AI's Economic Impact: Opportunities and Risks for the Commonwealth by vedpvirginia

AI’s Economic Impact: Opportunities and Risks for the Commonwealth December 2023

Table of Contents EXECUTIVE SUMMARY

ARTIFICIAL INTELLIGENCE: WHAT IT IS, HOW IT HAS EVOLVED, AND WHAT’S DIFFERENT NOW What is AI?

Evolution of AI

What is different now?

UNCERTAINTY AND LIMITS TO AI’S GROWTH

FACTORS INFLUENCING INDUSTRY ADOPTION

POTENTIAL INDUSTRY CLUSTERS THAT MAY BENEFIT FROM AI IN VIRGINIA Industries that may see growth from the demand for AI technology

Industries that may benefit from integrating AI tools

Recommendations for attracting and growing industries

WAYS TO ENCOURAGE AI INNOVATION AND ENTREPRENEURSHIP Generative AI’s innovation context

21 22

Existing and emerging AI innovation ecosystems

Federal grants supporting AI research and development

Virginia’s existing assets for AI innovation

Recommendations for encouraging innovation and entrepreneurship

RISKS AND OPPORTUNITIES OF AI ON THE LABOR MARKET IN VIRGINIA Broader labor market context

Occupational impacts

Skills that will be useful for AI

Recommendations to support the existing workforce and prepare the future workforce

CONCLUSION

ACKNOWLEDGMENTS

APPENDIX A — AI EXPOSURE LEVELS FOR SELECTED SKILLS

APPENDIX B — OPEN FEDERAL GRANT OPPORTUNITIES FOR AI R&D AND EDUCATION

NOTES AND SOURCES

Executive Summary In the year since OpenAI debuted its generative language tool, ChatGPT 3.5, companies, governments, and the public have engaged in an important conversation around Artificial Intelligence (AI) and its potential impact on our world. While AI algorithms and tools have been used since the early days of computers, generative AI — the term used to describe the technology behind ChatGPT’s interface — is the first time an AI tool with this technology has been easily accessible and adopted so quickly by the public. The app’s release has ushered in a competition for widespread industry adoption of technology that leverages these recent advancements, with the aim of increasing productivity and profitability in industries ranging from logistics to financial services. Undoubtedly, generative AI and other AI advancements could have widespread impacts on how we design, build, learn, consume, and interact; however, there is no clear consensus among experts on how AI will affect our economy and its workforce in the coming decades. According to 2023’s Gartner Hype Cycle, a well-known framework in the Information Technology community for assessing the maturity of technologies and their potential for productive use, generative AI is at the peak of “inflated expectations.”1 Given the evolving and uncertain nature of AI and its impact, it is important for Virginia’s leaders across government and industry to regularly evaluate and assess AI’s effects to inform their approach to supporting industry growth, innovation, and workforce development now and in the years to come. To prepare Virginia for this next generation of AI technology, Gov. Glenn Youngkin has issued Executive Directive 5, tasking VEDP and other agencies to develop standards, strategies, and protocols, to proactively address the challenges and opportunities of generative AI. VEDP has specifically been tasked with developing a report that addresses the following topics: 1. Identify potential industry clusters that may benefit from AI in Virginia 2. Explore ways to encourage AI innovation and entrepreneurship 3. Assess the risks and opportunities of AI on the labor market in Virginia 4. Develop strategies to support workers impacted by AI 5. Coordinate with schools and workforce programs on steps needed to develop an AI-ready next generation In accordance with Executive Directive 5, this report provides information on AI’s current and anticipated impacts on Virginia’s industries, the innovation ecosystem, and its workforce. The insights and recommendations are designed to inform proactive policy decisions and are grounded in data and expert analysis of AI’s current and forecasted impact on the economy. VEDP leveraged existing public research and data, AI experts across Virginia, and informal conversations with more than 20 companies to develop the insights and recommendations shared in this report. Other state

agencies also contributed to the development of the recommendations including the Virginia Department of Education, State Council of Higher Education for Virginia, the Department of Workforce Development and Advancement, and the Virginia Innovation Partnership Corporation (see Acknowledgments for the full list of supporting organizations). VEDP also leveraged AI tools, such as ChatGPT, to develop preliminary drafts of this report in order to better understand and evaluate the effectiveness of generative AI. These efforts led to the following insights and recommendations for addressing industry and workforce challenges and opportunities brought on by recent AI technology advancements: Industry clusters that may benefit from AI in Virginia — Industries related to the development, delivery, and maintenance of AI tools, including software, cybersecurity, data centers, and business services, are poised to grow. All other industries, from logistics to financial services to healthcare, are expected to see varying levels of productivity impact through the integration of AI technology. This impact will be experienced by businesses in both urban and rural communities. Generative AI will have limited impact on industries and roles dominated by manual labor. In these instances, AI will most often be used for quality control or to optimize operational processes. Factors such as available funding, access to data, and the regulatory environment will affect the rate of AI adoption across industries and firms. Recommendations for attracting new industries and growing existing industries: Recommendations 1. Market Virginia’s advantages to attract and expand existing companies that develop or support AI tech 2. Pilot grants for startup companies pursuing fedRAMP certifications to compete for federal contracts

Detail ■

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3. Support efforts for small and medium-sized businesses to incorporate AI tools in their operations

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Data Center Alley, a high concentration of tech talent, and federal government assets make Virginia a competitive location for AI companies These assets should be marketed and leveraged to attract and grow AI software, cybersecurity, and business services companies The White House has tasked agencies to acquire AI tools and services Companies looking to sell cloud-based solutions to Federal agencies must get certified through the Federal Risk and Authorization Management Program (fedRAMP), a process that costs up to $3 millioni The state should consider a pilot grant that subsidizes the costs of the fedRAMP certification for startups Due to costs and technical abilities, small and medium-sized businesses and potential vendors will face barriers to adoption of AI technology, reducing their ability to compete in the market Virginia organizations like GENEDGE and Virginia Tech are starting to provide this support to businesses, and states like Maryland and Massachusetts have established dedicated programs for these efforts Expanded or new programs should prioritize support for businesses in rural, economically distressed, or marginalized communities

Ways to encourage AI innovation and entrepreneurship — Most AI innovation will likely occur at large, established tech companies able to marshal large data sets and bring in emergent startups. Virginia’s AI innovation ecosystem is most comparable to the ecosystems in peer states like Georgia, Texas, and North Carolina that are leveraging research universities and federal grants. Virginia universities have AI faculty and centers; however, Virginia has not received any major federal grant awards for AI, such as the National Science Foundation’s (NSF)

FedRAMP is a federal Office of Management and Budget program that enables providers of internet-based services to become certified by verifying the security of their processes for use in the federal government.

National AI Research Institutes grant. Virginia has several additional unique advantages, such as The Port of Virginia, proximity to the federal government and numerous major military installations, and the presence of major tech companies and federal contractors. Virginia also has existing R&D tax credits that could be expanded to incentivize further public-private collaboration on AI research. Recommendations for encouraging innovation and entrepreneurship: Recommendations 4. Expand existing R&D tax credits to incentivize established companies to partner with in-state universities

5. Facilitate connections between state organizations, companies, and incubators and accelerators to invest in AI technology and work with Virginia AI startups 6. Appoint a coordinating entity to develop a roadmap for securing federal grant funding for AI R&D in partnership with universities 7. Fund efforts to recruit top faculty for universities, nonprofits, and other research and education institutions

Detail ■

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Virginia should allocate additional funding to the oversubscribed and prorated Refundable R&D Expenses Tax Credit and consider increasing the portion of this incentive for companies that partner with Virginia higher education institutions from 5% to 10% In alignment with JLARC’s 2022 recommendation, increase the per company cap on the Major R&D Expenses Tax Credit for firms conducting R&D with a Virginia higher education institution The need for financial and data resources requires startups to partner with larger companies and organizations to develop effective AI products Virginia-based organizations and companies, such as The Port of Virginia, Amazon, and defense agencies, should partner with incubators and accelerators (e.g., Mach37, Lighthouse Labs) and others to help entrepreneurs access the financial and data resources necessary to develop AI tools Virginia lags peer states in securing federal R&D grants for AI A roadmap developed by a coordinating entity in collaboration with universities, companies, and other stakeholders can identify research opportunities that would be most competitive for federal grant awards AI faculty at peer state universities have enabled those universities to win significant federal funding — for example, faculty recruited through the Georgia Research Alliance’s (GRA) Eminent Scholars program has attracted over $200M to Georgia Tech A similar program to GRA’s could be considered for Virginia to grow its AI faculty talent base and secure federal grants for AI research

Risks and opportunities of AI on the labor market in Virginia — Most occupations will likely see AI tools integrated to augment specific tasks, leading to productivity gains rather than massive job displacement. Higher cognitive skills and socio-emotional skills, in addition to a general knowledge of and familiarity with AI tools and capabilities, will be important for jobs that heavily leverage this technology. However, a deep technical understanding of AI models often will not be required for workers to effectively leverage generative AI technology. Additionally, knowledge of the industry or the work being aided by AI will continue to be essential to ensure the quality of AI-generated outputs. Although most AI technology is expected to enhance, rather than replace, job functions, some jobs, predominantly in customer service, may see displacement as companies integrate more sophisticated text and audio chatbots. As AI tools are incorporated into the workplace, it will be important for Virginia to modify and expand existing education and workforce programs to ensure familiarity with the technology and help workers and job seekers in urban and rural regions develop new skills to leverage this technology.

Recommendations for supporting the existing workforce and preparing the future workforce: Recommendations 8. Direct the Virginia Board of Workforce Development to regularly assess programs and make recommendations to support AI-impacted workers 9. Develop and enhance AI degree programs, including associate’s degree programs 10. Develop and enhance other AI programs, such as non-credit programs and CTE programs in K-12 11. Develop, enhance, and adopt AI curricula and AI standards

Detail ■

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12. Prepare current and future teachers to use and teach students about AI 13. Work with SCHEV, VCCS, and post-secondary institutions to recruit and train faculty and instructors 14. Create pathways through higher education-defense agency partnerships that allow participants to earn a security clearance 15. Develop opportunities for workbased learning around AI skills 16. Use existing programs and funds to support displaced workers

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17. Develop or adopt an AI course for state workers 18. Work with businesses to develop AI literacy among incumbent workers through existing funding streams or new pilot programs

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The Board should engage subject matter experts and state partners to develop annual recommendations to support workers impacted by AI, with a focus on rural communities In the first year, the Board should work with its state partners to identify existing and emerging careers in AI and review available education and training related to AI Community colleges and vocational schools are underutilized to train AI talent Virginia should work with four-year institutions to expand and rebrand degree offerings around AI Short-term credential- or skill-based programs are often more accessible to students and workers than degree programs New incumbent worker training programs should also be considered New AI curricula and AI standards should be incorporated into both K-12 and post-secondary education These curricula and standards should include a focus on how to use AI effectively and responsibly and the risks of using it inappropriately Teacher education programs and professional development should include AI concepts Recommendations 8–12 depend on fully staffed AI programs and teacher training programs at Virginia’s post-secondary institutions

Government contract work requiring security clearances will likely grow as agencies incorporate AI tools Internship pathways between higher education institutions and defense agencies that lead to a security clearance would position Virginia students to access these roles These experiences should be developed for students and incumbent and dislocated workers Virginia’s existing systems and funds to support displaced workers should be leveraged and possibly expanded to support workers displaced by AI Virginia’s Workforce Credential Grant and G3 programs could support workers displaced by AI Virginia should train state workers to use AI effectively and responsibly Workers will need to be literate in AI to use the technology effectively and responsibly Virginia should harness existing funding streams or pilot new programs, targeting rural and distressed areas, to support business efforts to train workers

To effectively address the challenges and opportunities presented by rapidly evolving AI technologies, Virginia’s government and its partners should continuously monitor changes and adapt their approaches to workforce and economic development. This report and other efforts undertaken by agencies in the Governor’s Executive Directive 5 represent a strong first step toward addressing the uncertain challenges posed by generative AI. A continued focus on understanding AI innovation and recognizing its impact as the technology advances will enable the Commonwealth to be a leader in supporting industry growth, advancing AI innovation, and preparing a workforce that will increasingly leverage AI tools.

Microsoft rendering, Fairfax County

Artificial Intelligence: What It Is, How It Has Evolved, and What’s Different Now

Artificial Intelligence: What It Is, How It Has Evolved, and What’s Different Now What is AI? Artificial intelligence (AI) refers to the ability of a machine to replicate human thinking. AI accomplishes this by analyzing data to identify trends and produces an output that aligns with those trends. Applications are numerous and varied, but most AI models built to-date are designed to perform one specific task and can often do that task as well or better than a human can. These tasks can range from beating humans at the game of chess to providing customer service chatbot support for companies by adeptly answering a preprogrammed set of customer questions. Usage of AI has grown more prominent in recent years as different tools have been integrated into offices, factories, and schools.2 Traditional AI runs nearly all of the search engines, robotics, quality control technology, and analytical tools widely used today.3 AI’s newest technology, generative AI, can effectively respond to a broader set of inputs through its use of large language models — algorithms that recognize, summarize, generate, and predict content using very large datasets. These new tools are expected to create even more opportunities for leveraging AI to accomplish tasks and augment human abilities. AI has exceptional potential to impact industries and the economy in two fundamental ways. The first and most common benefit of AI is its ability to increase worker productivity. The second and related fundamental benefit is cost savings. AI tools could allow a set number of workers to generate a level of output that would have historically required a much larger workforce. The economics of AI work best with large groups of employees who do similar tasks, as developing an AI tool can be a costly process that requires the use of specialized skills and large amounts of data.4

Evolution of AI The first AI program was developed in 1956 and was followed by heavy investment from the federal government’s Defense Advanced Research Projects Agency (DARPA). While language processing was one of the main goals of AI research at the time, computers did not have the processing power to execute on leading AI theories for several decades.5 By the 1980s, computers could accomplish “deep learning” to identify trends from large data sets, and “expert systems” that supported data-driven decision-making processes. With the advent of mainstream computers in the 1990s, advanced AI tools like speech recognition were already being integrated into commercial software.6

1956

1997

2009

Logic Theorist, the first AI computer program, is developed

Chess grandmaster Garry Kasparov loses a match to IBM computer program Deep Blue; Dragon Systems speech recognition software is included on Windows

Google builds the first urban autonomous vehicle

1962 President John F. Kennedy signed the Manpower Training Act to train workers impacted by AI and automation 1960

1970

1980

1990

2000

2018 Large language models are trained on data, becoming the precursors to ChatGPT

2010

2020

1958

1980s

2002

2011

Defense Advanced Research Projects Agency (DARPA) is created, with funds allocated for AI research

“Deep learning” techniques are developed, allowing computers to learn using experience

The Roomba autonomous vacuum debuts

Speech recognition ChatGPT assistants like Siri launches and Cortana are added to smartphones

2022

Even in the early days of AI development, industry leaders, academics, and the media predicted that AI and robotic automation would lead to massive job displacement. As early as 1962, President John F. Kennedy signed the Manpower Training Act to train workers affected by AI.7 While the concerns about job loss did not materialize immediately, the advancements of AI in the 1980s and 90s, paired with the use of robotics, caused some job displacement in the manufacturing sector. Automation that leveraged AI, along with other factors, led manufacturing productivity in the United States to double since 1987 and the number of workers on the factory floor to decrease by 25%.8 Over the past two decades, AI technology has become seamlessly integrated into everyday interactions both on and offline. The Roomba autonomous vacuum cleaner, social media algorithms, smart appliances, and predictive text are all possible due to AI. Industry applications, such as predictive analytics, digital twins, and supply chain modeling also leverage this technology. With billions of people and many commercial and industrial processes connected to the internet, the era of “big data” — where large amounts of information are collected by and accessible to computers — has accelerated the use and proliferation of AI and has made breakthroughs like ChatGPT possible.9

What is different now? ChatGPT’s rise to prominence has kicked off the era of generative AI, which differs from traditional AI in several ways. Generative AI has the same goal of taking an input, analyzing it by applying a trend, and producing a desired output, but accomplishes this through advanced pattern recognition programs built on computer algorithms. These programs process vast quantities of data and use trends in that data to generate outputs ranging from text, images, and sound to predictions, category identifications, and strategic planning.10 Like most automated processes, generative AI is best suited to rote tasks, but it also can be used in areas that require some critical skills, are not tightly controlled, or involve advanced pattern matching. In certain areas, AI’s access to vast quantities of data can generate capabilities far beyond a person’s skill level.11 The expanded potential of generative AI to accomplish higher-level tasks has led to renewed concern for widespread job displacement, particularly in white-collar roles that require higher order thinking and complex reasoning that were historically unaffected by AI. While expert predictions vary wildly, many agree that some jobs, like customer service representatives and software developers, carry greater risks than others. Further displacement of production or transportation jobs that combine autonomous robotics with existing AI technology is expected to be limited; however, continued advancements over the coming decades will increase the likelihood of such displacement. There is consensus that generative AI’s predominant workforce impact will likely be in the form of productivity gains that could add trillions of dollars in value to the global economy. Recent research by McKinsey & Company estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to the global economy.12 Estimates from Goldman Sachs, put this number even higher at almost $7 trillion — with productivity growth of 1.5% over a 10-year period.13 Additional academic studies looking at firm-level data have likewise found a strong, positive relationship between AI technology adoption and productivity growth.14

Uncertainty and Limits to AI’s Growth

Uncertainty and Limits to AI’s Growth Despite its many use cases for industry, AI also has several features which will likely restrain its potential. The three main limitations include: 1. A lack of sufficient data to train AI models. AI models consume massive amounts of data during their training — the most recent generative AI models have processed trillions of words.15 Since the quality of an AI model increases roughly proportionately to the amount of training data, complex situations where there are relatively few datapoints for an AI to analyze may prove challenging to build a usable model. For example, if a “legislative AI” tool were asked to recommend regulations for a new industry, the tool may not be able to find enough relevant examples to develop appropriate or useful legislation. Similarly, any novel process without precedent (and associated data) will strongly resist AI modeling.16 Additionally, companies may be reluctant to give their proprietary data to AI models, limiting the customized use of AI for specific industries and challenges. 2. Risks and costs to using models that produce unforeseen errors. This is often referred to as the “black box” problem, which refers to the current difficulty of accessing the logic of an AI model: only the input and the output are known, while the AI’s reasoning is completely opaque. This becomes a major issue when AI models inevitably make mistakes — the reason for the mistake cannot be found or corrected, making the mistake likely to occur again. It also means that an AI’s response cannot be predicted or controlled beforehand, making them risky tools for situations where the consequences of decisions have high human costs, such as warfare or self-driving vehicles.17 3. High energy consumption required to run and maintain these models. Training and operating AI models requires massive amounts of energy, which could strain the grid and require significant increases to planned power production. It is estimated that AI servers could use around 0.5% of the world’s electricity by 2027 — approximately as much as a mid-sized country like Argentina or the Netherlands.18 Governments and utility providers must ensure that the grid and new energy production matches the upward trend in energy consumption generated by increased use of AI. Additionally, it is estimated that training one AI model produces more than five times the lifetime CO2 emissions of a gas automobile.19 In a world increasingly concerned with climate change, the environmental impact of AI is likely to become a key concern for companies and activists. An informative comparison for this challenge is the cryptocurrency industry, another heavy power consumer, where there has been a strong push towards co-locating crypto operations with green energy production.20

Factors Influencing Industry Adoption

Factors Influencing Industry Adoption The primary factors influencing industry adoption are the type of job tasks prevalent in different industries and the extent to which those tasks can be augmented or replaced by AI. Beyond these, several other factors are likely to influence the pace of adoption of AI across different industries: Financial resources and technical capacity — Companies’ financial resources and technical capacity could play a pivotal role in the pace and scope of AI adoption. Significant capital investment, especially for customized solutions, is needed to cover costs like data storage and expertise for AI model development. This can be prohibitive for companies with limited financial resources. Furthermore, the iterative nature of AI — where models require refinement based on real-world feedback — means that companies need sustained financial commitment to realize the greatest benefits. Additionally, technical expertise for data management, model training, and AI system maintenance is crucial for effective AI integration. Companies with strong technical capabilities, either in-house or via strategic partnerships, can more readily adopt AI tools and leverage them to the fullest potential.21 Industry nature and needs — Industry-specific challenges will impact the pace of AI integration. For example, in healthcare, the need for precision and the life-critical nature of decisions means that while there’s immense potential for AI use (e.g., in diagnostic imaging), heightened scrutiny and rigorous validation are required prior to widespread adoption. These industries will likely undergo meticulous, protracted paths to AI integration for some use cases. In contrast, the e-commerce sector, driven by efficiency gains for customer personalization and inventory optimization, might rapidly integrate AI tools. Data accessibility and quality — Data-intensive industries present a more conducive environment for rapid AI adoption. Companies with access to large, accurate, datasets could develop more customized and productive generative AI tools. For instance, the data-rich financial sector has been a leader in AI adoption, leveraging algorithms for high-frequency trading, risk assessment, and fraud detection. Industries with constraints in data access due to regulatory barriers, competitive withholding, data silos, or other factors might not be able to fully leverage generative AI’s benefits.22 Market Competition — Peer competitive pressure is expected to be a top decision factor for AI adoption.23 Firms in highly competitive industries will look to generative AI to realize competitive advantages in data analytics, automation, and predictive modeling to enhance operations and tailor product offerings. Research by Harvard Business Review simulated the economic impact of AI for three groups of companies: “front-runners,” “followers,” and “laggards.” The “front-runners” experienced the largest benefits from AI. “Followers” also benefited but only by a fraction of the benefits realized by “front-runners,” while laggards (many of them nonadopters) witnessed shrinking market shares.24 Regulatory Environment — AI specific regulations could slow the pace of AI’s adoption, with industries more heavily regulated seeing a lower uptake of AI tools. Many policy analysts predict that regulation in the U.S. is more likely to be a patchwork of domain-specific regulations at federal and state levels.25 Industries with high regulation, such as healthcare or finance, where data security and algorithmic transparency are critical, may see restricted use of generative AI tools. In industries where regulatory frameworks are less stringent, such as retail or customer service, the uptake of AI is likely to be more rapid for functions such as marketing analytics or chatbot deployment.26 12

Potential Industry Clusters That May Benefit from AI in Virginia

Potential Industry Clusters That May Benefit from AI in Virginia Industries that may see growth from the demand for AI technology Subsets of several industries that produce AI products and services are likely to see direct growth from demand for generative AI technology. These industries include: ■

Software companies that develop AI tools

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Consulting firms and government contractors that support businesses with AI integration

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Cybersecurity companies that combat security vulnerabilities exposed by AI

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Data centers, due to the data demands of training and maintaining AI programs27

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Semiconductor companies, such as Nvidia, that produce chips to power the AI models28

Each of these traded sector industries are targeted by Virginia for economic development purposes, and many have seen substantial growth in the past five years (see Table 1). Virginia has the opportunity to capture additional growth in these industries spurred by the development and deployment of AI tools. Table 1 AI-related industry employment, output, and growth data

VA jobs (K, 2022)

Contribution to VA GSP* ($B, 2022)

VA historic growth (2017–2022)

Software and Cybersecurity

173.4

31.5

1.6%

1.7%

2.1%

2.64

Consulting Firms

97.6

15.3

2.8%

2.4%

2.6%

1.75

Data Centers

18.3

7.9

8.5%

4.8%

3.3%

1.43

Semiconductors

2.0

1.0

3.5%

0.6%

0.5%

0.34

Category

VA projected U.S. projected growth growth (2023–2028) (2023–2028)

Virginia location quotient

Note: NAICS include 5415 (Software and Cybersecurity), 5416 (Consulting Firms), 5182 (Data Centers), 3332 and 3334 (Semiconductors) *Gross state product Source: Moody’s Analytics; JobsEQ; VEDP analysis

Virginia already has strong industry clusters in most of these industries, a high concentration of tech talent, and the presence of a robust data center industry. In 2022, Virginia saw the fifth-highest number of AI job postings, ahead of tech hotspots like Washington, high-population states like Florida and Illinois, and peer states like Georgia and North Carolina (see Table 2).

Table 2 States by total 2022 AI job postings Rank

State

Job postings (K)

AI jobs per 10,000 jobs

Percent of U.S. total (%)

California

142.1

78.5

17.9

Texas

66.6

47.4

8.4

New York

43.9

45.1

5.5

Massachusetts

34.6

91.5

4.3

Virginia

34.2

82.3

4.3

Florida

33.5

34.2

4.2

Illinois

31.6

51.3

4.0

Washington

31.3

85.9

3.9

Georgia

26.6

53.8

3.3

Michigan

25.4

57.3

3.2

North Carolina

23.9

48.3

3.0

Tennessee

11.2

33.6

1.4

South Carolina

4.9

21.2

0.6

Note: Includes top states and Southeastern peer states Source: 2023 AI Index Report29

Owing to Virginia’s proximity to the federal government, the integration of AI technology into government and military agencies will likely be the leading drivers of the Commonwealth’s strength within these industries. President Joe Biden’s Executive Order on AI, issued on Oct. 30th, 2023, includes a directive for government agencies to “modernize federal AI infrastructure.”30 Virginia companies with an AI focus, like those in Figure 1, should be well-positioned to support agencies as they work to integrate AI technology. These companies are positioned across a broad array of industries and range in size from small tech startups to Fortune 500 federal contractors. Regardless of size or industry, all are at the leading edge of identifying ways to effectively leverage AI in their work. Figure 1 Virginia companies developing AI technology or supporting AI integration*

*For illustrative purposes – not comprehensive

Industries that may benefit from integrating AI tools All industries will likely see AI’s role expand in analytical tools, organizational logistics, and business software products. Given that AI tools augment specific occupational tasks, such as writing code or conducting analysis, industry impact will vary based on their occupational composition. Near-term effects of generative AI are expected for industries with more expansive levels of administrative, IT, scientific, customer service, and creative jobs. Given the extent of these roles across a range of industries, the impact of AI will be felt in urban and rural businesses across the Commonwealth.

Leidos, Fairfax County

General Dynamics, Fairfax County

Booz Allen Hamilton, Fairfax County

An analysis by the Pew Research Center provides estimates of the percentage of workers by industry that could see high exposure to AI tools, a strong proxy for which industries might see quicker adoption rates and larger productivity gains from generative AI technology (see Table 3).31 The greatest impacts will likely be in business services sectors, including professional, scientific, and technical services, information technology, finance, and management companies — industries that are core to Virginia’s growth and have been prioritized for economic development efforts. As discussed earlier in this report, other industries, like healthcare and education, are likely to face regulatory hurdles to AI integration and manual labor-intensive industries like construction will have fewer use cases or productivity gains from AI technology.

Table 3 Industries by estimated share of workers likely to see high AI exposure at their jobs and each industry’s relative employment presence in Virginia VEDP core industry Industry

Additional industries Share of workers likely to see high AI exposure

Professional, Scientific, and Technical Services

52%

2022 Virginia jobs (K) employment

*Location quotient

496.7

11%

1.55

Finance and Insurance

37%

157.9

0.86

Management of Companies and Enterprises

37%

85.9

1.29

Government

36%

863.9

19%

1.34

Information

33%

72.4

0.85

Wholesale Trade

32%

113.7

0.70

10.7

0.72

29%

Utilities Manufacturing

22%

248.8

0.71

Transportation and Warehousing

19%

161.5

0.83

Arts, Entertainment, and Recreation

17%

69.2

0.94

Health Care and Social Assistance

13%

479.9

11%

0.84

Administrative and Support and Waste Services

11%

275.3

0.98

Educational Services

11%

98.2

0.89

Other Services (except Public Administration)

10%

228.0

1.04

Real Estate and Rental and Leasing

10%

72.7

0.93

Agriculture, Forestry, Fishing, and Hunting

23.1

0.44

Mining, Quarrying, and Oil and Gas Extraction

5.4

0.36

Retail Trade

407.9

0.95

Construction

251.1

0.97

Accommodation and Food Services

334.5

0.92

*Location Quotient (LQ) measures the concentration of an industry’s employment in Virginia relative to the concentration of an industry’s employment in the U.S. An LQ of 1.0 means Virginia’s concentration of employment in that industry is the same as the U.S. A higher LQ means that employment in Virginia is more concentrated relative to the U.S., and vice versa for LQs below 1.0. Source: Pew Research Center32; JobsEQ, Q2 2023

Virginia has several high employment industries that could see greater levels of AI integration to augment occupational tasks. The Professional, Scientific, and Technical Services sector, which forms 11% of Virginia’s employment and is 50% more concentrated in Virginia than the U.S. at large, is the most likely to experience extensive AI integration. This sector is inclined towards innovation and is likely to leverage generative AI for advanced data analytics, research and development, and technical problem-solving. This sector also includes many of the consulting firms that would support integration of AI technology in other industries. The government sector, which constitutes 19% of Virginia’s employment and has a location quotient of 1.34, is also expected to see high levels of generative AI use. Encompassing a range of functions from administrative to security, this sector could see significant changes with the integration of AI for data management, policy analysis, and even security enhancements. The traditionally cautious and regulated nature of government work and the slow pace of procurement may moderate the pace of AI adoption. Other high employment sectors, such as Health Care and Social Assistance and Retail Trade that also form significant portions of Virginia’s employment landscape, might experience varied impacts. Health Care, with a location quotient of 0.84, may adopt AI for patient data management and tools, but could face regulatory hurdles. Virginia’s Retail Trade, close to the national average in concentration, might see AI integration in inventory management and customer service but it is not likely to impact the workers managing store shelves. Mapping these expected impacts regionally reveals how industry composition might cause the impact of AI to be felt at different rates across the Commonwealth. Communities with a higher concentration of professional services, finance, IT, or similar industries will likely see greater impacts (see Figure 2). These impacts are expected to be a combination of augmentation and displacement, with augmentation likely comprising the majority. Figure 2 Virginia localities by employees’ estimated exposure to AI tools

Source: O*NET33; Goldman Sachs34; Pew Research Center35; VEDP analysis

Figure 2 highlights that Virginia’s urban areas are likely to be more impacted than rural areas. This uneven exposure presents potential positive and negative implications for rural Virginia. Rural economies, more concentrated in agriculture and manufacturing, are at lower risk for job displacement than urban ones; however, call center and customer service jobs could see displacement from generative AI, and rural economies are less likely to realize significant economic uplift from generative AI, potentially widening the urban-rural economic divide. Additionally, rural communities and businesses are often slower to adopt new technology, exacerbating the digital divide and creating critical skill gaps for rural workers in the increasingly technology-driven economy. To limit the impact of these challenges, Virginia should support small and medium businesses in technology adoption, particularly in rural areas, and prioritize development and implementation of AI training and education programs in rural communities.

Recommendations for attracting and growing industries 1. Market Virginia’s advantages, including its high concentration of tech talent and proximity to the federal government and Data Center Alley to attract and expand existing software, cybersecurity, and business services companies that work on AI technology Knowledge work companies focusing on developing or deploying generative AI tools are poised to grow in the coming years. Virginia offers several key advantages that should be leveraged to recruit new and expand existing companies, including Data Center Alley, Virginia’s existing tech talent base, and proximity to the federal government and military assets. Companies supporting the deployment of generative AI tools will greatly benefit from the proximity to the federal government, which through the White House’s recent Executive Order on AI, has prioritized acquiring AI products and services for agencies and accelerating the hiring of AI professionals in government.36 2. Pilot grants for startup companies pursuing fedRAMP certifications to compete for federal contracts Virginia’s proximity to the federal government and the priorities outlined in the White House’s Executive Order on AI, creates an opportunity to attract and expand AI startups looking to compete for federal contracts. Startups and smaller contractors face barriers to entry, including the need to become fedRAMP certified — a process that can cost between $250,000 and $3 million.37 The state should consider a pilot grant program that subsidizes the costs of the fedRAMP certification for startups and small companies. 3. Support efforts for small and medium-sized businesses to incorporate AI tools in their operations Costs and the technical expertise required to integrate AI tools effectively into business operations mean small and medium-sized businesses are less likely than larger businesses to integrate AI technology, reducing their ability to compete in the market. Smaller businesses would benefit from financial and technical support for integrating AI technology. Several Virginia organizations are supporting businesses in this way already, including GENEDGE through its consulting services and Virginia Tech’s Industry 4.0 for the ACE Workforce project.38 A few states, including Massachusetts and Maryland, have dedicated funding for Industry 4.0 programs. MassTech, Massachusetts’s business innovation agency, started a pilot program called AI Jumpstart in 2021 to support businesses in adopting AI to enhance their operations and productivity. The program is a partnership between MassTech and universities, including Northeastern, Boston University, and Tufts. $2 million in state funding was allocated to the program, which helped 18 small businesses deploy and

embrace AI technology. Maryland’s Manufacturing 4.0 program is another example of state-provided grants to companies investing in advanced technologies like AI to increase their competitiveness and support growth.39 Through existing or additional programs, Virginia can support businesses in leveraging AI tools to increase their competitiveness and keep Virginians employed. As these programs are developed or expanded, consideration should be made for prioritizing efforts in rural and distressed communities where small and medium-sized business are economically vital.

Virginia Tech Innovation Campus rendering, Alexandria

Ways to Encourage AI Innovation and Entrepreneurship

Ways to Encourage AI Innovation and Entrepreneurship Generative AI’s innovation context Since generative AI requires large amounts of data and energy to develop effective tools, the development of AI has traditionally been led by researchers and small startups that have the financial support of larger tech companies.40 Several industry leaders, including OpenAI, were founded as non-profits, offering their initial products and research to the public for no fee. This has reduced barriers to entry for the time being, with many small startups entering the space.41 These startups face stiff competition from tech giants such as Google, Meta, and Microsoft that are taking advantage of their vast financial and data resources to build large AIfocused teams and train their AI models. Startup success stories, most notably OpenAI, come from long-existing partnerships with these larger companies and joint development of innovative products.42 Because of this, success in AI will likely favor large companies with the ability to marshal large datasets and bring emergent startups into their own organizations. The limited growth in the number of newly funded AI startups supports this trend (see Figure 3). Despite a jump in growth from 2020 to 2021, the number of AI startups actually declined from 2021 to 2022. Figure 3 Number of newly funded AI companies in the United States, 2013–2022 700 600 542

500 400 300 200 100 0

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Source: 2023 AI Index Report43

Existing and emerging AI innovation ecosystems The resource requirements for training generative AI models have also limited the geographic dispersion of innovation and startup investment (see Figure 4). California, New York, and Boston, areas where major tech companies and ample capital exist, dominate the AI innovation space. However, several metros are emerging as players in the AI innovation space that provide examples of how Virginia can accelerate innovation with its existing assets. An assessment by the Georgetown Center for Security and Emerging Technology shows rapid talent growth occurring in competitor states like Texas, Georgia, and North Carolina, which have top AI university programs that are capturing federal funding.44

Figure 4 Total investments in the top 1000 AI companies by major metro area

Source: Georgetown Center for Security and Emerging Technology45; Crunchbase; VEDP analysis

In these emerging AI markets, universities are driving talent growth and innovation and are heavily supported by federal research funding.46 Universities face similar limitations to startups because energy and data center resources are shared across scientific research domains. However, universities are starting to leverage sizable grant funding to advance generative AI innovations and develop research centers for AI capabilities. While universities may not be able to compete with broad AI models generated by tech companies, they can develop AI models that leverage significant bodies of research on narrow topic areas of AI or targeted subfields of other industries, such as healthcare.

Federal grants supporting AI research and development In FY22, the federal government spent over $1.7 billion on AI R&D across 16 agencies (Figure 5). The bulk of the funding came from Department of Defense (DoD) and the National Science Foundation (NSF); however, AI funding has been increasing for other agencies, such as the Department of Energy (DOE), the National Institutes of Health (NIH), and the Department of Agriculture (USDA). In addition to R&D spending, the federal government has supported the commercialization and adoption of AI innovations through the Economic Development Administration’s (EDA) Build Back Better and Tech Hubs grant programs47 (see Appendix B for open federal grant programs for AI innovation and education).

Figure 5 Federal investment in AI R&D by agency, FY 2022 ($M) 700 600 500 400 300 200 100 0 Department National Science National Institutes of Defense Foundation of Health (including DARPA)

USDA

Department of Energy

Other*

*Other includes: Department of Homeland Security, Food and Drug Administration, National Institute of Standards and Technology, Department of Transportation, National Institute of Justice, Department of the Interior, National Institute for Occupational Safety and Health, NASA, National Oceanic and Atmospheric Administration, National Telecommunications and Information Administration Source: National Science and Technology Council48

NSF has emerged as a major player in the federal government’s push to harness opportunities and address risks associated with AI.49 Over the past three years, the NSF has awarded almost $500M to establish National Artificial Intelligence Research Institutes at universities and other research organizations. To date, no Virginia institution been designated as a National AI Research Institute (see Figure 6). Figure 6 NSF-led National AI Research Institutes and total grant funding

Source: National Science Foundation50

Universities in peer states such as North Carolina, Georgia, and Texas demonstrate how Virginia’s institutions can capitalize on these federal grant opportunities to fund new degrees and develop research centers for innovation. Universities in each of these states have higher education programs focused on AI that have spurred innovation, talent growth, and further investment. These institutions have also utilized federal funding to create new research centers, add AI-relevant university degrees, and engage in cross-university partnerships. NC State, Duke University, and the University of North Carolina at Chapel Hill have all leveraged NSF funding to build their AI-focused programs, with the first two receiving National AI Institute designations and funding. In 2021, NSF awarded $20 million to NC State to establish the AI institute for Engaged Learning, with UNC Chapel Hill receiving $4.5 million as part of the award to develop AI tools for machine learning and computer vision.51 Duke University also received a $20 million NSF award in 2021 to establish the AI Institute for Edge Computing Leveraging Next-generation Networks.52 NC State partnered with the University of Minnesota and several other universities for the NSF’s 2023 funding round to establish the AI-CLIMATE Institute focused on providing AIpowered solutions to address climate issues such as reducing carbon emissions in agriculture.53 In 2020, the Department of Labor awarded $6 million to NC State to establish the Artificial Intelligence Academy to support workforce development. The program is designed to bring diversity to the AI workforce by upskilling IT professionals who are either underemployed or unemployed, veterans or from disenfranchised groups.54 Georgia has also leveraged NSF funding to successfully create AI-focused research institutes and develop workforce programs. In 2021, Georgia Tech received three $20 million awards from the NSF to fund the AI Institute for Advances in Optimization (AI4Opt), AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), and the AI Institute for Adult Learning and Online Education (AIALOE), with the latter being led by the Georgia Research Alliance.55 Additionally, Georgia Tech won a $65 million Build Back Better grant from the EDA to develop the Georgia Artificial Intelligence Manufacturing Technology Corridor (GA-AIM).56 With this grant, Georgia Tech is centralizing its AI intelligence and manufacturing innovations at their AI Manufacturing Pilot Facility where manufacturers can create and test AI innovations for the government and conduct industry pilot trials.57 In addition to these innovation-focused grants, Georgia Tech developed innovative educational programs that have allowed the university to become a leader in computer science and AI. The university was the first in the United States to offer an online AI master’s program beginning in 2014. This low-cost program aims to upskill existing IT workers, creating a pipeline for the future AI industry.58 The entire program costs $7,000, enrolled over 14,000 students in its first five years, and has served as a blueprint for programs in other states.59 For example, in 2020, the University of Texas at Austin used a portion of its $20 million NSF grant to develop an online AI program based on Georgia Tech’s model.60

Virginia’s existing assets for AI innovation In addition to producing top tech talent, Virginia’s universities have existing AI research assets that can be leveraged to advance innovation and AI talent in the Commonwealth. Below is a selected list of Virginia’s major university assets that is not meant to encompass all AI efforts in Virginia.

Virginia Tech’s Sanghani Center for AI & Data Analytics, and the Hume Center for National Security and Technology Virginia Tech’s Sanghani Center for Artificial Intelligence & Data Analytics offers both undergraduate and graduate programs for studying big data research and education. With 20 faculty and 120 graduate students, the Center is located at Tech’s Northern Virginia campus but has faculty and students in Blacksburg. The Center’s research focuses include Network Analysis, Adversarial AI, Visual Analytics, and human-AI collaboration, and has received nearly $15 million in research grants.61 Virginia Tech has also leveraged private partners to expand educational offerings in AI. In 2021, Tech’s Hume Center for National Security and Technology partnered with Deloitte to launch the Deloitte Graduate Research Program on Artificial Intelligence. The program is designed to prepare students for careers in AI in pragmatic ways and technical skills. Students are advised by Hume Center faculty and Deloitte staff to complete their research.62 George Mason’s Center for Mathematics and Artificial Intelligence In 2020 the Center for Mathematics and Artificial Intelligence was created to enable research and learning experiences that bridge academia, national labs, industry, and government. The Center has received federal funding for 19 projects since its inception, including funding from the NSF, DARPA, and the Department of Energy.63 University of Virginia’s Human-AI Technology Lab, and the Artificial Intelligence Research Team The Human-AI Technology Lab focuses on using AI to improve health outcomes and behaviors. The team also develops mobile applications based on its research to support positive health outcomes.64 UVA’s Computer Science Research Department has also created an AI Research Team of 20 faculty members focusing on advancing AI research and education in a range of disciplines.65 Research and Development tax credits Virginia has two R&D incentives for firms, the Refundable R&D Expenses Tax Credit, and the Major R&D Expenses Tax Credit. One element of the Refundable R&D Expenses Tax Credit incentivizes corporate R&D partnership with universities; businesses receive an increased tax credit rate from 15% to 20% if the R&D was conducted with a Virginia higher education institution. Currently, demand for the incentive exceeds available funding and companies are receiving prorated amounts. In 2022, Arizona, Arkansas, Mississippi, and Nebraska offered similar incentives though all offered larger rate increases for partnering with an in-state higher education institution and higher maximum rates.66 The Major R&D Expenses Tax Credit is available to firms with over $5M in annual qualifying R&D expenses, and does not provide any unique benefit for R&D collaboration with universities or other education institutions. In 2022, JLARC estimated that tax credits to smaller firms, like the Refundable R&D Expenses Tax Credit, create $1.75 in R&D spending for every $1 of credit, while those to large firms, like the Major R&D Expenses Tax Credit, create $1.25 for every $1 of credit.67

Recommendations for encouraging innovation and entrepreneurship 4. Expand existing R&D tax credits to incentivize established companies to partner with in-state universities Virginia should increase the incentive to partner with an in-state higher education institution by raising the associated tax credit rate. Additionally, the Refundable R&D Expenses Tax Credit is oversubscribed and

prorated; its credit cap should be increased to realize the benefits of this recommendation. The Major R&D Expenses Tax Credit does not incentivize R&D collaboration with higher education institutions. In alignment with JLARC’s 2022 recommendation, Virginia could establish that mechanism within the existing incentive framework by increasing the per company cap for firms conducting R&D with an in-state higher education institution.68 5. Facilitate connections between state organizations, companies, and existing Virginia incubators and accelerators to invest in AI technology and work with Virginia AI startups Significant barriers to entry exist for entrepreneurs in the generative AI space. While entrepreneurs can compete in the development of generative AI models for specific use cases (e.g., logistics analytical models), those startups will still need access to significant financial resources and training data from large private and public organizations to build effective tools. To provide an environment for AI startups in Virginia, larger companies and organizations, such as the Port of Virginia, Amazon, defense agencies, the Commonwealth Center for Advanced Manufacturing, the Commonwealth Center for Advanced Logistics Systems, and others should partner with incubators and accelerators (e.g., Mach37, Lighthouse Labs) to provide AI entrepreneurs access to the resources and initial market needed to develop their products. 6. Appoint a coordinating entity to develop a roadmap for securing federal grant funding for AI R&D in partnership with universities Securing federal funding for higher education institutions is one of the primary ways states are developing AI innovation ecosystems. Winning major grants for AI through the NSF, Department of Energy, or other sources would allow Virginia’s universities to lead in research areas, build student and faculty talent, establish connections to AI companies, and develop AI-related research assets such as a National AI Institute. Virginia institutions, well positioned to capture federal contracts and funding, have an opportunity to leverage these partnerships for grant applications. A coordinating entity, working with higher education institutions, businesses, and other stakeholders, should lead the development of a roadmap that will provide a strategy for securing federal grant dollars for AI innovation, R&D, and talent development. The roadmap would establish key research areas that are wellpositioned for funding and identify ways to break down silos between institutions and stakeholders that will lead to more robust federal grant applications. 7. Fund efforts to recruit top faculty for universities, nonprofits, and other research and education institutions, similar to Georgia Research Alliance’s Eminent Scholars Program Despite Virginia’s strong research universities and federal connections, other states’ networks of companies and higher education have been the leaders in winning competitive federal grants. Attracting leading AI faculty to universities and encouraging commercialization is essential for Virginia to be a leader in AI. An effective example of such a program is the Georgia Research Alliance’s Eminent Scholars Program, which has already yielded $200M in AI research funding through faculty recruited to Georgia Tech.69 In addition to R&D funding, such a program is likely to attract more companies and talent to Virginia to collaborate with leading researchers.

Risks and Opportunities of AI on the Labor Market in Virginia

Risks and Opportunities of AI on the Labor Market in Virginia Broader labor market context Over the past two decades, the U.S. labor market has seen two major transformations. The first has been the progressive decline in the labor force participation rate (see Figure 7). The second has been a significant structural transformation, where technology has created new markets, like the gig economy, and further widened the pay gap between cognitive and manual tasks. Figure 7 Labor force participation rate (%) in Virginia (2000–2023) 72.0 70.0 68.0 66.0 64.0

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2001

60.0

2000

62.0

Source: U.S. Bureau of Labor Statistics (BLS) and Current Employment Statistics (CES), October 2023

From these dynamics, the advent of generative AI presents both challenges and opportunities. With labor shortages in many industries, companies are looking for ways to use AI to make existing employees more productive and to streamline operations.70 Productivity impacts will not be automatic but rather depend on how effective AI tools are at allowing workers to shift to more value-added tasks and how well employees work in tandem with AI systems. In cases where this is possible due to strong AI tools, employees will need training and support to effectively utilize these tools. The expanded use of generative AI tools might have potential impacts on the technology-driven skill shifts and wage disparities we have seen in the last two decades.71 AI-driven automation tends to disproportionately affect routine-intensive tasks; however, generative AI expands the scope of what may be considered “routine.” Drafting

documents, developing code, talking with customers, conducting research, and analyzing data all have elements that can be completed with great efficiency by generative AI. Leveraging these tools will increase demand for people who have the technical skills to develop and manage AI technology and people who have familiarity with AI tools, in general. It is likely that low-skilled jobs, which are often non-routine and harder to automate, will realize lower productivity gains. These jobs may see relative stability or even a modest increase in demand, although the productivity gains in high-skilled jobs from generative AI may increase the competition for talent and exacerbate wage disparities.

Occupational impacts Predicting the nature of a mature, AI-supported work environment is an imprecise science with many unknowns. Nonetheless, there have been numerous attempts to quantify the size and scope of AI’s impact on workforce. On the upper end, Tyna Eloundou of OpenAI and colleagues have estimated that up to 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of AI technology (specifically, large language models).72 Joseph Briggs et al., writing for Goldman Sachs, have advanced a more conservative figure of around 60% of the workforce.73 In every case, the analyses emphasize that although the impacts of AI would be felt to some degree by almost all employees, some professions are likely to be impacted more acutely. An analysis by VEDP looks at the comparative impacts of AI across occupation groups. Based on methodology used by Goldman Sachs and Pew Research, our analysis examines the core tasks of each occupation and AI’s capacity to complete those tasks (see Appendix A). We have scored occupations based on the approximate percentage of overall work which could be impacted by AI, a measure which considers the feasibility of AI to be used for core tasks, how frequently those tasks are done within each occupation, and the importance of manual tasks to the occupation (which are less conducive to generative AI). The results of our analysis, which align with other published reports (e.g. Edward Felton’s 2019 study from Princeton University74), show that white-collar work, especially white-collar work of a repetitive nature, should be most impacted by AI. Some of the occupations which are most likely to be impacted are in the areas of administrative support, architectural drafting, engineering, life sciences, and legal services. These professions tend to be more highly skilled and have avoided automation until the present. On the other hand, the lowest-scoring occupations largely are non-office jobs that require a high degree of physical effort and exertion. For businesses, the areas where there are the greatest potential efficiency gains from AI align neatly with these areas of high potential occupation impact. When McKinsey & Company reviewed 63 AI use cases across 16 business functions, their research found that about 75% of the value that generative AI use cases could deliver falls across four areas: customer operations, marketing and sales, software engineering, and R&D.75 Business considerations will push companies to ensure that AI’s potential uses across occupations are implemented for optimal efficiency.

Table 4 Approximate percentage of work impacted by AI across occupation groups Approximate % work impacted* by AI

Virginia employment (K)

Virginia location quotient

Office and Administrative Support Occupations

36%

443.1

0.91

Architecture and Engineering Occupations

36%

67.0

1.03

Life, Physical, and Social Science Occupations

34%

37.1

1.08

Legal Occupations

32%

35.9

1.13

Arts, Design, Entertainment, Sports, and Media Occupations

27%

49.5

0.92

Management Occupations

27%

227.3

0.88

Business and Financial Operations Occupations

27%

360.2

1.42

Protective Service Occupations

26%

100.8

1.12

Healthcare Practitioners and Technical Occupations

25%

221.7

0.94

Sales and Related Occupations

25%

350.8

1.02

Computer and Mathematical Occupations

24%

241.6

1.84

Farming, Fishing, and Forestry Occupations

23%

5.6

0.46

Construction and Extraction Occupations

23%

161.7

1.02

Production Occupations

23%

165.9

0.72

Installation, Maintenance, and Repair Occupations

21%

155.3

1.02

Food Preparation and Serving Related Occupations

21%

309.0

0.94

Personal Care and Service Occupations

20%

76.8

1.03

Educational Instruction and Library Occupations

20%

237.3

1.07

Transportation and Material Moving Occupations

20%

306.2

0.86

Building and Grounds Cleaning and Maintenance Occupations

20%

118.5

1.05

Community and Social Service Occupations

18%

63.4

1.05

Healthcare Support Occupations

16%

138.7

0.78

Occupation group

*Impact is an approximate modeled measure meant to differentiate degrees of impact between occupations rather than directly estimate time spent Source: O*NET76; Goldman Sachs77; Pew Research Center78; VEDP analysis

In some cases, generative AI will lead to the creation of novel jobs that pair with the development and management of the technology. These roles will center on the design, development, and refinement of AI algorithms and models, and require expertise in data science, machine learning, and computational techniques. Not all new roles will be technical in scope. The use of AI is also expected to create positions dedicated to the ethical, legal, and societal implications of AI deployment. As AI systems become more widely incorporated in work tasks, there may be an escalating demand for professionals who can work effectively with AI tools to apply enhanced decision-making, personalized interventions, and strategic foresight.79 Finally, in some cases AI could lead to job displacement. As with many prior technological advancements, this risk receives the most attention from industry leaders, policymakers, and the media. While job displacement due to technological advancements has occurred, past predictions of massive permanent unemployment have not materialized. Generative AI does pose some occupational risks that are worth noting. These risks involve professions already seeing displacement from traditional AI and additional professions due to the enhanced capabilities of generative AI. Traditional AI, for example, is already impacting customer service roles and it is widely anticipated that generative AI’s advanced language capabilities could accelerate job displacement in this profession as companies integrate more sophisticated text and audio chatbots. There is additional concern, given generative AI’s exceptional ability to write computer code, that software developers are at risk of displacement. Many manual labor jobs that have seen displacement from traditional AI and robotics, such as manufacturing production workers, are unlikely to see further job displacement in the near-term. The ability of generative AI to interact with a physical environment through robotics with the same skill as a human is not yet possible, although industries may see incorporation of generative AI into machinery for more rote tasks.80

Skills that will be useful for AI Generative AI tools are expected to enhance workers’ abilities to engage in higher-order cognitive tasks such as critical thinking, complex problem-solving, and analytical reasoning. Research by Microsoft found that the top skill that business leaders believe is essential for an AI-powered future is analytical judgment.81 These skills, less amenable to automation, become central to navigating a workplace with increasing human-AI collaboration. The expected proliferation of AI also increases the need for digital fluency. Even in an era where computers in the workplace have become ubiquitous, over three-quarters of workers do not feel ready to operate in a digitalfirst world.82 As workplaces become more technologically integrated, a foundational understanding of digital tools, including the basics of AI and machine learning use cases, becomes crucial. This digital fluency transcends mere operational knowledge and encompasses an understanding of the ethical, social, and business implications of AI systems. Such comprehension allows professionals to harness AI’s potential while being cognizant of its limitations and ramifications. As AI systems permeate the workplace, we will likely see a greater demand for socio-emotional skills, often termed “soft skills.”83 Emotional intelligence, interpersonal communication, and adaptability gain prominence in an environment where machines undertake data-driven decision-making. Skills necessary to navigate interpersonal relationships, manage teams, and adapt to changing scenarios become pivotal. This underscores the enduring importance of uniquely human attributes, even in professions with increasingly automated tasks. To effectively deploy AI technologies, companies will need the right mix of talent to translate business needs into solution requirements, build AI solutions, integrate them into processes, and interpret results. A recent

survey by Deloitte found that most early adopters of AI face a significant AI skills gap with 68% of companies reporting a moderate-to-extreme skills gap.84 At this early stage “AI builders” are the most sought-after professionals, including software developers and data scientists. Companies with greater experience deploying AI technologies could also face other skills gaps, such as business leaders with the skills to know when AI can be leveraged to solve business problems and change management experts necessary to integrate AI tools into an organization.

Recommendations to support the existing workforce and prepare the future workforce AI’s effects on Virginia’s labor market will present both challenges and opportunities. While the technology has the potential to displace workers in some occupations (customer service, office support, and coding),85 it is expected to change the nature of many other jobs. This reality means that workers will need to adapt, learn new skills, and sometimes retrain entirely to stay connected to the labor market and progress within it. To help incumbent and displaced workers manage these changes, Virginia will need to partner with stakeholders in higher education, K-12, workforce development, and the business community to provide appropriate education and training. Below are nine broad, flexible recommendations for how Virginia can support its workforce in all regions in the age of AI. AI is quickly evolving, and its effects on Virginia’s workforce are difficult to predict. Given this uncertainty, these recommendations should be considered preliminary. 8. Direct the Virginia Board of Workforce Development to consult subject matter experts and work with state partners to regularly assess programs and make recommendations to support AI-impacted workers The Governor should direct the Virginia Board of Workforce Development (VBWD) to consult subject matter experts around AI and the workforce, including top researchers in AI, business, economics, and/or public policy; business and industry representatives; K-12 and higher education representatives; and policymakers. The Board should also work closely with state partners, including the Department of Workforce Development and Advancement (DWDA), the Department of Human Resource Management (DHRM), the State Council of Higher Education for Virginia (SCHEV), the Virginia Community College System (VCCS), the Virginia Department of Education (VDOE), and the Virginia Office of Education Economics (VOEE). These experts and partners should regularly assess the effects of AI on the workforce and annually make recommendations to the Board about how to support workers. Their reviews should include a focus on the unique challenges and opportunities AI will present for workers in rural communities. In the first year, the Board should work with its state partners to identify existing and emerging careers in AI and review available education and training related to AI. This review should include programs that are not branded as AI but include significant AI content. This baseline information will inform the Board’s policy recommendations to the Governor and General Assembly around expanding or enhancing existing offerings and developing new education and training. 9. Develop and enhance AI degree programs, including associate’s degree programs Based on the Board’s recommendations, Virginia should develop new and enhance existing associate’s, bachelor’s, and master’s degree programs in AI. Part of this effort may involve rebranding or clarifying language around existing programs that include AI training. These programs will help build the AI workforce. They will also provide retraining for some workers displaced by AI.

The U.S. Chamber of Commerce Technology Engagement Center recently recommended using community colleges and vocational schools to train workers with AI-related skills,86 but as of 2020, these institutions were being underutilized to train AI talent.87 Virginia is well-positioned to change that. In 2020, the Commonwealth awarded the fifth most associate’s degrees in Computer and Information Science (CIS).88 Virginia should leverage its strength in this area to provide AI training to more students and workers. 10. Develop and enhance other AI programs, such as non-credit programs and career and technical education (CTE) programs in K-12 Virginia should encourage the expansion of other forms of AI training, including non-credit programs at community colleges and CTE provided by public school divisions. Short-term, credential- or skill-based programs are often more accessible to students and workers than degree programs. This type of training can help workers quickly and efficiently shift into new occupations or take on new responsibilities in existing roles. Virginia should also consider developing programming for incumbent workers to help them adapt to changes in their jobs as they happen. 11. Develop, enhance, and adopt AI curricula and AI standards To support the workforce of today and tomorrow, Virginia should develop, enhance, and adopt AI curricula and AI standards in both K-12 and post-secondary education. AI training should not be siloed to dedicated AI programs but integrated into all levels of education. AI concepts can be taught through specific courses and/ or included as standards in existing courses such as computer science. AI curricula and standards should include a focus on how to use AI effectively and responsibly and the risks of using it inappropriately. 12. Prepare current and future teachers to use and teach students about AI It will be especially important for Virginia’s K-12 teachers to understand how to effectively use AI and teach students about the benefits and risks of AI. Virginia should ensure that state teacher education programs incorporate AI concepts into their curricula, and current teachers should receive professional development related to AI. The Commonwealth should also build on current efforts to increase the supply of teachers licensed to teach computer science. 13. Work with SCHEV, VCCS, and post-secondary institutions to recruit and train faculty and instructors Recommendations 8–12 depend on fully staffed AI programs and teacher training programs at Virginia’s postsecondary institutions. To support these efforts, Virginia should work with SCHEV, VCCS, and post-secondary institutions to recruit and train faculty and instructors. These personnel will lead degree and non-degree programs, teach courses, develop curricula, and train teachers. 14. Create pathways through higher education-defense agency partnerships that allow participants to earn a security clearance The cybersecurity industry will continue to grow due to the security challenges posed by generative AI’s capabilities. Additionally, the integration of AI technology in government operations will require security clearances for employees at companies supporting that work. To make Virginia more competitive for companies supporting government efforts in AI, expanding the supply of workers with security clearances will be critical.

15. Develop opportunities for work-based learning around AI skills Work-based learning in the form of internships, externships, and other experiences is a valuable way to learn AI skills and their application in the workplace. Virginia should consider developing these experiences for students and incumbent and dislocated workers. 16. Use existing programs and funds to support displaced workers The Commonwealth already has robust systems and funds in place to support displaced workers. It should leverage and potentially expand these existing resources to support workers displaced by AI. The White House’s recent executive order on AI directed the Secretary of Labor to assess how federal programs, including unemployment insurance and the Workforce Innovation and Opportunity Act (WIOA), could support workers displaced by AI.89 The Commonwealth should build on federal efforts through its implementation and use of federal policy and funds. It should also consider how state programs and funds, including the Workforce Credential Grant and the Get Skilled, Get a Job, Get Ahead (G3) programs can be similarly leveraged. Depending on the extent of worker displacement, Virginia should consider increasing funding for these programs. 17. Develop or adopt an AI course for state workers Virginia should train state workers to use AI effectively and responsibly. The roughly 110,000 state employees account for 3% of the Commonwealth’s labor market.90 Virginia should develop a new AI course or adopt an existing AI resource to train these workers. 18. Work with businesses to develop AI literacy among incumbent workers through existing funding streams or new pilot programs Virginia should work with the business community to ensure that incumbent workers also receive training to use AI tools. While only a small subset of workers may be displaced by AI, all students and workers need to be literate in AI.91 AI literacy is “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.”92 Workers can learn to use AI quickly because it is based in natural language, but they often receive little to no training on how to use AI tools effectively and responsibly.93 To promote AI literacy for incumbent workers, Virginia should consider harnessing existing funding streams, such as GO Virginia and the Virginia Jobs Investment Program (VJIP) or develop new pilot programs to support businesses in their efforts to train workers with AI and encourage innovation in this space. Consideration should be made for prioritizing grants to businesses in rural economies and economically distressed communities. If Virginia develops or adopts an AI literacy course for state workers, it should consider sharing resources with businesses to train private-sector employees.

Conclusion

Conclusion Despite much uncertainty regarding the specific economic impacts of generative AI, early indications demonstrate a potential positive impact for Virginia’s growth prospects; however, special attention should be paid to ensure that both urban and rural Virginia realize the economic benefits induced by AI. The Commonwealth’s nation-leading concentrations of tech talent and technology- and business service-based industries ensure that Virginia is well positioned for the next chapter in technological evolution ushered in by generative AI. While Virginia is in position to benefit economically from the growth of generative AI, maximizing the technology’s economic impact will require early, proactive implementation of programs and policies to cultivate related industries and maintain Virginia’s competitive position. Economic competition between states has growth increasingly fierce in recent years. States have adopted aggressive policies and offered massive incentives, creating a “winner-take-most” environment where specific states dominate in targeted industries. In this competitive environment, achieving leadership in AI will require Virginia’s leaders and policymakers to commit to this objective and implement programs and policies necessary to rise above competitor states. To that end, the recommendations in this report can serve as a starting point for Virginia to accelerate growth in AI-related industries and prepare its workforce for the skills needed to leverage AI tools. Programs and policies should be implemented quickly, with clear metrics for measuring impact, and an openness to adapting the approach when impacts are not realized and as technologies continue to evolve.

Amazon HQ2 rendering, Arlington County

Acknowledgments

Acknowledgments The following organizations and individuals are acknowledged for their time and contributions to this report (in alphabetical order): State Council of Higher Education for Virginia | Peter Blake, Director with Joseph DeFilippo, Alan Edwards, and Emily Salmon University of Virginia Darden School of Business | Prof. Anton Korinek University of Virginia McIntire School of Commerce | Prof. Reza Moustavi University of Virginia School of Engineering and Applied Science | Prof. Madhur Behl Virginia Chamber of Commerce | Emily Webb, Vice President of Education and Workforce Development with Ethan Betterton and Eleni Poulos Virginia Department of Workforce Development and Advancement | Carrie Roth, Director Virginia Department of Education | Marcey Sorensen, Deputy Superintendent of Teaching & Learning and Andrew Armstrong, Assistant Superintendent of Strategic Innovation Virginia Innovation Partnership Corporation | David Ihrie, CTO and Vice President of the Strategic Initiatives Division; Chuck Kirby, Vice President of Smart Communications; Lynn McDaniel, Director of Smart Data Virginia Office of the Secretary of Education | Emily Anne Gullickson, Deputy Secretary Virginia Office of the Secretary of Labor | George Taratsas, Workforce Development Director Virginia Tech Sanghani Center for AI and Data Analysis | Prof. Naren Ramakrishnan, Director; Prof. Chris North, Associate Director; Prof. Danfeng Yao; Brian Mayer, Research Associate

Appendix A — AI Exposure Levels for Selected Skills

Appendix A — AI Exposure Levels for Selected Skills Highest AI exposure: Fundamental changes possible

Medium AI exposure: Mainly capacity improvements

■

Controlling Machines and Processes

■

Analyzing Data or Information

■

Documenting/Recording Information

■

Coordinating the Work and Activities of Others

■

Identifying Objects, Actions, and Events

■

Monitoring Processes, Materials, or Surroundings

■

Performing Administrative Activities

■

Processing Information

■

Scheduling Work and Activities

Some AI exposure: Occasional use ■

■

Communicating with People Outside the Organization Communicating with Supervisors, Peers, or Subordinates

■

Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment Estimating the Quantifiable Characteristics of Products, Events, or Information Evaluating Information to Determine Compliance with Standards

■

Getting Information

■

Guiding, Directing, and Motivating Subordinates

■

Inspecting Equipment, Structures, or Materials

■

Judging the Qualities of Objects, Services, or People

■

Monitoring and Controlling Resources

■

Organizing, Planning, and Prioritizing Work

■

Staffing Organizational Units

■

Training and Teaching Others

■

Updating and Using Relevant Knowledge

■

Working with Computers

Limited or no AI exposure: Tasks without clear AI application ■

Assisting and Caring for Others

■

Coaching and Developing Others

■

Developing and Building Teams

■

Establishing and Maintaining Interpersonal Relationships

■

Developing Objectives and Strategies

■

Interpreting the Meaning of Information for Others

■

Making Decisions and Solving Problems

Handling and Moving Objects

■

Performing General Physical Activities

■

Repairing and Maintaining Electronic Equipment

■

Repairing and Maintaining Mechanical Equipment

■

Resolving Conflicts and Negotiating with Others

■

Operating Vehicles, Mechanized Devices, or Equipment Performing for or Working Directly with the Public

■

Providing Consultation and Advice to Others

■

Selling or Influencing Others

■

Thinking Creatively

Appendix B — Open Federal Grant Opportunities for AI R&D and Education

Appendix B — Open Federal Grant Opportunities for AI R&D and Education The following NSF grants are currently available to support AI innovation and develop education programs (note: this list is not exhaustive): National AI Research Institutes Funding is still available for entities (institutes for higher learning and non-profits) to establish a National AI Intelligence Research Institute. Applicants for the 2025 awards will need to apply based on two themes: AI for Discovery in Materials Research and Strengthening AI. Each theme will see two awards at $20 million each. The award will be distributed over a four-to-five-year timeline, averaging $4 million a year.96 NSF Regional Innovation Engines (NSF Engines) This program was launched in 2023 to expand U.S. innovation capacity by investing in translational research and technology development in designated areas that align with national and economic security objectives. An NSF Engine brings together a coalition of university-based research expertise along with partners from private industry, non-profits, and other allied regional organizations. The CHIPS Act, which created this new program, specifies that NSF Engines should address several key technology and challenge areas, which includes artificial intelligence, high-performance computing, semiconductors, quantum technology, and bioengineering. The maximum award is a 10-year grant of up to $160 million. Applications for the first phase of the program closed in early 2023 but are expected to open again in early 2025. Enabling Partnerships to Increase Innovation Capacity This program is aimed at establishing inclusive innovation ecosystems at higher education institutions that historically are not major drivers of innovation. The purpose of the program is to provide capacity-building support to Minority-Serving Institutions (MSI), Predominantly Undergraduate Institutions (PUI), and two-year institutions to develop a network of partners that can collaborate on use-inspired research on a range of priorities, including AI. Higher education institutions can leverage this funding to develop an innovation network that would advance eligibility for other NSF grants.97 Computer Science for All This initiative aims to provide all students, statewide, with computer science and computational skills and education. The program specifically targets teachers and school districts to support the creation of computer science pathways across all grades. The program is applicable for research recipients, as well as small, medium, and large research-practitioner partnerships.98 Scholarships in Science, Technology, Engineering, and Mathematics Program (SSTEMP) SSTEMP targets social and education mobility for low-income students to access STEM education and skills, including AI related skills, with the goal of ensuring more low-income students graduate with a STEM degree. Higher education institutions are eligible for up to $5 million in funding.99

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