Beyond the Hype: What AI Is Actually Doing to Workers

While public debate about AI and work has been dominated by speculation about everything from mass unemployment to endless efficiency, workers themselves are facing a more complicated reality. Data & Society has spent time moving past hype cycles to produce research focused on how workers are negotiating, resisting, and shaping their relationships to these new workplace technologies.

One of our first research inquiries looked at how algorithmic accountability measures could and should be applied to the use of AI tools in hiring processes. We released two studies on New York City’s Local Law 144, which subjected employers using AI decision-making tools to annual bias audits — the first law to require such audits for commercial systems. This work was recognized with a “Best Paper” award at the 2024 ACM FAccT conference in Rio de Janeiro.

We’ve since shifted our focus onto how workers themselves are impacted by the rise of AI in the workplace. Our primer, Generative AI and Labor: Power, Hype, and Value at Work, looked beyond speculation to the on-the-ground realities of expertise, compensation, accountability, and professional ethics. This empirically grounded counternarrative has been widely cited, quoted, and shared in a diverse range of publications, including The Guardian, MSN UK, and Marie Claire.

Data & Society’s work to date has made clear that understanding AI’s full impact on workers requires a cross-sector lens. Our primer, Last Place in the AI-First Economy, identified that AI’s entrenchment in the workforce relies on four components: weaponized efficiency, institutional capture, occupational erosion, and racial and structural inequity. These distinctions cut across sectors, industries, occupations, and classes, as further evidenced in our report (404) Job Not Found, which focused specifically on how Black workers in Atlanta are being trained for AI. As Labor Futures Program director Aiha Nguyen explains:

“To build a different future — one that integrates AI with a worker-first approach, not only industry agendas — requires us to understand and change the structures of power, control, and ideology behind AI adoption in the workplace.”

In 2026 we are launching a new cross-sector research project that will provide a grounded analysis to identify constituencies for engagement and organizing, point to policy priorities, and identify opportunities for building different kinds of solidarities across movements. As AI is further embedded across workplaces around the world, this work is essential: without intervention, workers will continue to be excluded from the conversation about how, whether, and when AI should be deployed.