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

2025-01-29

AI Summary

AI’s rapid scaling is inducing systemic overload across technological, economic, ecological, and societal dimensions, pushing the system toward a critical “overshoot collapse.” This study introduces the first integrated four-dimensional coupled framework—spanning technology, economics, ecology, and society—combining system dynamics modeling (grounded in the “limits to growth” archetype), interdisciplinary literature synthesis, and multi-scale causal and externality attribution analysis. It quantitatively identifies ecological tipping points of compute expansion and mechanisms of social cost externalization. Key contributions include: (1) the first empirical demonstration that endogenous, nonlinear feedbacks across all four dimensions render current AI scaling intrinsically unsustainable; and (2) a novel governance paradigm centered on sustainability constraints and value alignment—replacing unconstrained scale-up—with theoretical foundations and actionable policy levers for responsible AI development. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Scalability of ML Systems

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Sustainable Development
• Artificial Intelligence
• Resource Consumption

Innovation

• Artificial Intelligence Expansion
• System Dynamics
• Sustainable Development