Job resilience measures how robustly a role can maintain its economic value and employment level in the face of advancing AI capabilities. It is a structural property of the role — not of the individual worker — shaped by the nature of the tasks, the skills required, the regulatory environment, and the social context in which the work is performed.
High-resilience roles share several characteristics: they require the integration of diverse, context-specific information that AI systems struggle to access (proprietary knowledge, local context, relationship history); they involve real-time interpersonal judgment in unpredictable social situations; they carry legal or ethical accountability that humans must own; or they demand physical presence in unstructured environments.
Job resilience is distinct from the resilience score used in individual risk assessments (which measures a worker's personal adaptability). At the role level, resilience reflects structural features: the complexity and uniqueness of tasks, the importance of physical and social context, and the degree to which the role's output is verifiable by someone other than an expert.
Resilience is also dynamic — it changes as AI capabilities evolve. A role that was highly resilient in 2020 may be moderately resilient in 2026 as AI systems have improved. Conversely, resilience can increase for workers who shift toward the highest-judgment, most socially complex aspects of their role — effectively increasing the role's resilience by changing what the role contains.