the work lies in its call to move beyond the binary of "efficiency vs. access." The authors assert that achieving Sustainable AI requires a coordinated, multi-stakeholder effort to democratize access to the material base (compute, data, energy) while simultaneously imposing strict climate constraints. They argue that without this joint consideration, efforts to "green" AI or "democratize" AI will fail, either by exacerbating environmental degradation or by reinforcing global inequities. The paper concludes that the AI community must resist the forces of the arms race to address the more existential threat of climate change, ensuring that AI development serves the broader goals of the Sustainable Development Goals (SDGs).