发表机构
University of Colorado Denver（科罗拉多大学丹佛分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨AI指数增长面临的技术与社会挑战，分析泡沫内外的不稳定性，涵盖计算竞赛、投机投资及治理问题。
AI 中文摘要
近年来，人工智能（AI）的指数级增长已进入一个由大型语言模型（LLMs）、大规模计算基础设施和自主推理系统的发展所驱动的变革时代。然而，AI的快速加速日益暴露出与数据峰值限制、计算需求上升、合成数据递归、估值膨胀和社会不稳定相关的技术、社会、经济、伦理和基础设施挑战。支撑现代AI系统的传统扩展范式在维持持续指数增长方面正逐渐遇到阻力。本文审视“AI指数增长的终结”，探讨其如何在泡沫内外波动，其中不稳定性在AI生态系统内部通过计算和数据中心竞赛、投机性投资以及通往超级智能的激烈竞争而出现，在生态系统外部则通过全球范围内围绕未来智能系统和基础设施的劳动力 disruption、治理担忧、公众不确定性以及地缘政治加速而显现。
英文摘要
The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large language models (LLMs), large-scale compute infrastructures, and autonomous reasoning systems. However, the rapid acceleration of AI has increasingly shown technological, societal, economic, ethical and infrastructural challenges associated with peak data limitations, rising computational demands, synthetic data recursion, valuation inflation, and societal instability. The traditional scaling paradigms that have powered the modern AI systems are gradually encountering friction in sustaining continuous exponential growth. This paper views ``the end of AI exponentiation,'' thus exploring how it flutters inside and outside the bubble, where instability emerges within the AI ecosystem through compute and data-center races, speculative investments, and the rat-race toward superintelligence, and outside the ecosystem through labor disruption, governance concerns, public uncertainty, and geopolitical acceleration surrounding future intelligent systems and infrastructures globally.
