Empirical research on the labor-market impact of artificial intelligence has converged, since Frey and Osborne (2017), on a continuous-gradient representation in which each occupation is assigned a real-valued exposure score on [0,1] obtained by linear aggregation across capability dimensions. This continuity is rarely articulated as an assumption and has not been tested at the micro-action level where substitution actually occurs. We decompose 1,961 O*NET Detailed Work Activities into 15,817 micro-actions using a multi-agent LLM pipeline with 31-expert HITL calibration, then project the DWA-level Occupational Automation Index from our prior work onto a 7-macro semantic typology. The result is a bipolar structure. Tool-Mediated Physical (M2, mean OAI = 0.054) and Planning & Design (M7, mean OAI = 0.499) form two extremes separated by Cohen's d = 2.41 (H = 172.88, p = 6.21e-34). The geometry is robust under three independent stress tests: resolution (K=7 to K=15, polar gap widens from 0.45 to 0.57), encoder swap to BGE (LLM-class OAI lead replicates at 3.37x), and Eloundou's GPT-4 task ratings (DWA-level rho = 0.635). The six middle macros form a low-contrast band between the poles (TOST at d=0.2 admits only 1/15 pairs as equivalent), not a flat plain. The geometry's stability does not, however, extend to its content. Across a decade, the polarity has inverted. Frey-Osborne (2013) placed Tool-Mediated Physical near the highest computerisation risk and Planning & Design near the lowest; our LLM-era OAI reverses that order, with macro-level FO-Eloundou Spearman rho = -0.750, p = 0.020, against the original Oxford Martin appendix. Which pole is high is therefore contingent on the era's dominant capability frontier, while the stable geometry itself is the structurally robust object.

翻译：自Frey与Osborne（2017）以来，关于人工智能对劳动力市场影响的实证研究已趋于采用连续梯度表征，即每个职业被赋予一个通过对各能力维度进行线性聚合而得到的[0,1]实值暴露分数。这种连续性很少被明确表述为假设，也未在替代实际发生的微观动作层面进行检验。我们利用一个包含31位专家人机协同校准的多智能体LLM流水线，将1,961项O*NET详细工作活动分解为15,817个微观动作，然后将我们先前工作中的DWA级职业自动化指数投射到7个宏观语义类型上。结果呈现出一个双极结构。工具介导体力型（M2，平均OAI = 0.054）与规划与设计型（M7，平均OAI = 0.499）构成两极，Cohen's d = 2.41（H = 172.88，p = 6.21e-34）。该几何结构在三种独立压力测试下保持稳健：分辨率（K=7至K=15，极差从0.45扩大到0.57）、编码器切换至BGE（LLM级OAI领先优势复制为3.37倍），以及Eloundou的GPT-4任务评分（DWA级rho = 0.635）。六个中间宏观类型在两极之间形成一个低对比度带（TOST在d=0.2下仅承认1/15对为等价），而非平坦平原。然而，该几何结构的稳定性并未延伸至其内容。历经十年，极性已发生翻转。Frey-Osborne（2013）将工具介导体力型置于最高计算机化风险附近，而将规划与设计型置于最低风险附近；我们的LLM时代OAI逆转了这一顺序，宏观层面FO-Eloundou Spearman rho = -0.750，p = 0.020，与原始牛津马丁附录相对。因此，哪一极处于高位取决于时代的主导能力前沿，而稳定的几何结构本身才是结构上稳健的对象。