Debates about artificial intelligence (AI) and the labor market have largely focused on the future of college graduates and on which individual occupations will be disrupted. Far less attention is being paid to how AI will reshape economic mobility for workers without four-year degrees and across groups of jobs rather than individual jobs. Often left out of consideration is whether the pathways that connect those job groups will remain strong enough to support workers’ upward mobility, meet employers’ talent needs, and drive regional growth.
That omission matters. At its core, the U.S. workforce system rests on a simple premise: When the needs of employers change, workers can transition in ways that advance their economic prospects while supporting the economy. A central question, then, is how AI will reshape the nation’s career pathways—and with them, economic mobility.
To answer that, leaders who are worried about AI’s coming disruptions to the labor market need to think hard about the future of career pathways—not just individual jobs—and the broader set of workers who rely on established pathways for economic mobility.
This report sheds light on these issues by focusing on AI’s impact on career pathways, paying special attention to the more than 70 million U.S. workers who are “skilled through alternative routes,” or “STARs.”
Opportunity@Work coined the term “STARs” to describe workers who do not hold a four-year degree but have developed valuable skills through work experience, military service, apprenticeships, community college, or other training. This report considers the challenging implications AI’s diffusion may have for STARs, and argues that problem-solving will likely need to take place at the regional level. Finally, we suggest a number of urgent questions the field will need to grapple with over the coming years.
Career pathways matter—especially for workers without a four-year degree
Career pathways are an especially pressing consideration as AI spreads. When these pathways weaken or disappear, workers lose not only their current jobs, but also future opportunities for advancement. Meanwhile, employers lose reliable conduits for developing experienced talent.
For workers, any such winnowing matters because economic mobility is shaped by the skills developed in prior roles. Research consistently shows that workers are more likely to move into higher-paying occupations that share underlying skill similarities with their current job. In the aggregate, these interconnected moves constitute employment pathways—sequences of roles through which workers build skills, accumulate experience, and access higher-wage opportunities. The quality and durability of these pathways are central to mobility across the labor market.
These pathways are particularly important for STARs. For these workers lacking a four-year degree, economic mobility depends on the transferability and recognition of their skills as they move along durable job pathways.
Recently, Opportunity@Work analyzed how STARs transition through job types and employment sequences, grouping occupations into three categories based on the role they typically play in those pathways. Drawing from data on wages, worker transitions, and skill similarity across occupations, the analysis identified entry-level “Origin” occupations that provide accessible starting points for STARs; “Gateway” occupations that connect strongly to both lower- and higher-wage work; and higher-wage “Destination” occupations that represent common endpoints for upward mobility (see Figure 1). These categories reflect the typical mobility opportunities associated with different occupations, rather than a rigid career ladder.
In this framework, Gateway occupations play a pivotal role in mobility, offering immediate wage gains from lower-wage work while enabling workers to build the skills needed to transition into higher-wage work and serving as a critical source of experienced talent for employers. STARs account for 62.3% of workers in Gateway occupations, underscoring how central these roles are both to STARs’ upward mobility and to the functioning of the pathways employers rely on to develop experienced talent.
Consider, as an example, customer service representative roles. Roles in this Gateway occupation are accessible from many entry-level Origin occupations (such as receptionists, tellers, cashiers, and couriers), yet they also enable workers to build skills that support transitions into roles in higher-wage Destination occupations (such as human resources assistants and sales representatives).
Such progressions—from Origin to Gateway to Destination occupations—have provided a template for upward mobility over the past 40 years. Overall, more than 23 million STARs have transitioned across occupations on pathways to higher-wage work over the last 10 years.
AI may impact not just jobs, but also the pathways that drive economic mobility
AI’s workplace impacts to any given occupation will not be felt in isolation. Rather, AI systems will likely impinge broadly on entire pathways containing millions of jobs.
Jobs function as interconnected stepping stones, so the disruption of a key role within a pathway can alter employment opportunities both upstream and downstream. And because these pathways depend heavily on key transition points (especially Gateway occupations), disruptions in these roles can have outsized effects on workers’ ability to move into higher-wage work.
For instance, if AI were to significantly reorient or automate a customer service role, the opportunity for economic mobility for workers in Origin roles such as receptionists and clerks would likely also be impaired, undercutting the route toward Destination roles such as payroll and timekeeping clerks and human resources assistants.
These are not singular effects. Given the interconnection of jobs across numerous pathways, AI’s growing diffusion across the economy implies broad impacts on individual jobs, but also on career progressions that matter for both workers and employers who rely on accessible and experienced talent pipelines.
