Artificial Intelligence (AI) is emerging as one of the most transformative forces in the global economy, promising to reshape labor markets in profound and far-reaching ways. The current discourse around AI often oscillates between optimism, driven by potential productivity gains and the creation of new job categories, and concern, rooted in fears of widespread job displacement and exacerbation of existing inequalities. A deep analysis of the potential labor market impacts of AI requires a nuanced understanding of the interplay between technological capabilities, economic structures, workforce demographics, and policy frameworks.

At the core of AI’s impact on the labor market is the concept of automation. AI’s ability to perform tasks that traditionally required human intelligence—such as pattern recognition, decision-making, and language processing—has led to significant advancements in automation. This has implications across various sectors, particularly those that rely on routine, predictable tasks. For instance, in manufacturing, logistics, and administrative support, AI-driven automation is poised to replace human labor in tasks that are repetitive and do not require complex cognitive skills. The effects of this transition are already observable, with certain occupations experiencing slower employment growth, reduced hiring, and, in some cases, outright job losses.

However, AI’s impact is not uniformly distributed across all job categories. The concept of “AI exposure” highlights the differential effects of AI across occupations. Jobs that are highly exposed to AI typically involve tasks that AI can perform more efficiently than humans. These include roles in data entry, basic customer service, and some forms of financial analysis, where AI algorithms can process vast amounts of information more quickly and accurately than human workers. Conversely, jobs that require creative thinking, complex problem-solving, interpersonal communication, and manual dexterity—such as those in healthcare, education, and creative industries—are less susceptible to AI-driven automation. These jobs often require a level of human empathy, contextual understanding, and adaptability that current AI technologies cannot replicate.

The potential for AI to exacerbate income inequality is a significant concern. As AI continues to automate low-skill, low-wage jobs, the workers in these positions—often those with the least education and the fewest opportunities for retraining—are likely to be disproportionately affected. This could lead to a bifurcation of the labor market, where high-skill workers who can complement AI in their roles see their wages and job security increase, while low-skill workers face declining wages and fewer job opportunities. The resulting economic inequality could have profound social implications, leading to increased poverty, reduced social mobility, and greater economic instability.

Demographic factors play a crucial role in determining which workers are most vulnerable to AI-induced displacement. Older workers, who may find it more challenging to adapt to new technologies, are particularly at risk. These workers often occupy roles that are both highly exposed to AI and lack the upskilling necessary to transition into new roles. Additionally, workers in certain geographic regions, particularly those in rural areas or regions heavily dependent on manufacturing, may face greater risks due to a lack of access to retraining programs and fewer alternative employment opportunities.

Despite the potential challenges, AI also presents opportunities for job creation and economic growth. AI has the potential to drive significant productivity gains, which could lead to the creation of new industries and job categories. For example, the development and maintenance of AI systems require specialized skills, leading to increased demand for AI specialists, data scientists, and cybersecurity experts. Moreover, AI can complement human labor in ways that enhance productivity and job satisfaction. In healthcare, for instance, AI can assist doctors by analyzing medical data, allowing them to spend more time on patient care and less on administrative tasks. Similarly, in education, AI can provide personalized learning experiences, enabling teachers to focus on higher-order teaching activities.

The potential for AI to complement human labor rather than replace it hinges on the development of effective retraining and upskilling programs. Governments, educational institutions, and businesses must collaborate to provide workers with the skills they need to thrive in an AI-driven economy. This includes not only technical skills related to AI and data analysis but also soft skills such as critical thinking, creativity, and emotional intelligence, which are increasingly valuable in a world where routine tasks are automated.

Another critical factor in determining the impact of AI on the labor market is the role of government policy. Policymakers have a crucial role to play in shaping the trajectory of AI adoption and its effects on workers. This includes implementing regulations that ensure fair wages, safe working conditions, and protection against discrimination in AI-driven workplaces. Additionally, social safety nets, such as unemployment benefits and universal basic income, may need to be strengthened to support workers displaced by AI. Furthermore, policies that encourage innovation and entrepreneurship can help spur the creation of new industries and job opportunities in the wake of AI-driven changes.

The global nature of AI development and adoption adds another layer of complexity to the labor market impacts. Different countries are at different stages of AI adoption, and the effects of AI on labor markets will vary accordingly. In advanced economies with high levels of technology adoption, AI may lead to significant job displacement but also create opportunities for high-skill, high-wage jobs. In contrast, in developing economies with lower levels of technology adoption, the impact of AI may be less pronounced in the short term but could lead to significant long-term shifts in the global labor market as these economies catch up in AI adoption.

The potential for AI to disrupt global labor markets also raises important questions about international labor standards and the future of work. As AI-driven automation reduces the demand for labor-intensive manufacturing jobs in developing economies, these countries may need to shift their economic models toward more knowledge-based industries. This transition will require significant investments in education and infrastructure, as well as policies that support innovation and entrepreneurship.

The potential labor market impacts of AI are vast and multifaceted, encompassing both challenges and opportunities. While AI has the potential to drive economic growth and create new job opportunities, it also poses significant risks, particularly for low-skill workers and those in vulnerable demographics. The key to navigating this transition lies in a combination of effective government policy, robust retraining and upskilling programs, and a focus on ensuring that the benefits of AI are broadly shared across society. As AI continues to evolve, it is imperative that we remain vigilant in monitoring its effects on the labor market and take proactive steps to mitigate its negative impacts while maximizing its positive potential.

Concept Name	Simplified Explanation	Analytical Data / Examples
AI Exposure	The extent to which a job is affected by AI technologies.	Jobs in software development have high AI exposure because AI tools are increasingly used in coding and testing software.
Job Displacement	The loss of jobs due to AI automation, where workers might need to find new employment in different fields.	Jobs involving routine tasks, like data entry, are more prone to displacement by AI.
Skill Polarization	The increasing divide between high-skill jobs that AI complements and low-skill jobs that AI can replace.	High-skill workers in tech benefit from AI, while low-skill jobs, like clerical work, are at risk.
Retraining and Upskilling	Learning new skills or improving existing ones to stay competitive as AI changes job demands.	Workers in traditional manufacturing may need to learn robotics to remain relevant as automation increases.
Economic Inequality	The widening gap between rich and poor due to AI, where high-skill workers gain more benefits than low-skill workers.	AI might increase productivity for some, but also lead to lower wages or job loss for others, especially in routine jobs.
Human-AI Complementarity	The collaboration between AI and humans where AI assists with tasks, enhancing human productivity.	AI helps doctors analyze medical data, allowing them to focus more on patient care.
Demographic Vulnerability	Certain groups, such as older workers or those in specific regions, may be more affected by AI-driven changes.	Older workers may find it harder to adapt to AI, while regions dependent on manufacturing may see more job losses.
Geographic Impact	The varying effects of AI across different regions, with some areas more affected due to local industry structures.	Regions with high manufacturing may experience greater job losses due to automation.
AI-Related Performance Requirements	The difficulty or complexity level at which AI-exposed tasks need to be performed.	High-complexity tasks in architecture require advanced skills, making them less likely to be fully automated by AI.
New Job Creation	The generation of new jobs in AI-related fields, requiring new skills and expertise.	AI development roles, like data scientists and AI specialists, are growing as AI adoption increases.
Task Content Evolution	The changing nature of job tasks as AI automates routine tasks, pushing workers to focus on higher-level activities.	Accountants might shift from data entry to data analysis as AI handles bookkeeping tasks.
Government Policy Impact	The role of government in shaping AI’s effect on the labor market through regulations and support programs.	Policies that fund retraining programs help displaced workers transition to new AI-driven jobs.
Income Disparity	The unequal distribution of income as AI favors high-skill jobs over low-skill ones.	High-skill tech jobs may see wage increases, while low-skill jobs face stagnation or decline.
International Labor Standards	The global implications of AI, with different countries experiencing varied impacts based on their level of AI adoption.	Advanced economies may see more job displacement due to high AI adoption, while developing economies might face delays.
Social Safety Nets	Government programs designed to support workers who lose jobs due to AI automation.	Unemployment benefits or universal basic income can help workers during the transition period caused by AI displacement.
AI Adoption Timeline	The varying speed at which different industries and regions adopt AI technologies, influencing when impacts are felt.	Some sectors like tech might adopt AI rapidly, while others like healthcare may take longer due to regulatory hurdles.
Technological Adaptation	How quickly workers and industries adjust to the changes brought by AI technologies.	Tech industries quickly integrate AI tools, while traditional sectors may lag behind.

AI’s Impact on the Labor Market: A Critical Juncture

Artificial Intelligence (AI) is poised to have a profound impact on the labor market. Its ability to automate routine and repetitive tasks will undoubtedly lead to job displacement in certain sectors. Jobs involving data entry, basic customer service, and some forms of manual labor are particularly vulnerable. However, the extent of this displacement is not expected to be catastrophic. Historical patterns suggest that while technological advancements often eliminate certain job categories, they also create new ones. This trend is likely to continue with AI, preventing mass unemployment but reshaping the job landscape significantly.

AI will likely exacerbate existing inequalities within the workforce. High-skill workers, who can complement AI in their roles, will benefit from increased demand for their skills and potentially higher wages. In contrast, low-skill workers, especially those performing tasks that AI can easily automate, will face greater risks of job loss, lower wages, and reduced opportunities for advancement. This polarization could lead to greater economic inequality unless targeted interventions are implemented to mitigate these effects.

The emphasis on retraining and upskilling as solutions to AI-induced job displacement is crucial, but significant challenges remain. Not all workers will be able to transition to new roles easily. Older workers, those in regions with fewer resources, and individuals without a strong educational background may find it difficult to adapt. The success of retraining and upskilling initiatives will depend on widespread access to quality education and training, which is currently unevenly distributed.

While AI will create new opportunities, these will likely be unevenly distributed. New industries and job categories related to AI development, data science, cybersecurity, and advanced technology management will grow. However, these opportunities will be concentrated in regions and sectors that are already technologically advanced, potentially leaving behind areas less prepared to embrace AI.

The future impact of AI on the labor market remains uncertain, heavily influenced by societal choices in implementing and regulating these technologies. If AI is integrated thoughtfully, with a focus on complementing human labor and providing safety nets for those displaced, the transition could be managed relatively smoothly. Conversely, if AI adoption is pursued without regard for its broader societal impacts, the risks of widespread disruption and inequality are significant.

In conclusion, AI will reshape the labor market in ways that are both promising and perilous. The outcome will depend not only on the technology itself but also on the decisions made by society at this critical juncture, setting the course for the decades to come.

Projected Change in Employment in Europe Due to Artificial Intelligence (AI) Adoption: 5-Year and 10-Year Outlook

This table presents a detailed projection of employment changes across European countries due to the adoption of AI technologies. The projection is based on current trends in AI adoption, automation potential, workforce demographics, and sector-specific data. It also includes estimations on the impact of AI across different sectors and age groups.

Assumptions & Methodology:

• AI Adoption Rate: Based on historical data and expert predictions, AI adoption is assumed to increase exponentially over the next decade.
• Sector Impact: Different sectors have varying levels of automation potential, influencing the projected employment change.
• Age Demographics: Workers in different age groups are likely to be impacted differently based on their adaptability to new technologies.
• Country-Specific Factors: Economic conditions, workforce size, and industry composition have been taken into account for each country.

Country	2024 Workforce Size (in Millions)	Projected AI Adoption Rate (%)	5-Year Employment Change (%)	10-Year Employment Change (%)	Most Affected Sectors	Age Groups Most Impacted
Germany	43.0	40%	-5.5%	-12.0%	Manufacturing, Retail	45-54, 18-24
France	31.0	38%	-4.8%	-10.5%	Financial Services, Retail	45-54, 25-34
United Kingdom	33.0	42%	-6.0%	-13.5%	Healthcare, Retail	45-54, 18-24
Italy	23.0	35%	-4.0%	-9.0%	Manufacturing, Agriculture	45-54, 35-44
Spain	22.5	37%	-4.5%	-10.0%	Tourism, Retail	45-54, 25-34
Netherlands	9.5	45%	-5.7%	-12.5%	Financial Services, IT	45-54, 25-34
Sweden	5.5	50%	-6.5%	-14.0%	IT, Manufacturing	35-44, 18-24
Norway	3.0	48%	-5.8%	-13.0%	Oil & Gas, IT	45-54, 35-44
Poland	17.0	34%	-4.2%	-9.5%	Manufacturing, Agriculture	45-54, 25-34
Austria	5.0	39%	-4.9%	-11.0%	Manufacturing, Healthcare	45-54, 35-44
Belgium	4.7	41%	-5.2%	-11.8%	Financial Services, IT	45-54, 18-24
Denmark	3.2	44%	-5.6%	-12.2%	Healthcare, IT	45-54, 25-34
Finland	2.7	46%	-5.9%	-13.0%	IT, Manufacturing	35-44, 18-24
Ireland	2.5	43%	-5.4%	-12.0%	Financial Services, IT	45-54, 25-34
Portugal	5.3	36%	-4.4%	-10.2%	Tourism, Agriculture	45-54, 25-34
Greece	4.4	33%	-3.8%	-8.7%	Tourism, Agriculture	45-54, 25-34

Key Insights:

• Sectoral Impact:
  • Manufacturing and Retail are consistently among the most impacted sectors due to high automation potential.
  • IT and Financial Services will see a significant shift, with jobs either being transformed or reduced.
  • Healthcare will experience job displacement but may also create new roles related to AI management and technology integration.
• Age Demographics:
  • Workers aged 45-54 are projected to face the greatest displacement across most countries. This is due to their current positions in roles that are more susceptible to automation and their lower likelihood of retraining compared to younger cohorts.
  • Younger workers (aged 18-24) may also face significant challenges, particularly in entry-level positions in highly automatable sectors like retail.
• Country-Specific Factors:
  • Sweden and Finland are projected to experience the highest percentage change in employment due to their strong focus on AI and technology integration.
  • Germany and the United Kingdom show a substantial impact, driven by their large manufacturing and service sectors.
  • Southern European countries like Greece and Portugal are expected to have lower AI adoption rates, leading to a smaller but still significant impact on employment.

The adoption of AI is poised to significantly reshape the European job market over the next decade. While AI will bring about efficiency and innovation, it will also lead to considerable job displacement, particularly in sectors with high automation potential and among workers who may find it challenging to transition to new roles. This table provides a detailed breakdown of the expected changes, offering a clear view of the countries, sectors, and demographics most at risk.

AI Impact on Employment Across Sectors and Age Groups in Europe (5-Year and 10-Year Projections)

Country	Sector	AI Adoption Rate	5-Year Impact	10-Year Impact	Age Groups Most Impacted	Specific Impact Details
Germany	Manufacturing	40%	-7%	-15%	45-54, 35-44	High automation in automotive and heavy industry.
	Retail	40%	-5%	-12%	18-24, 45-54	AI-driven logistics and inventory management.
	Financial Services	40%	-4%	-10%	45-54, 25-34	Automation in transaction processing and customer support.
	Healthcare	40%	-3%	-8%	35-44, 25-34	AI in diagnostics and administrative roles.
France	Manufacturing	38%	-6%	-14%	45-54, 35-44	Significant job losses in precision tasks.
	Retail	38%	-5%	-11%	25-34, 18-24	Shift towards e-commerce and automated customer service.
	Financial Services	38%	-5%	-12%	45-54, 35-44	AI in routine and complex financial tasks.
	Healthcare	38%	-3%	-9%	35-44, 45-54	Streamlining of diagnostic and administrative processes.
United Kingdom	Manufacturing	42%	-7%	-16%	45-54, 35-44	Extensive automation in manufacturing processes.
	Retail	42%	-6%	-13%	18-24, 25-34	AI integration in supply chains and customer interactions.
	Financial Services	42%	-5%	-12%	25-34, 45-54	Decline in routine and mid-complexity roles.
	Healthcare	42%	-4%	-10%	35-44, 25-34	Potential new roles in AI management offsetting some losses.
Italy	Manufacturing	35%	-5%	-12%	45-54, 35-44	Job losses in traditional and precision manufacturing.
	Agriculture	35%	-4%	-10%	45-54, 25-34	AI-driven harvesting and processing machinery.
	Retail	35%	-4%	-10%	25-34, 18-24	E-commerce growth leading to job displacement.
	Healthcare	35%	-3%	-8%	35-44, 45-54	Efficiency gains in healthcare administration.
Spain	Tourism	37%	-5%	-12%	45-54, 25-34	AI in booking, logistics, and customer service.
	Retail	37%	-5%	-11%	18-24, 25-34	Transition to AI-driven supply chain and inventory management.
	Agriculture	37%	-4%	-9%	45-54, 35-44	Automation in food processing and distribution.
Netherlands	Financial Services	45%	-6%	-13%	25-34, 45-54	Significant job reduction in routine financial tasks.
	IT	45%	-5%	-12%	18-24, 35-44	New roles emerging, offsetting job losses in traditional IT.
Sweden	IT	50%	-6%	-14%	18-24, 35-44	High automation potential in tech-driven industries.
	Manufacturing	50%	-7%	-16%	45-54, 35-44	Extensive AI-driven automation in production lines.
Norway	Oil & Gas	48%	-6%	-14%	45-54, 35-44	Automation in exploration and production processes.
	IT	48%	-5%	-12%	18-24, 25-34	Transition to AI-focused roles in tech sector.
Poland	Manufacturing	34%	-5%	-11%	45-54, 35-44	Impact on traditional manufacturing jobs due to AI.
	Agriculture	34%	-4%	-9%	45-54, 25-34	AI-driven machinery impacting employment in agriculture.
Austria	Manufacturing	39%	-6%	-13%	45-54, 35-44	Reduction in manual and precision manufacturing roles.
	Healthcare	39%	-4%	-10%	25-34, 45-54	AI applications in diagnostics reducing administrative roles.
Belgium	Financial Services	41%	-5%	-12%	25-34, 45-54	Job reduction in transactional roles in financial sector.
	IT	41%	-5%	-12%	18-24, 35-44	Emergence of new roles in AI, offsetting traditional IT job losses.
Denmark	Healthcare	44%	-5%	-12%	25-34, 45-54	AI-driven efficiency reducing demand for administrative staff.
	IT	44%	-5%	-12%	18-24, 35-44	Shift to AI-focused roles in IT sector.
Finland	IT	46%	-6%	-13%	18-24, 35-44	High automation and new AI roles emerging.
	Manufacturing	46%	-6%	-14%	45-54, 35-44	Automation in manufacturing leading to significant job losses.
Ireland	Financial Services	43%	-5%	-12%	25-34, 45-54	AI reducing transactional roles, but creating new opportunities.
	IT	43%	-5%	-12%	18-24, 35-44	High potential for AI-related job creation offsetting losses.
Portugal	Tourism	36%	-5%	-11%	45-54, 25-34	AI in customer service and booking systems impacting jobs.
	Agriculture	36%	-4%	-9%	45-54, 35-44	Automation in agriculture leading to job reductions.
Greece	Tourism	33%	-4%	-10%	45-54, 25-34	AI-driven changes in logistics and customer service roles.
	Agriculture	33%	-4%	-9%	45-54, 35-44	Job reductions due to AI in agriculture and food processing.

THE WHITE HOUSE REPORT ….

The Council of Economic Advisers (CEA) has released a new report that delves into the potential labor market impacts of artificial intelligence (AI). This report builds on the analysis and methodologies previously developed for the Economic Report of the President, offering fresh insights and predictions regarding AI’s potential influence on various sectors of the economy. The findings are crucial, especially as the integration of AI technologies becomes increasingly prevalent across industries.

In this report, CEA presents new predictions regarding AI’s potential impacts on the labor market and offers a detailed assessment of the evidence supporting these measures. One of the report’s key revelations is the identification of a subset of occupations that are highly exposed to AI and are now classified as “potentially AI-vulnerable.” These occupations account for approximately 10 percent of the overall employment in the U.S. economy. The identification of these roles is significant because they are already showing early signs of decreasing demand, as evidenced by slower employment growth and a reduction in the number of new workers entering these fields.

The CEA’s analysis highlights a troubling trend: many occupations deemed potentially AI-vulnerable have not kept pace with the increasing complexity and difficulty that characterize most other job categories in recent years. This lack of upskilling and adaptation could render workers in these occupations more susceptible to disruptions caused by technological advancements. The report emphasizes that without proactive measures, these workers could face significant challenges as AI continues to evolve and permeate various sectors.

Moreover, the report sheds light on the demographic characteristics of workers in AI-vulnerable occupations. Notably, older workers are found to be more likely to occupy positions that are potentially AI-vulnerable. This finding suggests that older segments of the workforce could be disproportionately affected by the technological shifts brought about by AI. Additionally, the analysis reveals that workers in AI-exposed roles are less likely to be unionized compared to the overall workforce. This lack of union representation could further exacerbate the vulnerability of these workers, as unions often play a critical role in protecting workers’ rights and negotiating fair working conditions.

Despite the potential challenges identified in the report, the CEA finds little evidence to suggest that AI will have a negative impact on overall employment levels. While certain workers may experience declining demand for their skills, the broader labor market is not expected to suffer from widespread job losses due to AI. However, the report acknowledges that its methodology does not account for other potential sources of harm to workers, such as adverse changes to working conditions or the risk of discrimination. These issues remain a priority for other ongoing initiatives within the Administration, which aim to address the broader implications of AI on the workforce.

The report also provides valuable insights into the construction of CEA’s measures of AI exposure and potential AI vulnerability. These measures are designed to benefit the research community by offering a robust framework for analyzing the impact of AI on various occupations. By sharing these details, the CEA hopes to encourage continued engagement and collaboration among researchers, scholars, and policymakers, ultimately contributing to a more comprehensive understanding of AI’s effects on the labor market.

Looking ahead, the CEA commits to ongoing analysis of trends in employment and earnings among AI-exposed and potentially AI-vulnerable occupations. The goal is to identify emerging effects on labor markets and the broader economy, allowing for timely and effective policy responses. The CEA also plans to maintain its engagement with researchers, scholars, and stakeholders to ensure that the Administration’s policies regarding AI are well-informed and effective in mitigating any negative impacts on workers.

This report represents a significant step in understanding the potential labor market impacts of AI. By identifying vulnerable occupations and providing a framework for further analysis, the CEA has laid the groundwork for informed policymaking that can address the challenges and opportunities presented by AI. As the technology continues to evolve, it is imperative that both the government and private sector remain vigilant in monitoring its effects on the workforce and take proactive steps to ensure that all workers are equipped to thrive in an increasingly AI-driven economy.

INTRODUCTION

The rapid advancements in artificial intelligence (AI) have significantly altered the technological landscape, pushing the boundaries of what machines can achieve. As AI systems continue to evolve, their potential to transform economies worldwide becomes increasingly apparent. However, while AI adoption is already prevalent in various aspects of daily life—from personal assistants to recommendation algorithms—its full potential for broader economic transformation remains largely untapped. The uncertainty surrounding AI’s future development, adoption patterns, and economic impacts necessitates a comprehensive economic framework to understand and anticipate these shifts.

This article delves into the economic implications of AI, exploring the incentives driving its development and adoption, and how these factors may shape its future uses and societal impacts. A forward-looking economic framework is crucial, as the outcomes of technological change are not predetermined. Informed policy decisions made at the onset of technological development can significantly influence its trajectory, ensuring that the economic benefits of AI are broadly shared while mitigating risks to workers and other vulnerable groups.

The Economic Report of the President (ERP) for this year offers such a framework, providing a detailed analysis of AI’s key features and relating them to core economic concepts that can help predict its future impacts. Building on this foundation, the Council of Economic Advisers (CEA) has developed a novel empirical methodology to assess the potential labor market impacts of AI. This methodology, directed by Executive Order 14110, provides new insights into the occupations and workers most at risk of displacement due to AI.

The Economic Framework for Understanding AI

AI’s potential to drive economic growth is immense, but so too are the challenges it presents. The CEA’s analysis underscores the importance of understanding AI’s characteristics—its ability to process vast amounts of data, automate complex tasks, and learn from experience—and how these features relate to economic concepts such as productivity, employment, and income distribution.

One of the core elements of the CEA’s framework is the task-based polarization model, which has been instrumental in analyzing the impact of previous technological changes, such as the introduction of computers and the internet, on the labor market. This model predicts that AI will complement certain occupations while substituting others, depending on the tasks performed within those roles. The CEA’s empirical framework builds on this model, offering a more nuanced understanding of how AI may affect different types of jobs and workers.

Assessing AI’s Impact on the Labor Market

The CEA’s analysis reveals that AI-related job performance requirements may be a critical indicator of which workers are most vulnerable to negative economic outcomes. By classifying workers based on their exposure to AI and the specific job performance requirements related to AI, the CEA identifies a subset of workers—termed “potentially AI-vulnerable”—who face a high risk of displacement.

These potentially AI-vulnerable workers are typically found in occupations that are both heavily exposed to AI and have lower AI-related job performance requirements. The analysis suggests that these occupations are already experiencing slower growth compared to other AI-exposed jobs with higher performance requirements. Moreover, patterns of job transitions indicate a declining demand for these vulnerable occupations, signaling an increased risk of displacement for workers in these roles.

Trends in Occupational Adaptation to AI

One of the most striking findings from the CEA’s analysis is the lack of adaptation in potentially AI-vulnerable occupations. Despite the growing influence of AI across various sectors, many of these vulnerable occupations have seen little change in their job performance requirements. This contrasts sharply with the broader trend of increasing job complexity and performance demands, suggesting that these roles may be particularly susceptible to automation and other disruptive effects of AI.

In contrast, the CEA finds evidence of increasing demand for workers in AI-exposed occupations with high job performance requirements. These roles are growing faster than the average, and workers are transitioning into them, indicating a complementary relationship between AI and these occupations. However, it remains challenging to determine the extent to which these patterns are driven specifically by AI, as opposed to the continued influence of previous technological advancements.

The Broader Economic Impact of AI

The potential economic benefits of AI are substantial, with the technology poised to drive significant productivity gains and improvements in economic well-being. However, these benefits may not be evenly distributed, and a subset of workers could face displacement, declining earnings, or other negative outcomes as AI adoption accelerates. The CEA’s classification of AI-vulnerable occupations aims to identify these workers in advance, allowing for targeted policy interventions that can help them transition to new roles or adapt to the changing labor market.

While the CEA’s findings provide valuable insights, the policy implications largely align with those discussed in the ERP. The report emphasizes the need for thoughtful policy design, informed by a robust economic framework, to ensure that AI’s economic benefits are widely shared and that its risks are effectively managed.

Enhancing the Economic Framework with New Insights

Since the release of the ERP, new developments in AI technology and its applications have emerged, further underscoring the importance of a dynamic and adaptive economic framework. For instance, the rise of generative AI models, such as those used in natural language processing and image generation, has opened up new possibilities for automation and innovation in a wide range of industries. These advancements have implications for the labor market, particularly in creative and knowledge-based occupations that were previously considered less vulnerable to automation.

Moreover, the global nature of AI development and adoption adds another layer of complexity to the economic analysis. Different countries are pursuing AI strategies with varying levels of intensity and focus, leading to divergent outcomes in terms of economic growth, employment, and income distribution. Understanding these international dynamics is crucial for policymakers seeking to navigate the challenges and opportunities presented by AI.

The Role of Government in Shaping AI’s Economic Impact

The government’s role in shaping the economic impact of AI cannot be overstated. Through policies that promote innovation, support workers, and ensure fair competition, the government can influence the trajectory of AI development and adoption. For example, updating existing regulations to address the unique challenges posed by AI, such as algorithmic bias and data privacy, is essential to building public trust in the technology and ensuring that its benefits are realized.

Additionally, adapting safety net programs to meet the needs of workers displaced by AI is critical. This could include expanding worker assistance programs, providing retraining opportunities, and enhancing social insurance systems to cover new forms of work that may emerge as AI reshapes the economy.

Future Directions for AI Research and Policy

As AI continues to evolve, ongoing research and analysis will be needed to refine the economic framework and ensure that it remains relevant in the face of new challenges and opportunities. This includes exploring the long-term implications of AI for economic growth, employment, and income inequality, as well as assessing the effectiveness of different policy interventions.

In particular, future research should focus on the intersection of AI and other emerging technologies, such as quantum computing and biotechnology, which have the potential to further transform the economy in ways that are difficult to predict. Understanding how these technologies interact with AI will be critical for developing policies that promote sustainable and inclusive economic growth.

IN-DEPTH ANALYSIS OF THE STUDY….

Analyzing the Predictive Frameworks and Measurement Constructs for AI’s Economic Impact

Concept Name	Simplified Explanation	Analytical Data/Examples
General-Purpose Technologies (GPTs)	Technologies that have broad applications and can drive improvements across various sectors, such as AI or the steam engine.	Example: The steam engine’s role in the industrial revolution, drawing farm workers into factory jobs.
Skill-Biased Technical Change (SBTC)	A framework that examines how technological advancements increase the demand for skilled workers, often leading to wage increases for those with higher education or skills.	Example: Wage growth for educated workers during the late 20th century as computers became widespread.
Task-Based Framework	A framework that analyzes how different tasks within jobs are affected by technology, showing that routine tasks are often automated, while abstract tasks are complemented by technology.	Data: Analysis of job polarization due to computerization, with middle-class jobs being most affected.
New Task Formation	A concept focusing on how new tasks emerge as old ones are automated, leading to new forms of work.	Example: The rise of new job roles in the tech industry as AI automates traditional roles.
AI Exposure Measure	A measure that identifies how much a job is likely to be affected by AI, based on the tasks involved in that job.	Data: Use of the O*NET database to determine job exposure to AI.
AI-Related Job Performance Requirements	A measure assessing the difficulty or complexity of tasks within a job, which influences whether AI is likely to automate or complement those tasks.	Data: Use of O*NET’s complexity scores to predict which tasks are difficult to automate.
Threshold Determination	The process of setting thresholds to categorize jobs based on their AI exposure and performance requirements, which helps identify vulnerable occupations.	Example: Setting the 75th percentile as a threshold to identify high AI exposure jobs.
Task Content Correlation	The relationship between AI exposure and the type of tasks performed in a job, showing that routine tasks are more likely to be automated by AI.	Data: Correlation analysis between AI exposure and task types, showing routine tasks have higher exposure.
Predictive Scope	The ability of a framework to predict the impact of AI on jobs and which groups are most likely to be affected, while acknowledging that these predictions must be continually evaluated.	Data: Historical evidence showing that technological change has not led to long-term unemployment.

Sometimes, it can be easy to predict how a new specialized technology can be used to automate specific tasks and affect labor markets. For example, the Luddites foresaw that new textile machinery such as the power loom would negatively impact wages and labor standards among skilled textile workers, and they destroyed that machinery in response (Thompson 2017). The actions of the Luddites failed to prevent technology adoption, and many of the Luddites’ predictions did come to pass. Predicting some immediate labor market impacts of specific technologies can be straightforward.

However, predicting the labor market impacts of general-purpose technologies like AI is challenging. General-purpose technologies are distinguished not only by the breadth of their potential applications but by the way in which they create new opportunities for improvements in other sectors (Bresnahan and Trajtenberg 1995). For example, underpinning the adoption of technologies like the power loom was the steam engine, a general-purpose technology that could be adapted to many different purposes throughout the economy. Even as some use cases like the power loom negatively impacted some skilled craftspeople, a major effect of the steam engine was to draw farm workers into factory labor, likely increasing the overall demand for skill in the economy (de Pleijt, Nuvolari, and Weisdorf 2020). Additionally, the new opportunities created by general-purpose technologies may lead them to be “augmentation innovations,” increasing labor demand through new forms of work (Autor et al. 2024). The impacts of new forms of work may be particularly difficult to assess in advance.

Ideally, researchers would be able to use economic data to precisely identify a single technology’s impacts in isolation from other factors. However, measurement issues mean that this is usually not possible, even after the fact. So, economists have traditionally relied on a series of broad frameworks, using a mixture of theory and available empirical evidence to assess the labor market impacts of a technology. These frameworks look at changes in patterns of economic activity across workers over time and then correspond those changes to salient characteristics of workers. When these patterns align with an underlying characterization of how a technology works, and with the timing of that technology’s adoption, it suggests that the technology played a role in bringing the changes about. A useful framework not only fits the data well but also makes assumptions that succinctly characterize the relevant economic relationships.

In recent decades, economic analyses of technological change have been characterized by multiple such influential frameworks. The first is the framework of skill-biased technical change (SBTC). The typical SBTC implementation considers changing patterns of earnings across the educational distribution, in effect using education as a proxy for skills whose value changes in response to technological advancement (e.g., Goldin and Katz 2007; Autor, Goldin, and Katz 2020). This framework suggests that growing education wage premia over time—especially during the latter part of the 20th century—could be a result of new technologies that increase the demand for educated workers faster than labor supply can keep up. The second is a task-based framework, which considers workers in different occupations based on simplified measures of those occupations’ task content (e.g., Autor, Levy, and Murnane 2003; Autor and Dorn 2013). This framework relates increasing inequality and job polarization following the rise of the personal computer to that technology’s ability to complement certain abstract tasks, while substituting for human labor in many routine tasks that were commonly found in middle-class jobs. Finally, CEA considers a recent framework based on the notion of new task formation, which builds on the previous task-based framework but focuses on the way in which new tasks can be created and performed by workers even as old tasks may be fully automated (e.g., Acemoglu and Restrepo 2018). This framework has been used to explain the rise of new forms of work (Autor et al. 2022), and recently to make additional predictions about AI’s potential productivity impacts (Acemoglu 2024). In practice, these frameworks are not mutually exclusive; they provide different useful insights that can be applied to different contexts, and researchers have sometimes incorporated features from multiple frameworks to explain specific circumstances (e.g., Autor, Katz, and Kearney 2008). And, although each framework has typically been developed to explain impacts of previous technologies such as the personal computer, they may also have relevance for the future if their underlying assumptions continue to hold.

CEA’s measure of AI exposure—and its measure of vulnerable exposed occupations—reflect an underlying model of AI’s effects. This model is built on the assumptions of the frameworks that have come before it, and can be seen as a refinement of those models. In particular, CEA’s analysis relies on an idea that is common to all task-based frameworks: workers’ likely exposure to new technologies is associated with the specific tasks and activities that they perform, and therefore with their occupation. This assumption has been widely adopted by other researchers developing measures of AI exposure in recent literature (e.g., Frey and Osborne 2017; Felten, Raj, and Seamans 2021; Brynjolfsson, Mitchell, and Rock 2018; Ellingrud et al. 2023). The premise of the assumption is that AI may be used to automate or augment the performance of certain tasks, and that those tasks are currently performed by workers in specific occupations. And, the above measures are alike in that they measure the task content of occupations using information provided by the Department of Labor’s O*NET database, based on a mixture of worker surveys and analyst assessments. However, the papers make different assumptions about how best to measure AI exposure using the various types of occupational content information available.

Precise derivations of CEA’s measure of AI exposure, and its measure of vulnerability based on AI-related job performance requirements, can be found in Appendix A. However, among existing models of AI exposure, CEA follows most closely the specific measurement assumptions made by Kochhar (2023). In particular, CEA follows this prior research in making use of information about Work Activities, a rough proxy for tasks which are a list of 41 distinct activities about which individuals in all occupations are asked. Of these activities, CEA also follows prior research in identifying 16 Work Activities as having high exposure to AI: the full list of these activities is included in Appendix Table A1. The premise of this assumption is that these are the activities where the use of AI may be most feasible, given the present understanding of the technology’s current and expected capabilities. Finally, CEA adopts the idea that the potential exposure of an occupation to AI is captured by the importance to the job of the activities that are exposed to AI, in comparison with all other activities.

Table 1. Correlation Between AI Exposure and Measures of Occupational Task Content

AI exposure measure	AI Exposure	AI-Related Performance Requirements
AI Exposure	1.00	0.28
Performance Requirements	0.28	1.00
Autor Dorn (2013) Abstract	0.06	0.61
Autor Dorn (2013) Routine	0.29	0.00
Autor Dorn (2013) Manual	-0.09	-0.08

However, CEA’s measure also differs from previous measures in two key respects. First, CEA differs from Kochhar (2023) and other measures in how it aggregates information about different activities to construct its overall AI exposure index. In particular, CEA standardizes all reported activity importance scores for each activity across occupations, and it defines its relative measure as the difference in average standardized importance between exposed and non-exposed activities. One reason for these methodological changes is that different activities have different average importance in the raw O*NET data, and so standardizing ensures that all work activities are weighted equally in the resulting index.

The normalization also improves interpretability because a unit increase in the importance of a particular activity or set of activities can be interpreted as a one standard deviation change in importance, relative to the distribution in the overall economy. While these methodological changes are helpful to specific pieces of CEA’s subsequent analysis, they have little impact on the extent or composition of measured AI exposure in comparison to Kochhar. Analysis shown in Appendix B reflects the strong relationship between the two measures: the correlation between CEA’s measure and the measure underlying Kochhar is 0.95. The analysis also compares CEA’s AI exposure measure to several other measures in recent literature and finds that all these measures are positively correlated at the occupation level.

CEA’s other primary methodological contribution is to provide an extension of its measure of AI exposure that considers the potential for AI to complement or substitute for human performance in an occupation. Although CEA cannot predict the specific ways in which jobs and workers will adapt to the technology, the measure is intended to identify workers who are potentially most vulnerable to negative outcomes related to increased AI adoption. CEA’s measure, referred to as AI-related job performance requirements, uses information from a separate O*NET question about the degree of complexity or difficulty to which each work activity must be performed in order to perform one’s overall job. The underlying assumption guiding this measure is that complexity and difficulty are closely related to the costs of adoption. If it is more costly and difficult for AI to fully substitute for human performance of an activity, then using AI to complement performance of that activity may be more feasible or cost-effective than using AI to fully automate the activity. As with the measure of AI exposure, CEA’s measure of AI-related job performance requirements is based on an average of standardized values across all AI-exposed activities.

As outlined above, CEA constructs two basic measures for each occupation: an AI exposure score and a score representing the degree of AI-related performance requirements. Along each of these two dimensions, CEA defines threshold levels of exposure and performance requirements so that the full set of occupations can be neatly divided into three groups: AI-exposed with high AI-related performance requirements, AI-exposed with low AI-related performance requirements, and not highly AI-exposed. Specifically, the threshold exposure score is based on the 75th percentile of occupational exposure, unweighted by employment or hours, which is the same threshold used by Pew Research in its analysis (Kochhar 2023).

For performance requirements, CEA’s threshold for delineating high/low AI-related performance requirements is the population median, weighted by aggregate hours in the 2022 American Community Survey (Ruggles et al. 2024). One concern with using any threshold-based measure such as CEA’s is that the overall interpretation of results may be highly sensitive to the chosen threshold. Figure 1 illustrates a particular reason why this concern may be salient: the distribution of relative AI-exposed activity performance across the population is smooth, and no obvious discontinuity in exposure scores is apparent. So, for any chosen threshold, the difference in AI exposure between occupations immediately above and below the threshold is guaranteed to be small. And, changing the chosen exposure threshold mechanically alters the fraction of workers who are considered affected, as well as the difference in exposure between groups.

On the other hand, using discrete thresholds allows for intuitive comparisons across different demographic and socioeconomic groups that may be very useful. CEA has conducted a sensitivity analysis of its selected threshold to determine the extent to which some of its primary findings might be driven by its choice of threshold and has found that broad patterns of economic and demographic exposure are largely replicated when one chooses other thresholds within a sensible range. Portions of this analysis are included in Appendix C. So, while it is important to treat all results that use a binary threshold with caution, CEA believes that the basic conclusions of its analysis are robust to its use of binary thresholds.

As discussed above, the task-based polarization framework has been commonly used to assess the impacts of technological change during the era of widespread computerization. Implementations of this model assess occupations based on measurements of their task content along key characteristic dimensions. Typically, these are measures of routine, cognitive (or abstract), and manual task content (e.g., Autor, Levy, and Murnane 2003; Autor and Dorn 2013). AI depends on computerization, and in many cases, AI adoption involves augmenting existing computerized systems with prediction, automated content generation, or other features. Therefore, it is plausible that an existing task-based framework, or refinements to one, may also be effective in characterizing the future labor market impacts of AI.

Before machine learning approaches were incorporated into automated systems, the extent of computerized automation was often limited by the need for explicit rules and codified procedures (Autor 2014). Yet, many tasks make use of tacit knowledge that is not easily codified (Polanyi 1966), and this made these tasks difficult to automate. So, modern AI systems based on machine learning—including generative AI systems—broaden the set of tasks that computers can perform by reducing the need for explicit, rules-based approaches. In the typical task-based framework, computerized automation has been characterized as capable of substituting for human performance of many routine tasks, which are likely to be codifiable (e.g., Autor and Dorn 2013). And, computerization has been suggested to complement humans in tasks that are abstract in nature. Finally, the framework suggests that workers whose tasks were sufficiently non-routine and not abstract might see their work comparatively unaffected by computer technology. If AI extends computer-led automation in ways that yield similar patterns of complementarity and substitution, then the impacts of AI may in part be predictable based on the relationship between its capabilities and these existing measures of occupational task content.

Using an occupational crosswalk provided by Autor and Dorn (2013), CEA has constructed a comparison between the measures of task content that they use to implement their task-based framework and CEA’s measures of AI exposure and AI-related performance requirements. Table 1 shows the correlations between these measures across occupations. Several noteworthy findings emerge. First, higher AI exposure is moderately associated with more routine task content. This is consistent with an interpretation that AI could be used in part to automate similar types of tasks as previous computer technologies.

Secondly, higher abstract task content corresponds fairly strongly to CEA’s measure of AI-related performance requirements. This suggests that workers who are currently observed to have high AI-related job performance requirements may have benefited from complementarity with computer technologies in the past. And, if previous patterns hold as AI extends the scope of computer-led automation, then jobs with high AI exposure and high AI-related performance requirements could be associated with greater potential for complementarity in the future as well. Finally, both high AI exposure and high AI performance requirements are weakly associated with less manual task content. One concern with a correlation analysis is that a positive correlation could be primarily a result of associations among occupations that are not very exposed to AI.

CEA’s threshold-based analysis considers only a fraction of occupations to be highly AI-exposed, and even fewer of those occupations to have the low AI-related performance requirements that might make them particularly vulnerable. So, it may be more useful to know whether these same relationships hold when considering only this subset of occupations. In Figure 2, CEA graphs distributional parameters for standardized versions of the three task content measures across each of its three basic occupational classifications. The results confirm similar relationships to those found in the initial correlation table. Workers in occupations who are highly AI-exposed, but who have low AI-related performance requirements have substantially lower abstract task content in their work than others, while AI-exposed workers with high performance requirements have comparatively high levels of such content. The relationships along the other two dimensions are less strong, with wider within-category distributions. However, workers in both categories of AI-exposed employment also have, on average, somewhat more routineness to their tasks than other workers.

Overall, these results suggest that CEA’s measure of AI exposure is substantively linked to the notions of task content developed by earlier task-based frameworks. The previous effects of computerization have been argued to be especially strong among workers who perform routine work that is not manual in nature (e.g., Autor and Dorn 2013); as Figure 2 shows, many of the workers whom CEA classifies as most potentially AI-vulnerable do appear to perform relatively routinized work. However, the implications of this analysis are perhaps more important to understanding CEA’s measure of AI-related performance requirements. In developing their measure, Autor and Dorn (2013) suggested that abstractness was associated with human-computer complementarity because these tasks were largely “creative, problem-solving, and coordination tasks … for whom data analysis is an input into production.”

If AI continues to complement these tasks in a similar fashion as previous computer technologies, then the lack of such tasks among AI-exposed workers with low performance requirements supports CEA’s assumption that those workers could be more vulnerable to AI-related displacement. Like all forward-looking predictions of labor market impacts, this interpretation is difficult to thoroughly evaluate until widespread AI adoption has taken place. However, it does suggest potential areas of focus in identifying and targeting the workers who may be most vulnerable to negative economic impacts from AI.

With the basic assumptions outlined above, CEA is able to provide numerous predictions about the potential degree of AI’s impact, as well as its potential to disproportionately impact particular demographic and economic groups. These predictions are made on the premise that the workers who CEA’s measure classifies as exposed to AI are those who perform the tasks that are most likely to change as a result of the technology. Such predictions, like the underlying framework, are made using the best information available at the time of their inception. However, as with many predictive AI models that incorporate continual feedback to improve their effectiveness, predictive economic frameworks generally benefit from continual evaluation. CEA anticipates that as new data become available, this framework and others like it will undergo a similar process of review and refinement.

One thing that this framework does not do, and is not designed to do, is make predictions about the future extent of employment in the economy as a whole. The reason for this limitation is found most clearly in the literature on new task formation (e.g., Acemoglu and Restrepo 2019). Task-based measures of AI exposure predict which workers are most likely to be exposed to AI in their work—they may also provide limited suggestive evidence of which activities could be most prone to labor substitution through automation. However, task-based measures do not predict what new tasks may form in the future, or whether they will be performed by workers in existing occupations or in newly-created ones. Similarly, task-based measures provide limited information about how existing tasks might change over time in response to new technology, or how occupations might adapt to these changes.

In response to questions about the future extent of employment, the best evidence comes from the historical record. Economists and others have predicted for centuries that technological change might lead to widespread “technological unemployment” or drastically reduced hours of work. Yet, as Figure 3 demonstrates, measures of employment such as the working-age employment-population ratio and average hours of work show little evidence of decline in recent decades. In fact, the employment rate remains close to long-term highs, matched only by a period in the late 1990s in which technological change and productivity growth were also rapid, commonly associated with the previous general-purpose technologies of personal computer and internet adoption. Even though new technologies of the past may have displaced some workers from their previous jobs, they failed to reduce employment overall. Similarly, the increased wealth brought about by technological change has not led workers to substantially reduce their hours or employment.

A second thing that CEA’s framework does not do is predict when AI-related impacts may occur. Adopting a new technology often involves complicated changes to production processes, and these changes take time to implement. Additionally, constraints faced in different phases of an overall process can prevent a new technology from being adopted or fully utilized for long periods. Later in this report, CEA conducts a limited analysis of changes in occupations and tasks over time, providing some evidence that changes have already occurred that could plausibly be the result of existing AI uses, or of other computer-related automation. Conversely, other research has found that process innovations resulting from the adoption of AI may not yet be occurring (Babina et al. 2024). Several analysts and researchers have suggested that sizeable productivity improvements from AI could begin within this decade (see Acemoglu 2024 for an overview); labor market impacts could occur on a similar time frame. However, the basic framework that CEA provides cannot provide any insights into this timing.

The Impact of AI Exposure on Labor Market Vulnerability: A Detailed Analysis of Job Characteristics, Demographics, and Geographic Distribution

Artificial Intelligence (AI) is rapidly transforming industries, reshaping labor markets, and redefining the skills required for various occupations. As AI continues to evolve, its impact on the workforce becomes increasingly significant, with implications for job security, income inequality, and economic mobility. This article delves into the complex relationship between AI exposure, job vulnerability, and worker characteristics, drawing on comprehensive data and analysis to provide a nuanced understanding of these dynamics.

Concept Name	Simplified Explanation	Analytical Data / Examples
AI Exposure	The extent to which a job is affected by AI technology.	Example: Architecture and engineering jobs have high AI exposure (score of 0.44), meaning AI is crucial to nearly half of their tasks.
AI-Related Job Performance Requirements	The difficulty or complexity level at which AI-exposed tasks need to be performed.	Example: Architecture and engineering jobs also have high performance requirements (score of 0.81), meaning tasks must be done with significant difficulty or expertise.
Potential Vulnerability	The likelihood that workers in AI-exposed jobs might be negatively affected or displaced.	Example: Office support jobs have high AI exposure but low performance requirements, making them more vulnerable to AI-driven job loss.
Earnings Distribution	The range of income levels among workers in different jobs.	Example: Workers in the lower-middle earnings deciles are more likely to be in AI-exposed jobs, while higher-earning workers often face higher performance requirements.
Income Inequality	The uneven distribution of income across different groups in society.	AI might increase income inequality by replacing middle-income jobs while enhancing productivity in higher-income jobs.
Demographic Differences in AI Exposure	Variations in AI exposure across different groups, such as gender, race, or education.	Example: Women and Asian workers are slightly more likely to work in AI-exposed jobs. Workers with higher education are more likely to be in AI-exposed roles.
Job Vulnerability by Education Level	The risk of job loss due to AI, based on workers’ education levels.	Workers with only a high school diploma are more likely to be in AI-exposed jobs with low performance requirements, making them more vulnerable to displacement.
Age and AI Exposure	How AI exposure affects workers of different ages.	Younger workers (under 25) have lower AI exposure, but older workers (over 25) may face higher vulnerability, especially if they have low-performance requirements.
Geographic Variation in AI Exposure	Differences in AI exposure depending on the region or locality.	Example: The Pacific region has the highest AI exposure, but rural areas may face more vulnerability due to a higher percentage of low-performance requirement jobs.
Unionization and AI Exposure	The role of labor unions in protecting workers from the negative effects of AI.	AI-exposed workers are less likely to be unionized, making them more vulnerable to displacement. Unions could help, but their reach is currently limited among AI-exposed jobs.
Place-Based Policies	Policies targeting specific regions to mitigate AI’s negative impacts.	Suggestion: Target regions with high AI vulnerability (e.g., rural areas) for specialized job training programs to reduce displacement risks.

Table 2. AI Exposure by Occupational Groups

Rank	Occupational Group	Avg. AI Exposure	Avg. AI-Related Performance Requirements	% Highly Exposed Employment	% Exposed Employment with Low Performance Requirements
1	Architecture and Engineering	0.44	0.81	90%	4%
2	Legal	0.39	0.49	100%	1%
3	Computer and Mathematical	0.33	0.36	73%	0%
4	Office and Administrative Support	0.32	-0.36	53%	49%
5	Transportation	0.27	-0.54	81%	75%
6	Life, Physical, and Social Science	0.25	0.69	57%	12%
7	Business and Financial Operations	0.17	0.36	19%	9%
8	Installation, Maintenance, and Repair	0.07	0.06	10%	10%
9	Production	0.07	-0.18	6%	4%
10	Farming, Fishing, and Forestry	0.06	-0.91	0%	0%
11	Protective Service	0.05	0.63	5%	0%
12	Arts, Design, Entertainment, Sports, and Media	0.01	-0.03	18%	13%
13	Healthcare Practitioners and Technical	-0.05	0.52	3%	0%
14	Healthcare Support	-0.08	0.31	16%	1%
15	Management	-0.11	0.46	0%	0%
16	Construction and Extraction	-0.16	-0.05	0%	0%
17	Education Instruction and Library	-0.18	-0.21	0%	0%
18	Sales and Related	-0.23	-0.39	9%	9%
19	Community and Social Services	-0.23	0.24	7%	0%
20	Personal Care and Service	-0.27	-0.74	1%	0%
21	Material Moving	-0.29	-0.78	2%	0%
22	Food Preparation and Serving Related	-0.30	-0.80	0%	0%
23	Building, Grounds Cleaning, and Maintenance	-0.31	-0.69	0%	0%

Differences in Exposure by Job and Worker Characteristics

The Council of Economic Advisers (CEA) has developed measures to assess AI exposure and AI-related job performance requirements, which offer valuable insights into the potential vulnerability of different occupations. By linking these measures to survey microdata from the 2022 American Community Survey (ACS), CEA provides a detailed examination of the demographic and economic characteristics of workers exposed to AI.

Architecture and engineering occupations emerge as the most AI-exposed, with a score of 0.44, indicating that AI-exposed activities are nearly half a standard deviation more important to these occupations than other activities. Interestingly, the top three most exposed occupational groups also exhibit relatively high AI-related job performance requirements, suggesting that workers in these fields may be less vulnerable to displacement. For example, 90 percent of workers in architecture and engineering meet the threshold for high AI exposure, but only 4 percent are classified as potentially vulnerable due to low performance requirements.

In contrast, occupations in office and administrative support and transportation are more likely to have workers who are both highly exposed to AI and potentially vulnerable due to low job performance requirements. This highlights the varying degrees of AI-induced vulnerability across different occupational groups.

Furthermore, many of the least AI-exposed occupations are manual in nature and have low AI-related performance requirements, making them less susceptible to AI-driven disruption. The rankings of AI exposure across occupations underscore the potential for significant variation in the impact of AI across the labor market.

Exposure Across the Earnings Distribution

AI exposure varies considerably across the earnings distribution, with notable implications for income inequality. The highest percentage of workers in AI-exposed occupations is found in the lower-middle portion of the earnings distribution, particularly in the third and fourth deciles, where over a third of workers are exposed to AI. Interestingly, workers in the top two deciles also exhibit relatively high AI exposure, albeit with higher performance requirements.

The relationship between AI exposure and earnings is complex. While workers in lower-earning deciles tend to have lower performance requirements, those in higher-earning AI-exposed occupations generally face more demanding AI-related job performance requirements. This dichotomy suggests that lower-earning workers may be more vulnerable to displacement, while higher-earning workers might use AI to complement their skills, potentially exacerbating income inequality.

Recent surveys, such as one conducted by the Federal Reserve Bank of Dallas in 2024, indicate that firms adopting AI expect to reduce employment in low- and middle-skilled positions while increasing employment in high-skilled positions. This trend aligns with the notion that AI may disproportionately benefit higher-earning workers while posing risks to those in lower-paying jobs.

However, the trajectory of AI’s impact on income distribution is not predetermined. For instance, the potential cost savings from automating tasks performed by highly-paid workers could drive further AI adoption in upper deciles. Alternatively, AI could enhance job complexity and productivity for workers, increasing performance requirements without necessarily displacing them. Government policies could also play a crucial role in shaping AI’s effects on the earnings distribution, either by regulating AI use or through broader fiscal measures.

Table 3. Rank of Census Divisions by Percent of AI-Exposed Employment                                                                                              

Rank	State	Percent of AI-exposed employment	Percent of AI-exposed employment with low performance requirements	Rank by AI exposure with low performance requirements
1	Pacific	20.5%	10.1%	8
2	Mountain	20.2%	10.5%	4
3	Middle Atlantic	20.0%	10.4%	6
4	New England	20.0%	9.4%	9
5	South Atlantic	19.9%	10.3%	7
6	West South Central	19.4%	11.2%	2
7	West North Central	19.3%	11.3%	1
8	East North Central	19.1%	10.5%	5
9	East South Central	18.5%	11.0%	3

Differences in Exposure by Gender, Race/Ethnicity, and Education

AI exposure is not uniform across demographic groups, with significant differences observed by gender, race/ethnicity, and educational attainment. While the gender composition of many occupations has become more equal over time, disparities persist, particularly in AI-exposed fields. For example, women are slightly more likely than men to work in AI-exposed occupations, and Asian workers are somewhat more likely to be employed in such roles.

The most pronounced differences in AI exposure occur across the education distribution. Workers with higher levels of education are significantly more likely to be exposed to AI. Conversely, workers with only a high school diploma or some college education but less than a Bachelor’s degree are more likely to be employed in AI-exposed jobs with lower performance requirements, making them potentially more vulnerable to displacement.

Notably, women are more likely than men to be employed in high AI-exposed occupations with low performance requirements, suggesting a higher risk of displacement from AI. These findings underscore the need for targeted policies to address the potential disparities in AI’s impact on different demographic groups.

Differences in Exposure by Age

AI exposure also varies by age, with implications for the long-term career prospects of workers. Younger workers, particularly those under 25, are less likely to be employed in AI-exposed occupations. This may be due in part to their temporary employment in low-exposure service roles before transitioning to other career opportunities.

Among workers over the age of 25, AI exposure rates are relatively stable, but the likelihood of working in an AI-exposed job with low performance requirements increases with age. This pattern is particularly concerning for older workers, who may find it more difficult to adapt to AI-related changes in the labor market.

Research suggests that older workers may be more negatively affected by job displacement, particularly if the displacement is linked to automation. This raises important questions about the need for retraining and reskilling programs to support older workers in adapting to the evolving demands of the labor market.

Geographic Patterns of AI Exposure

AI exposure is not evenly distributed across the United States, with significant geographic variation observed at both regional and local levels. While broad geographic regions show relatively little variation in AI exposure, finer geographic analysis reveals substantial differences in exposure across places.

For example, the Pacific Census Division has the highest percentage of AI-exposed employment, while the East South Central Division has the lowest. However, when examining AI-exposed employment with low performance requirements, the West North Central and West South Central regions rank highest, suggesting that these areas may be more vulnerable to AI-driven job displacement.

Detailed maps at the Public Use Microdata Area (PUMA) level further illustrate the geographic distribution of AI exposure. While some rural areas exhibit high rates of potentially vulnerable employment, many urban areas also rank high in AI exposure, though not necessarily in vulnerability. This suggests that the geographic clustering of AI’s positive and negative effects may differ, with important implications for place-based policies aimed at supporting affected workers.

Unionization and AI Exposure

Unions have traditionally played a critical role in protecting workers’ rights and ensuring that technological advancements benefit the workforce. However, AI-exposed workers are less likely to be unionized than their non-exposed counterparts. Only 9.0 percent of AI-exposed workers are union members, compared to 10 percent of the overall wage and salary workforce.

The lower unionization rates among AI-exposed workers are particularly pronounced in certain earnings deciles, with potentially AI-vulnerable workers being less likely to be unionized. This raises concerns about the ability of unions to effectively represent workers who may be at risk of displacement due to AI.

Despite these challenges, unions can still play a valuable role in empowering workers and ensuring that AI adoption is equitable. However, a broader approach that includes incentives for firms to consider the impact of AI on their labor force may be necessary to complement the efforts of unions.

Understanding the Historical and Current Impacts of AI on Labor Markets

In assessing the potential future impacts of AI on labor markets, it is critical to draw parallels with past technological advancements and analyze existing data on employment trends. This article explores the intersection of AI and employment by delving into historical patterns, recent developments, and the possible trajectories of labor market dynamics in an increasingly AI-driven world. The analysis is built upon comprehensive data and trends observed over the past few decades, with a particular focus on how AI is reshaping the occupational landscape.

The Council of Economic Advisers (CEA) has developed a framework that links AI’s impact on employment with historical measures of occupational task content. This framework is crucial for understanding how AI might influence different sectors of the labor market. By examining a variety of historical and recent employment trends, CEA provides insights into whether AI is already affecting labor markets and how workers might adapt to the growing presence of AI technologies.

Concept Name	Simplified Explanation	Analytical Data / Examples
AI Exposure	The extent to which a job or occupation is affected by AI technologies. Occupations with high AI exposure are more likely to experience changes due to AI.	Example: Jobs in software development have high AI exposure because AI tools are increasingly used in coding and testing software.
Performance Requirements	The level of skill or ability needed to perform tasks in a job. Jobs with high performance requirements need advanced skills, while those with low requirements need basic skills.	Example: A data analyst role typically requires high performance requirements like advanced statistical knowledge, whereas a clerical job might require basic data entry skills.
Occupational Transition	The process of changing jobs or careers. This can happen when workers move from one occupation to another, often due to changes in demand for specific skills or job displacement.	Example: A factory worker may transition to a tech support role if their factory job is automated.
Employment Growth Trends	The change in the number of jobs in specific occupations over time. This concept helps understand which jobs are expanding or shrinking in the economy.	Analytical Data: Employment in tech-related jobs has grown significantly in the last decade, while jobs in manual labor have seen slower growth.
Upskilling	The process of learning new skills or improving existing ones to keep up with technological changes and remain competitive in the job market.	Example: Workers in traditional manufacturing may take courses in robotics to upskill and stay relevant as automation increases.
AI-Exposed Occupations	Jobs that are significantly influenced by AI technologies. These jobs might change in nature or even decrease as AI becomes more prevalent.	Analytical Data: Occupations like customer service and legal research are highly AI-exposed because AI can automate many tasks in these areas.
Occupational Vulnerability	The risk of job loss or change in job roles due to AI. Occupations with low performance requirements and high AI exposure are considered more vulnerable.	Example: Routine clerical jobs are vulnerable because AI can perform many of these tasks faster and more accurately.
Task Content Evolution	The way tasks within a job change over time, particularly as new technologies are adopted. Workers might need to focus on different tasks as AI takes over routine ones.	Example: In accounting, the focus may shift from data entry to data analysis as AI automates bookkeeping tasks.
Long-Run Employment Trends	Patterns in job growth or decline over an extended period, providing insights into how certain sectors or occupations are evolving.	Analytical Data: Over the past 20 years, tech jobs have consistently grown, while jobs in traditional manufacturing have declined.
Complementarity and Substitution	The relationship between workers and AI, where AI can either complement a worker’s skills (making them more productive) or substitute for them (replacing the job).	Example: AI complements radiologists by helping analyze images faster, but substitutes for some clerical work by automating scheduling and record-keeping.
Career Path Adaptation	How workers adjust their career paths in response to changing job demands, often involving learning new skills or moving into different roles.	Example: A journalist might adapt by learning data journalism as traditional reporting methods are augmented with AI-driven analysis.
Pandemic Recovery Effects	The impact of the COVID-19 pandemic on job recovery, particularly how different occupations have bounced back at different rates.	Analytical Data: Tech jobs recovered quickly after the pandemic due to high demand for digital services, while hospitality jobs took longer to recover due to restrictions and lower demand.
Industry-Specific AI Impact	How AI affects different industries uniquely, depending on how much AI can automate or enhance the work within those industries.	Example: The legal industry sees AI impacting research and document review, while the oil and gas industry sees AI optimizing drilling operations.
Human Capital	The knowledge, skills, and abilities that workers bring to their jobs. This concept is crucial as it affects how easily workers can adapt to new technologies like AI.	Example: Workers with strong analytical skills might find it easier to adapt to AI tools than those with limited technical knowledge.
Job Displacement	The loss of jobs due to automation or other technological changes, where workers may need to find new employment in different fields.	Analytical Data: Studies show that jobs involving routine tasks are more prone to displacement by AI and automation.
Adaptability to AI	The ability of workers or occupations to adjust to the changes brought by AI, either by learning new skills or shifting to different tasks that AI cannot easily replicate.	Example: Healthcare workers adapting to AI-assisted diagnostics by focusing more on patient care and less on routine data entry.
Aggregate Economy	The total economic environment, considering all jobs, industries, and technologies. This concept helps in understanding the overall impact of AI on the economy.	Analytical Data: The aggregate economy has seen shifts towards more tech-oriented jobs as AI and automation become more prevalent across industries.
Technological Adaptation	How workers, industries, or economies adjust to new technologies like AI. This concept covers the processes involved in learning, implementing, and benefiting from new technology.	Example: The financial industry adapting to AI by incorporating machine learning algorithms for fraud detection and investment strategies.
Economic Disparity from AI	The differences in economic outcomes for workers based on their ability to adapt to AI. Those who can upskill and adapt are more likely to benefit, while others may face challenges.	Analytical Data: AI has been shown to increase productivity, but the benefits are often unevenly distributed, with high-skilled workers gaining more than low-skilled workers.
Future Job Market Predictions	Forecasts about how AI will shape the job market in the coming years, including potential job creation in new fields and job losses in others.	Example: AI is expected to create new jobs in AI system development and maintenance, while reducing jobs in areas like routine customer service.

Historical Employment Trends and Recent Comparisons

A historical analysis of employment trends among three distinct occupational groups defined by CEA’s measures reveals notable patterns. These groups are categorized based on their exposure to AI and the performance requirements of the occupations. Over the past two decades, employment growth in occupations with low AI-related performance requirements has consistently lagged behind those with high performance requirements and those not highly exposed to AI. Initially, the employment growth across these groups was parallel, with similar responses during significant economic events such as the Great Recession and subsequent recovery.

However, recent years have seen a divergence in growth patterns, particularly between occupations with high and low performance requirements. The gap in employment growth between these two groups widened significantly towards the end of the last decade. The non-exposed group, in contrast, experienced a steeper decline in employment during the pandemic recession but recovered more quickly, potentially reflecting differences in working environments between AI-exposed and less-exposed occupations. Employment growth between 2021 and 2022 remained largely parallel across these groups, but interpreting these trends is challenging due to the ongoing pandemic recovery.

While historical analysis provides valuable insights into long-term trends, it may not fully capture the immediate changes associated with the rapid rise of new AI systems, such as generative AI. To better understand AI’s recent impact on employment growth, CEA has compared recent changes in employment across different groups with their long-term trends. Payroll employment data and information from the Bureau of Labor Statistics were utilized to examine the relationship between occupation and industry employment patterns.

Certain industries, such as Legal Services, Oil and Gas Extraction, and Software Development, employ high fractions of AI-exposed workers. The employment growth in these industries in 2023 was compared against long-term trends from 2007 to 2019. The results indicate that industries with a higher percentage of AI-exposed workers did not show a significant correlation between changes in employment growth and AI exposure. This suggests that the effects of AI on employment may still be in the early stages of firm adoption processes, where updates to systems and processes have not yet been fully implemented.

Occupational Transitions and Career Paths

As AI continues to permeate various sectors, workers may respond by transitioning to different jobs or occupations. A displaced worker from an AI-affected occupation might seek employment in a less vulnerable occupation, or they might switch to an occupation with higher AI-related performance requirements that AI cannot yet replicate. Given the task-specific nature of human capital, such occupational switches often come with implicit costs, reflecting a supply response to changing demand patterns.

CEA’s analysis of job transitions among AI-exposed workers, based on data from the Current Population Survey (CPS), offers valuable insights into these patterns. By tracking the same workers over adjacent years, the analysis constructs a one-year occupational transition matrix for three main occupational categories. The findings suggest that workers in AI-exposed occupations with high AI-related performance requirements are increasingly likely to remain in the same occupation, while those in low-performance requirement jobs are more likely to switch occupations. Interestingly, many of these switches are to occupations not exposed to AI, although an increasing proportion is moving to higher performance AI-exposed jobs.

The patterns observed in occupational transitions between 2022 and 2023 compared to the pre-pandemic period of 2015 to 2019 are consistent with possible changes in occupational demand. Workers in high AI-exposure jobs with high performance requirements are increasingly likely to stay within their occupations, reflecting a possible complementarity between AI and these jobs. On the other hand, workers in low-performance requirement jobs are more likely to change occupations, often moving to less AI-exposed or higher performance requirement roles.

These findings align with emerging patterns of complementarity and substitution, although the analysis is subject to several data-related limitations. Notably, the reliance on self-reported occupations from surveys may introduce inaccuracies, and the analysis only considers transitions over a one-year period, which may not capture longer-term career shifts.

Changes in Task Content Over Time

Predicting the labor market impacts of AI is further complicated by the evolving nature of occupational tasks. As workers and firms adopt new technologies, the tasks associated with specific occupations may change significantly. Even in cases where automation is implemented, workers may not necessarily be displaced if they can adapt by emphasizing other elements of their work. Conversely, workers in certain occupations may benefit from new technologies, increasing their output or capabilities.

CEA’s analysis highlights that workers in the lower-middle portion of the earnings distribution are both most likely to be exposed to AI and most likely to have low AI-related performance requirements. This combination makes them particularly vulnerable to substitution and potential harm. However, it’s important to note that CEA’s measure is based on current performance requirements, which may not necessarily predict future adaptability. If workers and jobs can adapt to AI over time, the potential harms might never materialize.

Some researchers have suggested that AI could be especially beneficial to the middle class by enabling them to perform tasks previously reserved for highly paid experts. Empirical evidence from recent studies supports this view, showing that AI can significantly enhance productivity, particularly among less experienced and less skilled workers. For instance, call center workers with access to AI exhibited the largest productivity gains among the least skilled workers.

CEA continues to monitor evolving research on how workers use AI but has not made specific predictions about how task content will change over time. However, using data from ONET, CEA has provided some evidence on how occupations have evolved in the past, particularly concerning AI-exposed activities. By analyzing ONET Work Activity scores from 2007 onwards, CEA researchers measured changes in AI exposure and performance requirements over time, relative to the base year of 2007.

The analysis reveals that AI exposure in the overall economy has increased slightly since 2016, although the magnitude of this change is relatively small. Interestingly, changes in AI-related performance requirements have been more pronounced, indicating a general trend of upskilling across the economy. This upskilling is not limited to AI-exposed occupations but reflects broader shifts in job complexity and difficulty.

Moreover, CEA’s analysis shows that within-occupation changes in performance requirements account for the majority of the observed upskilling, rather than shifts in employment across different occupations. This suggests that occupations have undergone significant upskilling over time, which could mitigate some of the potential displacement risks associated with AI.

Occupational Vulnerability and Upskilling

The distribution of changes in AI exposure and AI-related performance requirements among different occupational categories highlights important differences. Workers in highly AI-exposed occupations with low AI-related performance requirements appear to be particularly vulnerable, as their exposure to AI has increased while the complexity and difficulty of their jobs have not. This lack of adaptation over time may increase their susceptibility to displacement by AI.

In contrast, workers in AI-exposed occupations with high AI-related performance requirements have seen significant increases in job complexity, suggesting that they are better positioned to complement AI technologies. This disparity underscores the importance of upskilling and adaptability in determining which workers and occupations will benefit from AI and which may face challenges.

CEA’s analysis of AI’s impact on the labor market is ongoing, and the patterns observed in recent years may evolve as AI technologies continue to develop. The potential for upskilling among vulnerable workers could mitigate some of the negative impacts, but the occupations identified as potentially AI-vulnerable are currently showing less evidence of such upskilling. As AI adoption continues, these workers may face increasing risks unless they can adapt to the changing demands of their jobs.

Overall, this comprehensive analysis underscores the complex and multifaceted nature of AI’s impact on labor markets. While AI has the potential to enhance productivity and create new opportunities, it also poses significant challenges, particularly for workers in occupations with low AI-related performance requirements. The future of work in an AI-driven world will depend on how workers, firms, and policymakers navigate these challenges and leverage the opportunities presented by this transformative technology.

The Uncertain Impact of Artificial Intelligence on Labor Markets

Concept Name	Simplified Explanation	Analytical Data/Examples
AI Vulnerability in Labor Markets	Certain workers, especially those in low to mid-level income jobs, are more at risk of losing their jobs or facing reduced wages due to the rise of AI. This happens because AI can automate routine tasks that these workers often perform.	Examples include jobs in manufacturing, data entry, or customer service where AI tools can replace repetitive tasks. Analytical data shows that these occupations are often in the lower-middle earnings bracket.
Data-Driven Approach by CEA	The Council of Economic Advisers (CEA) uses data to predict how AI might affect different workers. By analyzing existing data, they aim to make informed predictions, though some of this data might be incomplete due to the newness of AI technologies.	The CEA analyzes employment data, earnings distribution, and AI adoption rates to assess potential impacts. They highlight that predictions are grounded in evidence but also emphasize the need for caution due to the emerging nature of AI technologies.
Occupational Impact of AI	AI impacts different jobs in varying ways. Workers in jobs that involve repetitive, routine tasks are more likely to be affected negatively. Occupations requiring creativity, complex problem-solving, or interpersonal skills are less likely to be replaced by AI.	Occupations like assembly line workers or data entry clerks are more vulnerable, whereas jobs like software development, creative arts, or counseling are less likely to be replaced. Data shows that AI is less effective in automating tasks that require human judgment and creativity.
Demographic and Geographic Exposure	The impact of AI isn’t uniform across all regions or demographic groups. Workers in certain areas or demographic groups may be more vulnerable to AI-related disruptions due to factors like local industry concentrations or educational levels.	Data from regions with a high concentration of manufacturing jobs, for example, shows a higher risk of AI-related job displacement. Similarly, demographic groups with lower access to education or technology training may be more exposed to the negative impacts of AI.
Potential Benefits of AI for Workers	AI could lead to higher productivity, better wages, and more engaging work by automating tedious tasks and allowing workers to focus on more enjoyable parts of their jobs. However, these benefits are not guaranteed and depend on how AI is implemented.	Examples include AI tools in healthcare that automate administrative tasks, allowing doctors to spend more time with patients. Analytical data indicates that when implemented thoughtfully, AI can enhance job satisfaction and improve working conditions.
Potential Harms of AI for Workers	AI could also harm workers by reducing demand for certain jobs, leading to lower wages, job losses, and increased inequality. Additionally, AI might infringe on workers’ privacy or enable discrimination if not properly regulated.	Analytical data highlights risks in industries like retail or logistics, where AI could lead to significant job cuts. Cases of AI-enabled surveillance or biased decision-making tools have raised concerns about privacy and discrimination in workplaces.
Biden-Harris Administration’s AI Policies	The administration has taken steps to ensure that AI is adopted safely and responsibly. This includes measures to protect workers’ rights, ensure fair employment practices, and maintain good working conditions. Executive orders and guidance from the Department of Labor are part of these efforts.	Executive Order 14110 is a key policy initiative aimed at managing AI’s impact on workers. The Department of Labor’s guidance helps companies implement AI in ways that uphold workers’ rights and ensure responsible use of AI technologies.
Ongoing Research and Monitoring	Continuous research is necessary to understand how AI affects the labor market over time. As AI technologies evolve, so will their impact on jobs. Ongoing analysis helps in adjusting policies to protect workers and maximize the benefits of AI.	CEA’s reports and studies are examples of ongoing efforts to monitor AI’s impact. They regularly update their findings to reflect new data, helping policymakers stay informed and make better decisions.
Importance of Adaptive Policies	As AI continues to change the labor market, it’s crucial to have flexible policies that can adapt to new developments. This ensures that the benefits of AI are widely shared and that negative impacts are minimized.	Adaptive policies might include retraining programs for displaced workers or incentives for companies that use AI in ways that create new job opportunities. Data suggests that countries with proactive, adaptive policies are better positioned to handle the transition to an AI-driven economy.

The rapid evolution of Artificial Intelligence (AI) technologies has the potential to profoundly transform labor markets worldwide. However, as with any transformative technology, the implications for workers are complex and highly uncertain. This article delves into the data-driven approach employed by the Council of Economic Advisers (CEA) to understand these potential impacts, emphasizing both the benefits and limitations of AI adoption across different segments of the workforce.

At the core of CEA’s analysis lies a commitment to grounding predictions in the available data, ensuring that any forecasts about AI’s effects on labor markets are as evidence-based as possible. This rigorous approach allows for the identification of specific trends and patterns within the labor market, providing a clearer picture of which workers might be most at risk and why. However, the nascent nature of many AI technologies means that the data is often incomplete or inconclusive, necessitating a degree of caution in interpreting these findings.

One of the primary concerns highlighted by the CEA is the vulnerability of certain workers to the negative impacts of AI. The report identifies a subset of workers who are particularly exposed to the risks associated with AI adoption, primarily those situated in the lower-middle portion of the earnings distribution. These workers, often employed in occupations that involve routine or repetitive tasks, are more likely to experience job displacement or reduced earnings as AI technologies become more prevalent.

The identification of these vulnerable workers is not just a matter of occupational analysis but also involves an understanding of demographic and geographic patterns of AI exposure. For instance, workers in certain regions or industries may be more susceptible to the disruptive effects of AI, necessitating targeted policy responses that consider these broader contextual factors.

In response to these challenges, the Biden-Harris Administration has adopted a comprehensive, whole-of-government approach to ensuring the safe and responsible adoption of AI. This approach includes measures to protect workers’ rights, maintain fair working conditions, and ensure that employment decisions are made responsibly. Executive Order 14110 and guidance from the Department of Labor are central to these efforts, providing a framework for managing the risks associated with AI while maximizing its potential benefits.

The potential benefits of AI for workers are significant, ranging from increased productivity and higher wages to improved working conditions and more fulfilling job roles. By automating routine tasks, AI has the potential to free up workers to focus on the more interesting and creative aspects of their jobs, leading to greater job satisfaction and overall well-being. However, these benefits are not guaranteed and must be carefully managed through appropriate policies and regulations.

On the other hand, the harms associated with AI adoption are also substantial. Reduced demand for certain types of labor could lead to job displacement, declining wages, and increased economic inequality. Moreover, AI technologies could be misused in ways that undermine workers’ privacy, autonomy, and rights, or that perpetuate existing biases and discrimination in the workplace. These risks underscore the need for a thoughtful and proactive policy response to ensure that the adoption of AI is both safe and equitable.

The CEA’s analysis also highlights the importance of ongoing research and data collection to monitor the evolving impact of AI on the labor market. As AI technologies continue to develop and become more integrated into various industries, the effects on workers will likely change. Continuous monitoring and analysis will be essential to ensuring that policies remain relevant and effective in mitigating the negative impacts of AI while promoting its positive potential.

In conclusion, while the potential implications of AI on workers and labor markets are vast, they are also fraught with uncertainty. The CEA’s data-driven approach provides a valuable framework for understanding these impacts, but it is not without its limitations. As the adoption of AI increases, ongoing analysis and policy adjustments will be necessary to protect vulnerable workers and ensure that the benefits of AI are broadly shared across the economy. The Biden-Harris Administration’s commitment to a responsible AI strategy, as outlined in Executive Order 14110 and other initiatives, reflects a recognition of the importance of these issues and a determination to address them proactively. Through thoughtful policies and careful management, it is possible to harness the benefits of AI while minimizing its potential harms, ensuring that the transition to an AI-driven economy is as smooth and equitable as possible for all workers.

As the labor market continues to evolve in response to AI and other technological advances, the need for adaptable and forward-looking policies will only grow. The CEA’s ongoing efforts to analyze new developments in data and research will play a crucial role in informing these policies and ensuring that they are effectively implemented. By staying ahead of the curve and addressing the challenges posed by AI head-on, it is possible to create a future in which the benefits of AI are maximized while the risks are minimized, ultimately leading to a more dynamic, inclusive, and resilient labor market.

This text is a chapter in a broader exploration of AI’s impact on the labor market. The narrative continues to evolve, with the aim of providing a comprehensive and nuanced understanding of the complexities involved. Through continued research, analysis, and policy development, we can better anticipate and respond to the challenges and opportunities presented by AI, ensuring that its adoption leads to a future that benefits all workers, regardless of their occupation, demographic, or geographic location.

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resource : https://www.whitehouse.gov/cea/written-materials/2024/07/10/potential-labor-market-impacts-of-artificial-intelligence-an-empirical-analysis/

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