Maryland AI Economic Impact Tracker

Introduction

AI adoption by businesses, governments, and individuals is moving quickly across the country, including here in Maryland. The growth of AI tools and platforms is expected to have a transformative impact on the economy, jobs, and businesses, which in turn could have a cascading effect on state revenues and budgets. The Comptroller of Maryland is committed to monitoring actual and estimated impacts of AI on the economy and state finances to help inform policymakers, businesses, and the public.

The Comptroller’s AI Economy Impact Tracker provides the latest data from trustworthy government and private sector data sources on AI’s impact on the workforce, businesses, state revenues, and other areas of economic performance. This dashboard will be updated quarterly and as new data and forecasts are published.

In addition to publishing and updating this dashboard, the Comptroller will release periodic research briefs starting in 2027 on what AI adoption could mean for the state’s economy, revenues, and budget. These briefs will incorporate the most recent data, economic modeling, and scenario analyses to present a range of Maryland-specific economic and fiscal impacts that could result from accelerating AI adoption.

Workforce: AI Job Creation

AI has added new functions to many already existing jobs that now utilize various AI tools or chatbots to help improve productivity and efficiency. At the same time, AI has created new jobs that require technical skills relating to the development, mechanics, or maintenance of AI systems.

Examples of existing jobs using AI tools include software developers who use AI coding assistants for rapid testing or code generation, customer service representatives who use AI chatbots to accelerate response times, and healthcare professionals who use AI tools to review medical imaging and manage patient records.

On the other hand, examples of new jobs requiring technical AI skills include machine learning (ML) engineers, AI software engineers, AI/ML quantitative researchers, and AI product managers.

Researchers at the University of Maryland Robert H. Smith School of Business manage the UMD-LinkUp AI Maps Project, an AI jobs mapping tool, developed in partnership with the labor market data firm LinkUp, that uses large language model (LLM) tools to record monthly counts of new AI jobs, based on job postings across all 50 states. The researchers define an “AI job” as requiring AI-related technical skills, including neural networks, LLMs, computer vision, deep learning, and pre-training /fine-tuning LLMs. Jobs that only require knowing how to use an AI tool, such as Claude or ChatGPT, are not counted as AI jobs. Similarly, jobs that build or manage AI infrastructure, such as construction workers, electricians, or facility managers at data centers are not counted as AI jobs. The researchers meticulously coded 200,000 job postings as AI or Non-AI and then used LLM tools to classify all job postings in LinkUp’s database starting on January 1, 2018 to the present and ongoing.

Figure 1 presents data from UMD-LinkUp AI Maps Project on annual AI job postings for the years 2023 to 2026 in Maryland and neighboring states. Maryland has 3,600 unique AI job postings so far in 2026 (as of September). Virginia leads the group with 10,200 AI job postings so far in 2026, followed by Pennsylvania with 7,300 and Washington, D.C. with 4,100. The total number of AI job postings (as classified by UMD-LinkUp AI Maps Project) in the U.S. for 2026 is 273,000. The top five states for number of AI jobs are California, New York, Washington, Texas, and Illinois. Virgina is 6th and Maryland is 19th.

In the first nine months of 2026 (through September), AI job postings in Maryland have increased by 79% from all of 2025, which is the exact same percent increase as the national figure. Washington, D.C. has seen a 138% increase during the same period, Pennsylvania has seen a 90% increase, and Virginia has seen a 69% increase.

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Figure 1: Number of Annual AI Job Postings by State, 2023 to 2026

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UMD-LinkUp AI Maps Project (LeaGd: Prof. Anil K. Gupta)
Note: U.S. AI job postings for 2026 (Jan-Sep) is 273,000.

Figure 2 presents data on AI job intensity, also from the UMD-LinkUp AI Maps Project, that measures the share of AI jobs in comparison to all job postings. The figure presents data from the most recent quarter. Maryland had an AI job intensity rate of 1.5% in Q3 2026, meaning 1.5% of all jobs posted in Maryland were AI jobs, based on the UMD-LinkUp AI Maps Project definitions and classifications. Maryland has a similar intensity rate to Pennsylvania (1.3%), both of which are slightly below the overall U.S. intensity rate of 1.8%. The other states in the region have greater AI job intensity than Maryland and the U.S., Virginia at 2.2%, Delaware at 2.4%, and Washington, D.C. at 4.6%. Just one year ago, in Q3 2025, Maryland’s AI job intensity rate was just 0.6%, demonstrating rapid growth over the past 12 months.

Figure 2: AI Job Posting Intensity by State, Q3 2026

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Source: UMD-LinkUp AI Maps Project (Lead: Prof. Anil K. Gupta)
Note: AI job posting intensity in the U.S. is 1.8% for Q3 2026

AI Job Posting Intensity by County

The UMD-LinkUp Maps Project also captures job postings at the county level. In Maryland, AI job posting intensity is greatest in the Central Maryland counties in the Washington D.C. – Baltimore corridor. As shown in Figure 3, Howard County has the highest AI job posting intensity rate in Maryland at 3.1% in Q3 2026. This is more than double the statewide rate of 1.5%. Prince George’s County has the second highest AI job intensity rate at 2.1%. For some counties in Western Maryland and on the Eastern Shore, there are very few or no AI job postings.

Figure 3: AI Job Posting Intensity by County, Maryland, Q3 2026

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Source: UMD-LinkUp AI Maps Project (Lead: Prof. Anil K. Gupta)

Workforce: Projected Job Losses Due to AI

There is much uncertainty and concern about AI jobs displacement (involuntary job loss from AI automation). Unlike the UMD-LinkUp AI Maps Project that tracks new AI jobs, there is limited data on the actual number of jobs lost to date due to AI automation. According to the September 2026 Challenger Reportof announced job cuts by U.S. employers, AI has been cited in 120,136 job cut announcements so far in 2026 (through September), which accounts for 21% of all announcements nationally and is the leading reason for job cuts.

A number of universities and research organizations across the country are analyzing occupations and industries with the greatest exposure (occupations that can be assisted by AI but not necessarily replaced) and/or vulnerability to job displacement (job loss) based on skills that can be automated (replaced) or augmented (assisted) by AI. Some of the research has applied job vulnerability rates to project job losses, which range dramatically – depending on AI adoption projections – from very limited employment disruption to widespread job losses.

One of the most comprehensive analyses available is the American AI Jobs Risk (AAJR) Index developed by researchers at the Fletcher School at Tufts University. The AAJR index estimates job vulnerability for occupations and industries and uses that to project job losses and resulting lost wage income at the state, sub-state, and metropolitan geographic levels. This is also the only comprehensive research that currently provides access to its projected job loss data that can be combined with other occupation and industry datasets to perform additional analysis for projections at the state or local level.

Table 1 presents a comparative list of workforce-related research products that were reviewed for this dashboard. The analysis below is based on the Fletcher School’s AAJR index.

Table 1: Comparative List of Workforce Exposure and Vulnerability Research Products by Release Date

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According to the American AI Jobs Risk Index, Maryland is projected to have the third largest rate of job losses of all states over the next 2-5 years from AI automation at 7.2%, slightly behind Massachusetts and Virginia (both at 7.3%). The index also shows that the Washington-Arlington-Alexandria, DC-VA-MD-WV metro area is the second most vulnerable region to AI job displacement with a projected 9% loss, behind only the San Jose-Sunnyvale-Santa Clara, CA region (projected 10% loss).

With regards to labor income loss, the index estimates that Maryland is projected to have the fifth greatest share of income loss (8.8%) behind D.C., Virginia, Washington, and Massachusetts. Loss of income at this amount would have significant impacts on Maryland households, businesses, state revenues, and government services. It will be critical to monitor actual income losses and new projections, and to also balance these income losses with potential and actual gains in income through AI adoption and other innovations.

Nationally, the index finds that there are between 2.7 to 19.5 million jobs vulnerable to loss from AI, depending on adoption rates over the next 2-5 years. This represents between 2% and 15% of all income from wages and salaries in the U.S. Most of the AAJR index analysis focuses on what they refer to as the “median AI adoption path,” which would result in 9.3 million vulnerable jobs within the range cited above.

Figure 4 is an interactive dropdown map utilizing the AAJR index data that shows both projected job and labor income losses at the state level under the median adoption path.

Figure 4: Map of Projected Percent of Job and Income Loss by State

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Source: The Fletcher School at Tufts University, American AI Jobs Risk Index

Job Automation Risk by Industry

The AAJR index provides projections of job displacement (or loss) by industry. Nationally, the industries with the highest share of projected AI job losses are information, finance and insurance, and professional, scientific, and technical services.

Figure 5 combines the AAJR index’s national industry percentage loss projections with Maryland industry employment data. The largest projected job losses in Maryland, based on the AAJR index methodology, are in professional, scientific, and technical services, which is Maryland’s third largest industry for employment with 280,000 jobs in 2025. Based on the AAJR Index, 44,000 Maryland jobs in this industry are at risk of displacement over the next 2-5 years, which translates to a loss of $5.3 billion in labor income. Educational services is projected to have the second most job losses at 24,000 and finance and insurance is projected to have the second largest labor incomes loss at $1.79 billion.

Figure 5: Projected AI Job and Income Loss by Industry in Maryland

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Source: Analysis of data from the Fletcher School at Tufts University American AI Jobs Risk Index applied to Maryland employment data from the Bureau of Labor Statistics (BLS), Quarterly Census of Employment and Wages (QCEW) 2025
Note: Figure does not show data for all industries. Excludes industries where employment or projection data is not available or data is not directly comparable across the two sources used.

Job Automation Risk by Occupation

The AAJR index also analyzes job vulnerability over the next 2-5 years at the occupation level. Figure 6 combines the index’s national job displacement estimates with Maryland annual occupation data for 2025 and displays the top 25 occupations in Maryland with the greatest job displacement risk at the median AI adoption rate. The figure also shows projected job losses for these 25 occupations at the slow and fast AI adoption rates. Based on this analysis, the occupation with the most projected job losses in the state (under the median adoption rate) is software developers at 8,270 jobs lost, followed by customer service representatives (6,733 jobs lost), management analysts (5,910 jobs lost), sales representatives (5,656 jobs lost), and secretaries/administrative assistants (5,505 jobs lost). The occupations with the most projected job losses under the fast adoption rate scenario are general and operations managers (17,075 jobs lost) and secretaries and administrative assistants (13,349 jobs lost), which rank as the 9th and 5th most job losses, respectively, under the median adoption rate scenario.

Figure 6: Top 25 Occupations with Most Projected AI Job Loss in Maryland

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Figure 7 presents estimated job and income loss for all occupations in Maryland. It also includes an AI exposure rating for each occupation, developed separately by the Maryland State Innovation Team. The rating places each occupation in one of three categories based on the share of its daily tasks that current AI can perform, either on its own or with human assistance: (1) high AI exposure, (2) high augmentation potential, or (3) low exposure. An occupation has high AI exposure when current AI can perform at least half of its daily tasks at or above the level of an average worker. This rating reflects technical capability, not a forecast of job loss, which also depends on cost, regulation, and how employers adopt AI. Of the 737 occupations with employment in Maryland, 317 have high AI exposure, 221 have high augmentation potential, and 199 have low exposure.

Figure 7: All Occupations in Maryland with AI Exposure and Estimated AI Job and Income Loss

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Source: Analysis of data from the Fletcher School at Tufts University American AI Jobs Risk Index applied to Maryland employment data from the Bureau of Labor Statistics (BLS), Occupational Employment and Wage Statistics (OEWS) May 2025; AI exposure ratings from the Maryland State Innovation Team (November 2026).
Note: Excludes some occupations without available data for employment or projected loss. Table is sorted by median projected job loss. AI exposure ratings from MD State Innovation Team reflect the share of an occupation’s daily tasks that current AI can perform and are not a forecast of job loss.

Businesses: AI Adoption and Impacts

Much of the economic and academic research produced to date on AI adoption has focused on employment effects. Another key area of analysis is how AI will reshape business operations and practices. While data on capital expenditures, or capex, by the AI “hyperscaler” firms (Amazon Web Services, Meta, Microsoft, Google Cloud, IBM) are regularly updated and reported (expected to exceed $1 trillion globally for 2026 alone according to Goldman Sachs Research), to date, there has been limited research on what businesses of all sizes and across all industries are experiencing in terms of AI enterprise adoption, AI-related capital and operational spending, AI-inspired productivity gains, and other AI-related business practices, activities, and investments. This type of data is important, as it helps inform actual (as opposed to projected) investment and hiring practices resulting from AI adoption and provides insight into key questions, such as whether the current AI boom is a bubble or is here to stay. As this type of research becomes more readily available, it will be presented in this dashboard.

To create an initial body of research for Maryland business practices, the Maryland State Innovation Team surveyed nearly 300 senior decision makers at AI-engaged firms on AI adoption last summer. Their Maryland Business AI Benchmark Report presents the results of the survey.

Figure 8 shows that 91% of businesses surveyed use AI in some capacity. Most AI use is defined as "Basic Use", which includes use of AI chatbots or AI features embedded in applications. Nearly one-third (32%) of businesses surveyed reported that AI is systemically integrated into business operations, classified as "Advanced Use."

Figure 8: Survey of Maryland Business Owners on Level of AI Use

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Source: Maryland State Innovation Team Maryland AI Business Benchmark Report September 2026

Figure 9 presents data on anticipated staffing impacts resulting from AI automation. Only 4% of businesses surveyed reported that they would eliminate existing positions as a result of AI automation. Meanwhile, 64% of businesses anticipate that AI will empower existing employees to increase their capacity and output and 32% of businesses anticipate hiring fewer replacement staff or slowing down the pace of new hires due to enhanced capacity from AI.

Figure 9: Survey of Maryland Business Owners on Staffing Impacts of AI

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Source: Maryland State Innovation Team Maryland AI Business Benchmark Report September 2026

Figure 10 displays results on the expected impact of AI on job gains or losses. The majority of businesses surveyed (56%) expected to see significant or slight growth in senior-level roles, with the most growth from medium and large firms and technology and engineering firms. This adoption is in contrast to entry-level roles where 81% of businesses expect entry-level roles will stay stagnant or slightly decrease. Notably, medium and large firms expect their entry-level workforce to shrink, while small firms are less likely to anticipate entry-level workforce reductions. The outlook improves for mid-level roles compared to entry-level roles, with 88% of businesses responding that they expect there to be no change or a slight increase in mid-level roles.

Figure 10: Survey of Maryland Business Owners on AI Impact on Size of Workforce

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Source: Maryland State Innovation Team Maryland AI Business Benchmark Report September 2026