用动态替代弹性分析AI如何取代人力,揭示技术进步与劳动力市场的关系。
Will Neural Scaling Laws Activate Jevons' Paradox in AI Labor Markets? A Time-Varying Elasticity of Substitution (VES) Analysis
- 构建时间可变替代弹性模型,追踪AI与人力关系演变。
- 发现当替代弹性大于1时,AI将全面替代人类劳动,关键在复合参数ϕ。
- 适合关注AI对就业影响、技术经济规律的研究者和政策制定者。
我们建立了一个正式的经济框架,分析人工智能中的神经网络缩放定律是否会引发劳动力市场的杰文斯悖论,导致AI采用增加并取代人类劳动。通过时间可变替代弹性(VES)方法,我们确立了AI从补充人力转向替代人力的解析条件。模型包含四个相互关联的机制:(1) 计算能力指数增长(C(t) = C(0) · e^{g·t});(2) AI能力随计算量对数增长(σ(t) = δ·ln(C(t)/C(0)));(3) AI价格下降(p_A(t) = p_A(0) · e^{-d·t});(4) 复合效应参数ϕ = δ·g 决定市场转型动态。我们识别出五个阶段的AI市场渗透过程,表明完全市场转型需替代弹性超过1(σ > 1),其时间主要由复合参数ϕ决定,而非仅由价格竞争驱动。研究为评估行业关于AI替代效应的主张提供了分析基础,尤其强调质量与价格在技术转型中的作用。
原文摘要 · Abstract (English)
We develop a formal economic framework to analyze whether neural scaling laws in artificial intelligence will activate Jevons' Paradox in labor markets, potentially leading to increased AI adoption and human labor substitution. By using a time-varying elasticity of substitution (VES) approach, we establish analytical conditions under which AI systems transition from complementing to substituting for human labor. Our model formalizes four interconnected mechanisms: (1) exponential growth in computational capacity ($C(t) = C(0) \cdot e^{g \cdot t}$); (2) logarithmic scaling of AI capabilities with computation ($σ(t) = δ\cdot \ln(C(t)/C(0))$); (3) declining AI prices ($p_A(t) = p_A(0) \cdot e^{-d \cdot t}$); and (4) a resulting compound effect parameter ($ϕ= δ\cdot g$) that governs market transformation dynamics. We identify five distinct phases of AI market penetration, demonstrating that complete market transformation requires the elasticity of substitution to exceed unity ($σ> 1$), with the timing determined primarily by the compound parameter $ϕ$ rather than price competition alone. These findings provide an analytical framing for evaluating industry claims about AI substitution effects, especially on the role of quality versus price in the technological transition.
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