AI增长正接近瓶颈，内外风险交织凸显系统性脆弱。
The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble
- 分析大模型与算力扩张带来的技术与社会失衡
- 指出数据瓶颈、算力竞争与估值泡沫构成增长阻力
- 适合关注AI可持续性与治理的政策与产业决策者
近年来人工智能的指数级发展由大语言模型、大规模计算基础设施和自主推理系统推动，但其快速推进已暴露出技术、社会、经济、伦理与基础设施层面的多重挑战，包括峰值数据限制、计算需求激增、合成数据循环、估值膨胀及社会不稳。传统规模化范式正逐步面临持续指数增长的阻力。本文探讨‘AI指数增长的终结’，分析其在生态内部因算力与数据中心竞赛、投机投资、超智能竞赛而产生的不稳定性，以及外部因劳动力冲击、治理争议、公众不确定性及全球智能系统与基础设施的地缘政治加速所引发的动荡。
原文摘要 · Abstract (English)
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.
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