Where To Move: States Best Positioned to Withstand the Rise of AI in the Job Market

It turns out sun and employment might both be found in States like Hawaii and Nevada in the event of an AI ‘jobpocalypse’. Let’s take a look at the full analysis based on our comprehensive study which takes into account US Bureau of Economic Analysis jobs data:

Most At Risk 5 States for AI Job Losses

• Vermont tops the list with the highest AI risk score, possibly reflecting its economy's unique composition that may include a significant presence of tech and professional roles susceptible to AI advancements.
• Virginia and Massachusetts feature prominently, likely due to their strong emphasis on high-tech industries, professional services, and educational institutions, sectors identified as highly susceptible to AI transformations.
• Missouri and Connecticut rounding out the top five suggest a significant presence of industries and job roles within finance, insurance, and tech sectors that are vulnerable to AI, underscoring the diverse economic landscapes that contribute to higher AI risk scores.

Least At Risk 5 States for AI Job Losses

• Hawaii, with the lowest risk score, indicates its economy's reliance on tourism and service-oriented sectors, which are currently less affected by AI but may face future transformations as AI technologies evolve.
• Indiana's position near the bottom suggests an economy possibly leaning towards manufacturing and agriculture, sectors where AI's current impact is less pronounced compared to more technologically driven industries.
• North Dakota, New Jersey, and Nevada show relatively lower risk scores, reflecting a mix of economies from agriculture and energy production to service-oriented sectors in gambling and tourism. These variations imply that while AI poses a risk to jobs across the board, its impact is felt differently across states due to the dominant industries and employment patterns.

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Risks To Jobs Based On Education Level

Key Findings:

• Occupations within the finance and insurance sectors exhibit the highest risk scores, indicating a significant susceptibility to AI advancements.
• High-skilled roles, particularly in the professional, scientific, and technical services, have been assigned considerable risk scores, suggesting that higher education does not necessarily insulate from AI's impact.
• Software developers, a profession synonymous with states like California, home to Silicon Valley, face a high exposure to AI, with risk scores exceeding 1.0, indicating the tech industry is not immune to the transformative effects of its creations.
• General and operations managers, often seen as roles requiring nuanced judgment and decision-making, also register high on the risk index, emphasizing that managerial positions are not exempt from the reach of AI.
• The accommodation and food services sector, historically considered less susceptible to automation due to its reliance on personal interaction, is now showing increased risk scores, signifying a shift in how AI is penetrating service industries.
• Educational roles, while varied, have been identified with moderate risk scores, reflecting the growing intersection of AI with teaching methodologies and administrative tasks.
• Transport and storage sectors, including roles like truck drivers, are facing moderate risk exposure, indicative of the growing interest in autonomous vehicle technology and AI logistics optimization.
• States with a heavy concentration of tech jobs, such as California and Washington, may face a unique paradox where the very industry driving their economies also presents a substantial risk due to AI development.

Methodology

Our analysis aimed to derive a weighted risk exposure score for States the United States, reflecting the potential impact of artificial intelligence (AI) on the most common jobs in each state. The initial step involved categorizing a list of occupations according to their susceptibility to AI-related changes, informed by a comprehensive study conducted by the UK Government ("Impact of AI on UK Jobs and Training," available athttps://assets.publishing.service.gov.uk/media/656856b8cc1ec500138eef49/Gov.UK_Impact_of_AI_on_UK_Jobs_and_Training.pdf). This study provided a framework to assign a risk score to each job category based on its exposure to AI technologies.

Subsequently, we identified the top occupations in each state using employment data sourced from a U.S. Government study conducted by the Bureau of Economic Analysis (BEA), which details employment figures by state (https://www.bea.gov/data/employment/employment-by-state). Each occupation was then matched with its corresponding risk score derived from the UK Government's findings.

To calculate the weighted average risk score for each state, we multiplied the number of individuals employed in each occupation by the occupation's risk score to obtain a weighted score. Summing these for all occupations and dividing by the total employment gave us an average risk score for the state, reflecting the weighted impact of AI across its top job sectors.

In the final step of our analysis, we normalized these average risk scores on a scale from 0 to 10 to facilitate a comparative view across states. This normalization allows us to present a clear and concise measure of potential AI impact, relative to the state with the highest risk exposure. The scores indicate the degree to which each state's labor market could be affected by AI, with higher scores representing greater potential impact.