The Potential Impact of Disruptive AI Innovations on U.S. Occupations

TL;DR

This paper analyzes the potential disruptive impact of AI innovations on U.S. occupations by calculating a disruption index for patents and linking them to job tasks, revealing differing effects based on innovation type and region.

Contribution

It introduces a systematic method to measure AI innovation disruption potential and links it to specific job tasks and regional labor market characteristics.

Findings

Disruptive AI mainly affects mental and unpredictable tasks.

Consolidating AI targets physical, routine, solo tasks.

Disruptive AI impacts areas with skilled labor shortages.

Abstract

The rapid rise of AI is poised to disrupt the labor market. However, AI is not a monolith; its impact depends on both the nature of the innovation and the jobs it affects. While computational approaches are emerging, there is no consensus on how to systematically measure an innovation's disruptive potential. Here, we calculate the disruption index of 3,237 U.S. AI patents (2015-2022) and link them to job tasks to distinguish between "consolidating" AI innovations that reinforce existing structures and "disruptive" AI innovations that alter them. Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks, common in manufacturing and construction in the Midwest and central states. By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors. Surprisingly, we also find that disruptive AI disproportionately affects areas already facing skilled labor shortages, suggesting disruptive AI technologies may accelerate change where workers are scarce rather than replacing a surplus. Ultimately, consolidating AI appears to extend current automation trends, while disruptive AI is set to transform complex mental work, with a notable exception for collaborative tasks.