AIによる職業代替性の二極構造とその10年規模での反転

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.