NLP Occupational Emergence Analysis: How Occupations Form and Evolve in Real Time -- A Zero-Assumption Method Demonstrated on AI in the US Technology Workforce, 2022-2026

TL;DR

This paper introduces a zero-assumption method to detect occupational emergence from resume data by analyzing vocabulary and population cohesion, revealing rapid vocabulary formation without practitioner cohesion in AI, indicating diffusion rather than emergence.

Contribution

The paper presents a novel, taxonomy-free approach to identify emerging occupations through self-reinforcing vocabulary and population structures, demonstrated on large-scale resume data.

Findings

AI vocabulary formed rapidly in early 2024

Practitioner population did not cohere around AI vocabulary

AI is a diffusing technology, not an emerging occupation

Abstract

Occupations form and evolve faster than classification systems can track. We propose that a genuine occupation is a self-reinforcing structure (a bipartite co-attractor) in which a shared professional vocabulary makes practitioners cohesive as a group, and the cohesive group sustains the vocabulary. This co-attractor concept enables a zero-assumption method for detecting occupational emergence from resume data, requiring no predefined taxonomy or job titles: we test vocabulary cohesion and population cohesion independently, with ablation to test whether the vocabulary is the mechanism binding the population. Applied to 8.2 million US resumes (2022-2026), the method correctly identifies established occupations and reveals a striking asymmetry for AI: a cohesive professional vocabulary formed rapidly in early 2024, but the practitioner population never cohered. The pre-existing AI community dissolved as the tools went mainstream, and the new vocabulary was absorbed into existing careers rather than binding a new occupation. AI appears to be a diffusing technology, not an emerging occupation. We discuss whether introducing an "AI Engineer" occupational category could catalyze population cohesion around the already-formed vocabulary, completing the co-attractor.