AI & the Future
of Work

AI is a general-purpose technology with an unusually rapid cycle of improvement and adoption across sectors, diffusing across the United States twice as fast as the internet. Unlike most prior general-purpose technologies, it also extends into certain types of cognitive work, reshaping not only how some tasks are performed but the foundational skills with which those tasks are performed. This combination of speed and scope increases both the scale of potential gains and the uncertainty around the potential near-term disruption.

History shows that technological revolutions simultaneously create, transform, and eliminate jobs—but with outcomes that are notoriously difficult to predict in advance. While past general-purpose technology advances enhanced productivity, created new industries, and ultimately expanded work, they also involved unpredictable and uneven adjustment periods and resulted in fundamental, permanent shifts in many occupations. As with past precedent, AI has the potential to expand the frontier of human work and result in net positive job creation, but uncertainties during the transition period are likely to create disruption for at least some workers and complicate projections of the near-term impacts.

Existing labor market data is not well suited to capture the speed and the breadth of changes that AI is already driving. Key indicators such as employment and productivity are generally lagging indicators that do not capture the underlying shifts in work in real time. As a result, early effects—such as changing skill demands or hiring patterns—are difficult to observe with traditional metrics and will require new data collection and analytic methodologies.

Jobs are composed of bundles of tasks, and where AI is being adopted its impact appears to be concentrated in automating or augmenting the performance of specific tasks rather than eliminating entire occupations. This leads to a gradual but potentially fundamental reconfiguration of roles, often without immediate changes in job titles or employment levels. Therefore, it is increasingly vital for workers to understand how AI impacts key tasks in their roles, the complementary tasks that gain prominence, and the new necessary skills to enhance their individual productivity.

Aggregate employment data remains generally strong; however, preliminary evidence suggests a contraction in entry-level roles in AI-exposed occupations over the last several years. While the extent of AI’s direct role in the downturn remains debated, these positions may be particularly vulnerable to impact from AI because they involve routine and largely standardized tasks that AI may be able to perform. Whether this long-term impact on entry-level jobs is a bellwether to a broader impact to come is currently hard to ascertain. Nevertheless, the implications remain significant, in part because AI may disrupt the pipeline of talent and the well-established pathways for career progression in certain industries.

The potential impact of AI on the economy appears to depend on what the technology can do. However, one must not overlook the importance of adoption, which is neither pre-ordained nor even. Adoption varies across sectors and organizations, shaped by incentives, workflows, and institutional constraints. The balance between augmentation and automation will be determined by these implementation choices, and who participates in making them, rather than technological inevitability.

As AI has started to reshape the skills required in the economy, it is inevitably raising questions about the type of training and roles that may have more enduring value. In some AI-exposed occupations, new digital and technical skills, as well as durable human capabilities—such as critical thinking, leadership, and adaptability—may be key to prosperity. In other emerging and expanding industries—such as new AI-focused job categories and construction to support AI infrastructure—the supply of skilled labor is insufficient.

The current education, credentialing, and training systems remain largely built around front-loaded learning in the lead up to a degree and career, leaving them misaligned with rapidly changing skill demands. To keep pace, these systems must be reimagined to leverage AI as a training mechanism and to produce a balanced mix of foundational capabilities and adaptable domain-specific skills.

AI can broaden access to high-productivity tools and raise worker performance, but it can also concentrate gains among firms, regions, and workers with early access and complementary skills. The outcome will depend on how AI is deployed, who benefits from productivity gains, and how quickly workers can transition skill sets and jobs.

No type of institution—government, industry, or educational—can manage the AI-powered transition alone. The speed, scale, and complexity of AI’s impact require coordinated action across sectors and actors to align incentives, invest in workforce development, and ensure broadly shared benefits. The window for proactive intervention is open.