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.

Amanda Pratt and Melissa Valentine argue in Harvard Business Review that driving AI adoption requires more than prompt engineering or context engineering skills. While crafting effective inputs for large language models is useful, the real value emerges when employees learn to apply generative AI to improve their actual workflows. This demands capabilities like defining valuable problems, evaluating possible solutions, rapid experimentation, and sustainable integration of new practices into daily work—disciplines that are fundamentally core to product management.

The article reframes the AI skills gap not as a technical challenge but as an organizational one. Rather than focusing narrowly on how to talk to AI tools, teams should build the product management mindset needed to identify where AI creates genuine value, test solutions quickly, and embed changes into ongoing processes. This perspective shifts the conversation from tool proficiency to workflow transformation, suggesting that companies investing in product management competencies across their teams will see far greater returns from their AI investments.