Incorporating AI impacts in BLS employment projections: occupational case studies

EP research connects the potential employment impacts of a new technology with data trends. The goal of this approach is to determine whether these impacts are likely to continue as the new technology grows in adoption and maturation or whether they are likely to diminish as the benefits of the technology are fully realized. For an emerging technology that is not yet reflected in historical data, EP aims to determine whether there is sufficient evidence to support a conclusion about the direction and magnitude of the technology’s future impact on the labor market.

One illustrative example of how this research has affected BLS projections involves photographic process workers. To show a decline in labor demand for this occupation in the early 2000s, when digital cameras were on the verge of displacing most film cameras, EP adjusted its models accordingly. Digital cameras improved on an already-existing technology, and the path to integrating them into business operations and consumer lives was clear. Digital cameras were already replacing film cameras, and the employment impacts of this shift were directly implied by the speed and maturation of technological change. Despite the absence of historical data showing employment declines for photographic process workers, EP projected that employment in the occupation would decline 23.6 percent from 2004 to 2014. Indeed, employment in the occupation started to fall precipitously in the early 2000s, declining from a peak of 86,300 in 2004 to 28,800 in 2014, with a further decline to only 9,200 in 2023.

By contrast, EP did not make any adjustments to its models for another occupation, truck drivers, despite speculations about autonomous vehicles potentially affecting this occupation in the 2010s. At the time, EP determined that the potential impacts of autonomous vehicles were too uncertain, judging that any such impacts were not likely to be felt in the short-to-medium term. Specifically, EP deemed that incorporating the new technology into actual work would likely take time because of regulatory and public safety concerns. So far, history has shown this assumption to have been correct: autonomous trucking remains a developing technology and has not yet had any meaningful employment impacts. Employment of heavy and tractor-trailer truck drivers was about 1.7 million in 2012 and had grown to over 2.2 million by 2023.

The timing and scale of many potential impacts of GenAI are too uncertain to be reflected in BLS projections. New technologies such as autonomous vehicles or AI are harder to assess than technologies that constitute incremental improvements. The lack of relevant historical data on AI technology necessitates assumptions about both the time frame and scale of technological impacts. Brand-new technologies inherently present many sources of uncertainty, including those related to degree of usefulness, developmental roadblocks, regulatory constraints, and pace and cost of adoption. EP generally makes adjustments to its models only if its research on a new technology provides clear expectations for the technology’s employment impacts.

Each occupation involves a set of tasks. Even when a new technology advances rapidly, it takes time for employers and workers to determine how to best incorporate the technology into their work. Although new technologies may change the composition or weighting of tasks performed by workers in an occupation, sometimes dramatically, they may still have no employment impacts. For example, the relatively recent ubiquitousness of smartphones has likely changed the operation of many workplaces, possibly affecting occupational tasks. However, it is unlikely that many workers have lost their jobs to smartphones. In the case of AI, it is important to keep in mind that BLS projections are not about the overall impact of the new technology, but rather about its potential employment impacts.