近期許多經濟預測指出人工智慧（Artificial Intelligence，AI）的崛起將對總體經濟產出與勞動生產力帶來巨大的正面效應。然而，此一技能偏向技術變遷（Skill-Biased Technological Change，SBTC）在帶來正面效應的同時也為勞動市場帶來結構性的轉變，其發展可能危及整體社會穩定與經濟成長。本研究以異質性個體模型分析AI對勞動分配的影響，藉由納入個別勞工相異的初始能力與內生的職業選擇機制，本模型將勞動供給的自我選擇對應至低階、中階與高階技能的市場均衡中。根據此模型的靜態分析，此研究發現隨著AI整合程度加深，固定訓練成本攀升與中階生產力貶值兩者的交互作用下，將對中階勞動力形成嚴重的擠壓效應。本研究進一步採用2010至2024年，一共741個通勤區的美國社區調查（American Community Survey ，ACS）樣本資料，並以工作任務去定義中階勞工。實證結果顯示，AI採用程度每增加一個標準差，中階勞工的工時佔比將下降1.7%至3.1%，且該現象在2022年生成式AI問世之後更呈現惡化趨勢。此外，本研究嘗試建構規範性分析架構以因應擠壓效應所引發的勞動市場兩極化現象，推導出一組最適政策組合，包含累進稅制、無條件基本收入（Universal Basic Income，UBI）與進階教育補貼，並評估其減緩中階勞動擠壓的效果，研究結果展示了一條邁向永續經濟和社會韌性的轉型路徑，說明透過適當的政策介入能促成人力資本的累積而非大規模的勞動替代，以因應AI所引發的勞動市場衝擊。

Contemporary economic forecasts suggest that the integration of Artificial Intelligence (AI) will generate substantial positive impacts on the aggregate output and labor productivity. However, this skill-biased technological change introduces profound structural transformations in the labor market, potentially jeopardizing social stability and economic growth. This paper examines the labor distributional effects of AI through the lens of a heterogeneous agent model. By incorporating the idiosyncratic initial ability and endogenous occupational sorting, the model maps labor supply selection into low-, middle-, and high-skill equilibria. We demonstrate analytically that as the AI integration deepens, the interaction between escalating fixed training costs and a depreciating middle-skill productivity induces a severe squeezing effect on the middle-skill labor force. This study further draws on the American Community Survey covering 741 commuting zones from 2010 to 2024, and defines middle-skill labor under task content. The estimates indicate that a one-standard-deviation increase in AI adoption lowers the middle-skill hours share by 1.7% to 3.1%, and that this trend deteriorates further after the arrival of generative AI in 2022. Furthermore, this study transcends a positive analysis by exploring a normative framework to address this polarization caused by squeeze effect. We derive an optimal policy mix—comprising progressive taxation regimes, a Universal Basic Income provision, and advanced education subsidies—and evaluate its efficacy in mitigating displacement. The findings elucidate a transition path toward sustainable economic resilience, illustrating how calibrated policy interventions can ensure that the labor market disruptions induced by AI ultimately foster human capital development rather than widespread displacement.

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