Editor's Synopsis
This Brookings commentary by scholars from Brown University, Case Western Reserve University, and the Brookings Institution argues that the debate around AI and jobs too often focuses narrowly on whether AI will destroy or create employment, while ignoring the steady degradation of job quality already underway. The authors contend that work has been progressively "enshittified" through increased surveillance, algorithmic scheduling, declining autonomy, and stagnant wages — trends that predate modern AI but risk being accelerated by it. Even Silicon Valley, once a bastion of worker autonomy, is pushing employees harder with fewer protections.
The piece outlines three core people-first policy proposals: protecting and expanding human roles in care professions like teaching and nursing through minimum staffing requirements; developing robust training institutions to support mid-career transitions (such as displaced software engineers moving into education); and creating tripartite institutions that bring government, business, and labor together to co-design AI systems rather than imposing them from above. The authors point to concrete examples, including a Carnegie Mellon–UNITE HERE collaboration on hotel worker software, as evidence that participatory AI design can produce mutual gains.
Ultimately, the authors argue that the AI revolution presents both a threat and an opportunity. Rather than accepting a future where AI enriches tech billionaires while hollowing out the workforce, policymakers should seize this moment to reinvest AI-generated wealth into human-centered professions, revitalize institutions like the NLRB, and build new lifelong learning systems modeled on successful European approaches.
