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.

翻译：职业的形成与演化速度远超过分类系统的追踪能力。我们提出，真正的职业是一种自我强化的结构（二分共吸引子），其中共享的专业术语使从业者凝聚为一个群体，而凝聚的群体又维持着该术语体系。这一共吸引子概念使我们能够基于简历数据检测职业涌现，且无需预设分类体系或职位名称：我们独立检验术语凝聚性与群体凝聚性，并通过消融实验验证术语是否为维系群体的机制。将该方法应用于820万份美国简历（2022-2026年），模型正确识别了已有职业，并揭示了AI领域的显著不对称性：2024年初快速形成了凝聚性的专业术语体系，但从业者群体始终未能凝聚。随着AI工具成为主流，原有的AI社群逐渐瓦解，新术语被吸纳进现有职业轨道而非绑定为新职业。AI似乎是一种扩散性技术，而非涌现性职业。我们探讨了引入"AI工程师"职业类别是否可能催化围绕已形成术语体系的人群凝聚，从而完善共吸引子机制。