mlsanityData to 13 Oct 2026

Limits to AI Growth: The Ecological and Social Consequences of Scaling

arXiv 2501.17980 ↗First posted 29 Jan 2025cs.CY cs.AIcited by 2

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Abstract (arXiv)

The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the factors that AI scaling depends on reach its limits, the push for its accelerated advancement and entrenchment continues. In this paper, we provide a holistic review of AI scaling using four lenses (technical, economic, ecological, and social) and review the relationships between these lenses to explore the dynamics of AI growth. We do so by drawing on system dynamics concepts including archetypes such as "limits to growth" to model the dynamic complexity of AI scaling and synthesize several perspectives. Our work maps out the entangled relationships between the technical, economic, ecological and social perspectives and the apparent limits to growth. The analysis explains how industry's responses to external limits enables continued (but temporary) scaling and how this benefits Big Tech while externalizing social and environmental damages. To avoid an "overshoot and collapse" trajectory, we advocate for realigning priorities and norms around scaling to prioritize sustainable and mindful advancements.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 29 of its 174 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

29 of 174 references.

References · 29

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17 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agentscited by 14
  2. 2025Irresponsible AI: big tech's influence on AI research and associated impactscited by 1