Many people associate artificial intelligence with job losses, increasing workplace surveillance, and ballooning profits for tech billionaires. A recent article from the Brookings Institution (March 2026), authored by researchers from Brown University, Case Western Reserve University, and Brookings itself, argues that this trajectory is not inevitable — but only if policymakers make a different choice.
The real crisis: the „enshittification“ of work
The authors diagnose a problem that predates AI: work has been getting steadily worse. More monitoring, algorithmic scheduling, shrinking autonomy, and stagnant wages have become the norm. They call this process „enshittification“ — and it started long before the current AI boom. The erosion of collective bargaining power, weakened regulatory agencies, and declining union membership are the root causes. AI accelerates this trend but did not create it. According to surveys, more than half of Americans fear that AI will take their jobs and replace their face-to-face relationships.
Three policy proposals for a people-centered AI future
1. Protect and expand human care professions. Teachers, nurses, and social workers should not be replaced by AI but strengthened through better policy frameworks. Minimum staffing requirements — already standard in aviation and nuclear power — could bring more people into these professions and improve quality. AI can support care work, for example by handling documentation so nurses spend more time with patients. But the core work stays human.
2. Build institutions for lifelong learning and career transitions. Workers displaced by AI need more than a short retraining course. The authors argue for institutionalized pathways for lifelong learning, drawing on the example of European countries with strong trade unions. As a concrete case, they point to software engineers — whose jobs are under growing AI pressure — who could, with targeted support, transition into teaching, where chronic shortages already exist. For this to work, salaries, status, and professional autonomy in care and education professions must also rise.
3. Create tripartite institutions for the co-design of AI. When AI is introduced top-down, without involving those affected, it tends to worsen working conditions. The authors give a telling example: utility workers forced to follow inefficient and unsafe routes by poorly designed client management software — a system built without understanding their specific context. When workers are brought into the design process, better systems emerge for everyone. Tripartite institutions — bringing together government, employers, and trade unions — can structure and anchor this participatory process.
What this means for campaigning
The Brookings article is not just relevant to labor market policy — it is a masterpiece in applied campaigning. It identifies a growing societal conflict (inequality driven by AI), defines clear target groups (workers in vulnerable professions, trade unions, policymakers), and proposes concrete, actionable measures. That is precisely what effective political campaigning requires: not just describing a problem, but articulating a vision that motivates action and „building golden bridges“ (Strategic Campaigning Principle No. 14)
The core message — people first, AI as a tool rather than a replacement — is campaign-ready: simple, emotionally grounded, and operationalized in clear policy proposals. That connection between vision and actionability is exactly the backbone of the Business Campaigning Model.
Happy Easter
Source: Sorelle Friedler, Serena Booth, Andrew Schrank, Susan Helper: „A people-first vision for the future of work in the age of AI“, Brookings Institution, March 25, 2026.