The atomic structure of work: a micro-action instrument reveals two-pole AI occupational exposure and its decade-scale polar inversion

Abstract

Research on artificial intelligence and work assigns each occupation a single exposure score. We build an instrument to see what those scores average over: a decomposition of 1,961 O*NET work activities into 15,817 atomic micro-actions by a consensus multi-agent LLM pipeline, clustered from text alone into seven semantic classes. Projecting exposure indicators onto these classes reveals two extreme poles, tool-mediated physical execution and planning-and-design, separated by a gap far larger than random partitions of the same data produce (permutation $P<10^{-4}$; Cliff's $\delta =0.80$ under our tech-risk index and $0.90$ under GPT-4 task ratings). The poles flank a broad central band that carries most work and is only weakly more compressed than chance. The poles are stable across clustering resolution, sentence encoder (under a common partition), and indicator, yet which pole is most exposed has inverted since 2013: the two extremes swap identity between the Frey-Osborne computerisation era and the LLM era, and at the occupation level an occupation's 2013 automatability declines as its linguistic content rises ($\rho =-0.40$, $n=618$). We release the instrument and its outputs. The durable object for forecasting is the structure of work itself, not any era's exposure ranking.

arXiv commentv2: major revision, retitled (v1: "Stable Geometry, Reversing Poles"). Adds an independent blind three-annotator validation study (construct kappa = 0.90; encoder-vs-human kappa = 0.63), a vintage-matched O*NET 18.1 reprojection, a demographic overlay, and recalibrated claims throughout. 27 pages; Supplementary Information (29 pages) in ancillary files. Dataset: doi:10.5281/zenodo.21395792