AI Job Risk by Region: Where Workers Face the Biggest Impact

Artificial intelligence has the potential to disrupt jobs nationwide, but new research suggests the impact could be especially severe in smaller metro areas and college towns. According to a January paper from the National Bureau of Economic Research (NBER), these regions often lack the adaptive capacity needed to absorb job losses, leaving workers more vulnerable to displacement.

Researchers measured adaptive capacity using factors such as liquid savings, age, population density, and skill transferability. Workers with higher savings, younger age profiles, and more transferable skills were found to be better positioned to manage job loss. Living in denser areas also improves outcomes, since larger labor markets enable better job matching and faster re-employment.

By contrast, smaller metros and college towns tend to have fewer industries, lower savings rates, and limited retraining opportunities. This combination makes it harder for displaced workers to transition into new roles, amplifying the risks posed by AI-driven automation.

The findings highlight a growing divide: while tech hubs like San Jose and Seattle may adapt more smoothly thanks to diverse skill sets and stronger financial cushions, less diversified regions could struggle. Policymakers and businesses will need to prioritize workforce development and retraining programs to ensure AI’s benefits don’t deepen regional inequality.

Even if your job is highly exposed to automation, several factors can help you navigate the risks of AI-driven displacement. Having a cushion of savings provides financial stability during transitions, while living in a densely populated city improves job-matching opportunities thanks to larger and more diverse labor markets.

Transferable skills are another critical safeguard. Workers who can apply their expertise across industries such as digital literacy, project management, or analytical problem-solving are better positioned to pivot into new roles. Younger workers also tend to adapt more easily, given longer career horizons and greater flexibility in retraining.

For those in smaller metro areas or college towns, where adaptive capacity is lower, proactive steps like building savings, investing in skill development, and exploring remote or hybrid work opportunities can help offset regional vulnerabilities.

Ultimately, the research underscores that while AI poses risks, individuals with financial resilience, transferable skills, and access to dynamic labor markets are far better equipped to manage disruption and secure new opportunities.

Although workers in professions like software and web development are highly exposed to AI since many of their tasks can be automated they also tend to have stronger adaptive capacity. Higher savings, transferable technical skills, and access to dynamic labor markets give them more resilience when facing job displacement.

In contrast, workers in clerical and customer service roles, such as cashiers and secretaries, are doubly vulnerable. Their jobs are highly exposed to automation, and they often lack the financial cushion or skill flexibility needed to transition smoothly into new roles. This combination makes them more susceptible to long-term disruption.

The difference lies in adaptability. A software engineer may face greater risk of being replaced by AI, but their emergency fund, technical expertise, and ability to retrain in adjacent fields provide a safety net. A cashier, by comparison, may struggle more due to limited savings and fewer transferable skills.

This research highlights the importance of building financial resilience and investing in skill development across all occupations. Workers who diversify their abilities and strengthen their adaptive capacity will be better positioned to navigate the future of work in an AI-driven economy.

Workers in roles with high AI exposure and low adaptive capacity are disproportionately concentrated in small metro areas and college towns across the Mountain West and Midwest. Researchers note that administrative and clerical positions supporting institutional employers dominate these regions, leaving workers more susceptible to automation-driven displacement. Cities such as Stillwater, Oklahoma, and Springfield, Illinois, stand out as examples where AI could have outsized effects on local employment.

By contrast, workers in tech-heavy hubs like San Jose, California, and Seattle, Washington, may be less affected despite their high exposure to AI. These regions benefit from stronger adaptive capacity, including higher savings rates and more diverse skill portfolios. This financial resilience and skill flexibility allow workers to pivot more effectively into new roles when faced with disruption.

The divide highlights how geography shapes the future of work. Smaller towns with concentrated clerical employment risk deeper disruption, while tech hubs with diversified industries and stronger retraining opportunities are better positioned to absorb AI’s impact.

For policymakers and businesses, the findings underscore the need for targeted workforce development and regional investment strategies. Supporting vulnerable regions with retraining programs, financial literacy initiatives, and diversified industry growth will be critical to ensuring AI’s benefits are more evenly distributed.

In contrast, the downside risks for less adaptive workers those with limited savings, narrower skill sets, or weaker networks may be harder to manage. This group is more vulnerable to long-term disruption, especially in regions or industries where retraining opportunities are scarce.

The findings highlight a critical divide: while highly skilled professionals may face exposure to AI automation, their adaptive capacity gives them a safety net. Workers in clerical or routine roles, however, often lack the resources to pivot quickly, making them more susceptible to economic shocks.

For policymakers and businesses, the takeaway is clear: investing in retraining programs, financial literacy, and skill diversification will be essential to ensure that AI’s benefits are shared more evenly across the workforce.

Artificial intelligence is reshaping the labor market, but its impact will not be evenly distributed. Workers in small metro areas and college towns particularly across the Mountain West and Midwest are more vulnerable due to concentrated clerical and administrative roles, limited savings, and fewer retraining opportunities. These regions face disproportionate risks of displacement as AI adoption accelerates.

By contrast, tech hubs like San Jose and Seattle, while highly exposed to AI, benefit from stronger adaptive capacity. Higher savings, diverse skill portfolios, and access to dynamic labor markets allow workers in these areas to pivot more effectively when disruption occurs.

The research underscores a clear divide: adaptability is the key differentiator. White-collar professionals may face automation risks, but their financial cushions, transferable skills, and networks provide resilience. Less adaptive workers such as clerical staff and cashiers lack these buffers, making job loss harder to manage.

For policymakers and businesses, the bottom line is that AI’s benefits must be balanced with targeted support. Investment in retraining, financial literacy, and regional diversification will be essential to ensure that vulnerable communities are not left behind in the AI-driven economy.