December 2016

PREPARING FOR THE FUTURE 

OF ARTIFICIAL INTELLIGENCE

National Science and Technology Council

Artificial Intelligence,

Automation, and the Economy

Executive Office of the President
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EXECUTIVE OFFICE OF THE PRESIDENT

WASHINGTON, D.C. 20502

December 20, 2016

Advances in Artificial Intelligence (AI) technology and related fields have opened up new markets and 

new opportunities for progress in critical areas such as health, education, energy, economic inclusion, 

social welfare, and the environment. In recent years, machines have surpassed humans in the performance 

of certain tasks related to intelligence, such as aspects of image recognition. Experts forecast that rapid 

progress in the field of specialized artificial intelligence will continue. Although it is unlikely that 

machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the 

next 20 years, it is to be expected that machines will continue to reach and exceed human performance on 

more and more tasks.

AI-driven automation will continue to create wealth and expand the American economy in the coming 

years, but, while many will benefit, that growth will not be costless and will be accompanied by changes 

in the skills that workers need to succeed in the economy, and structural changes in the economy. 

Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and 

to ensure that the enormous benefits of AI and automation are developed by and available to all.

Following up on the Administration’s previous report, Preparing for the Future of Artificial Intelligence, 

which was published in October 2016, this report further investigates the effects of AI-driven automation 

on the U.S. job market and economy, and outlines recommended policy responses. 

This report was produced by a team from the Executive Office of the President including staff from the 

Council of Economic Advisers, Domestic Policy Council, National Economic Council, Office of 

Management and Budget, and Office of Science and Technology Policy. The analysis and 

recommendations included herein draw on insights learned over the course of the Future of AI Initiative, 

which was announced in May of 2016, and included Federal Government coordination efforts and crosssector and public outreach on AI and related policy matters.

Beyond this report, more work remains, to further explore the policy implications of AI. Most notably, AI 

creates important opportunities in cyberdefense, and can improve systems to detect fraudulent 

transactions and messages.

Jason Furman John P. Holdren

Chair, Council of Economic Advisers Director, Office of Science and Technology Policy

Cecilia Muñoz Megan Smith

Director, Domestic Policy Council U.S. Chief Technology Officer

Jeffrey Zients

Director, National Economic Council
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

Contents

Executive Summary.....................................................................................................................................................1

Economics of AI-Driven Automation ..................................................................................................................1

Policy Responses..................................................................................................................................................3

Conclusion ...........................................................................................................................................................4

Outreach and Development of this Report................................................................................................................5

Introduction .................................................................................................................................................................6

Economics of AI-Driven Automation.........................................................................................................................8

AI and the Macroeconomy: Technology and Productivity Growth .....................................................................8

AI and the Labor Market: Diverse Potential Effects..........................................................................................10

Historical Effects of Technical Change..............................................................................................................11

AI and the Labor Market: The Near Term .........................................................................................................13

What kind of jobs will AI create? ......................................................................................................................18

Technology is Not Destiny—Institutions and Policies Are Critical...................................................................21

Policy Responses........................................................................................................................................................26

Strategy #1: Invest In and Develop AI for its Many Benefits............................................................................27

Strategy #2: Educate and Train Americans for Jobs of the Future .....................................................................30

Strategy #3: Aid Workers in the Transition and Empower Workers to Ensure Broadly Shared Growth...........34

Conclusion..................................................................................................................................................................43

References ..................................................................................................................................................................44
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

1

Executive Summary

Accelerating artificial intelligence (AI) capabilities will enable automation of some tasks that 

have long required human labor.1 These transformations will open up new opportunities for 

individuals, the economy, and society, but they have the potential to disrupt the current 

livelihoods of millions of Americans. Whether AI leads to unemployment and increases in 

inequality over the long-run depends not only on the technology itself but also on the institutions 

and policies that are in place. This report examines the expected impact of AI-driven automation 

on the economy, and describes broad strategies that could increase the benefits of AI and 

mitigate its costs.

Economics of AI-Driven Automation

Technological progress is the main driver of growth of GDP per capita, allowing output to 

increase faster than labor and capital. One of the main ways that technology increases 

productivity is by decreasing the number of labor hours needed to create a unit of output. Labor 

productivity increases generally translate into increases in average wages, giving workers the 

opportunity to cut back on work hours and to afford more goods and services. Living standards 

and leisure hours could both increase, although to the degree that inequality increases—as it has 

in recent decades—it offsets some of those gains. 

AI should be welcomed for its potential economic benefits. Those economic benefits, however,

will not necessarily be evenly distributed across society. For example, the 19th century was 

characterized by technological change that raised the productivity of lower-skilled workers 

relative to that of higher-skilled workers. Highly-skilled artisans who controlled and executed 

full production processes saw their livelihoods threatened by the rise of mass production 

technologies. Ultimately, many skilled crafts were replaced by the combination of machines and 

lower-skilled labor. Output per hour rose while inequality declined, driving up average living 

standards, but the labor of some high-skill workers was no longer as valuable in the market.

In contrast, technological change tended to work in a different direction throughout the late 20th

century. The advent of computers and the Internet raised the relative productivity of higherskilled workers. Routine-intensive occupations that focused on predictable, easily-programmable 

tasks—such as switchboard operators, filing clerks, travel agents, and assembly line workers—

were particularly vulnerable to replacement by new technologies. Some occupations were 

virtually eliminated and demand for others reduced. Research suggests that technological 

innovation over this period increased the productivity of those engaged in abstract thinking, 

creative tasks, and problem-solving and was therefore at least partially responsible for the 

substantial growth in jobs employing such traits. Shifting demand towards more skilled labor 

raised the relative pay of this group, contributing to rising inequality. At the same time, a 

 

1 A more extensive introductory discussion of artificial intelligence, machine learning, and related policy topics can

be found in the Administration’s first report on this subject. See The White House, “Preparing for the Future of

Artificial Intelligence,” October 2016

(https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_o

f_ai.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

2

slowdown in the rate of improvement in education, and institutional changes such as the 

reduction in unionization and decline in the minimum wage, also contributed to inequality—

underscoring that technological changes do not uniquely determine outcomes.

Today, it may be challenging to predict exactly which jobs will be most immediately affected by 

AI-driven automation. Because AI is not a single technology, but rather a collection of 

technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the 

economy. Some tasks will be more easily automated than others, and some jobs will be affected 

more than others—both negatively and positively. Some jobs may be automated away, while for 

others, AI-driven automation will make many workers more productive and increase demand for 

certain skills. Finally, new jobs are likely to be directly created in areas such as the development 

and supervision of AI as well as indirectly created in a range of areas throughout the economy as 

higher incomes lead to expanded demand. 

Recent research suggests that the effects of AI on the labor market in the near term will continue 

the trend that computerization and communication innovations have driven in recent decades. 

Researchers’ estimates on the scale of threatened jobs over the next decade or two range from 9 

to 47 percent. For context, every 3 months about 6 percent of jobs in the economy are destroyed 

by shrinking or closing businesses, while a slightly larger percentage of jobs are added—

resulting in rising employment and a roughly constant unemployment rate. The economy has 

repeatedly proven itself capable of handling this scale of change, although it would depend on 

how rapidly the changes happen and how concentrated the losses are in specific occupations that 

are hard to shift from.

Research consistently finds that the jobs that are threatened by automation are highly 

concentrated among lower-paid, lower-skilled, and less-educated workers. This means that 

automation will continue to put downward pressure on demand for this group, putting downward 

pressure on wages and upward pressure on inequality. In the longer-run, there may be different 

or larger effects. One possibility is superstar-biased technological change, where the benefits of 

technology accrue to an even smaller portion of society than just highly-skilled workers. The 

winner-take-most nature of information technology markets means that only a few may come to 

dominate markets. If labor productivity increases do not translate into wage increases, then the 

large economic gains brought about by AI could accrue to a select few. Instead of broadly shared 

prosperity for workers and consumers, this might push towards reduced competition and 

increased wealth inequality.

Historically and across countries, however, there has been a strong relationship between 

productivity and wages—and with more AI the most plausible outcome will be a combination of 

higher wages and more opportunities for leisure for a wide range of workers. But the degree that 

this materializes depends not just on the nature of technological change but importantly on the 

policy and institutional choices that are made about how to prepare workers for AI and to handle 

its impacts on the labor market.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

3

Policy Responses

Technology is not destiny; economic incentives and public policy can play a significant role in 

shaping the direction and effects of technological change. Given appropriate attention and the 

right policy and institutional responses, advanced automation can be compatible with 

productivity, high levels of employment, and more broadly shared prosperity. In the past, the 

U.S. economy has adapted to new production patterns and maintained high levels of employment 

alongside rising productivity as more productive workers have had more incentive to work and 

more highly paid workers have spent more, supporting this work. But, some shocks have left a 

growing share of workers out of the labor force. This report advocates strategies to educate and 

prepare new workers to enter the workforce, cushion workers who lose jobs, keep them attached 

to the labor force, and combat inequality. Most of these strategies would be important regardless 

of AI-driven automation, but all take on even greater importance to the degree that AI is making 

major changes to the economy.

Strategy #1: Invest in and develop AI for its many benefits. If care is taken to responsibly 

maximize its development, AI will make important, positive contributions to aggregate 

productivity growth, and advances in AI technology hold incredible potential to help the United 

States stay on the cutting edge of innovation. Government has an important role to play in 

advancing the AI field by investing in research and development. Among the areas for 

advancement in AI are cyberdefense and the detection of fraudulent transactions and messages. 

In addition, the rapid growth of AI has also dramatically increased the need for people with 

relevant skills from all backgrounds to support and advance the field. Prioritizing diversity and 

inclusion in STEM fields and in the AI community specifically, in addition to other possible 

policy responses, is a key part in addressing potential barriers stemming from algorithmic bias. 

Competition from new and existing firms, and the development of sound pro-competition 

policies, will increasingly play an important role in the creation and adoption of new 

technologies and innovations related to AI.

Strategy #2: Educate and train Americans for jobs of the future. As AI changes the nature of 

work and the skills demanded by the labor market, American workers will need to be prepared 

with the education and training that can help them continue to succeed. Delivering this education 

and training will require significant investments. This starts with providing all children with 

access to high-quality early education so that all families can prepare their students for continued 

education, as well as investing in graduating all students from high school college- and careerready, and ensuring that all Americans have access to affordable post-secondary education. 

Assisting U.S. workers in successfully navigating job transitions will also become increasingly 

important; this includes expanding the availability of job-driven training and opportunities for 

lifelong learning, as well as providing workers with improved guidance to navigate job 

transitions. 

Strategy #3: Aid workers in the transition and empower workers to ensure broadly shared 

growth. Policymakers should ensure that workers and job seekers are both able to pursue the job 

opportunities for which they are best qualified and best positioned to ensure they receive an 

appropriate return for their work in the form of rising wages. This includes steps to modernize 

the social safety net, including exploring strengthening critical supports such as unemployment 
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

4

insurance, Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary 

Assistance for Needy Families (TANF), and putting in place new programs such as wage 

insurance and emergency aid for families in crisis. Worker empowerment also includes 

bolstering critical safeguards for workers and families in need, building a 21st century retirement 

system, and expanding healthcare access. Increasing wages, competition, and worker bargaining 

power, as well as modernizing tax policy and pursuing strategies to address differential 

geographic impact, will be important aspects of supporting workers and addressing concerns 

related to displacement amid shifts in the labor market. 

Finally, if a significant proportion of Americans are affected in the short- and medium-term by 

AI-driven job displacements, policymakers will need to consider more robust interventions, such 

as further strengthening the unemployment insurance system and countervailing job creation 

strategies, to smooth the transition.

Conclusion

Responding to the economic effects of AI-driven automation will be a significant policy 

challenge for the next Administration and its successors. AI has already begun to transform the 

American workplace, change the types of jobs available, and reshape the skills that workers need 

in order to thrive. All Americans should have the opportunity to participate in addressing these 

challenges, whether as students, workers, managers, technical leaders, or simply as citizens with 

a voice in the policy debate.

AI raises many new policy questions, which should be continued topics for discussion and 

consideration by future Administrations, Congress, the private sector, academia, and the public. 

Continued engagement among government, industry, technical and policy experts, and the public 

should play an important role in moving the Nation toward policies that create broadly shared 

prosperity, unlock the creative potential of American companies and workers, and ensure 

America’s continued leadership in the creation and use of AI.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

5

Outreach and Development of this Report

This report was developed by a team in the Executive Office of the President including staff 

from the White House Council of Economic Advisers (CEA), Domestic Policy Council (DPC), 

National Economic Council (NEC), Office of Management and Budget (OMB), and Office of 

Science and Technology Policy (OSTP). This report follows a previous report published in 

October 2016 titled Preparing for the Future of Artificial Intelligence and the accompanying 

National Artificial Intelligence Research and Development Strategic Plan, developed by the 

National Science and Technology Council’s (NSTC) Subcommittee on Machine Learning and 

Artificial Intelligence. This subcommittee was chartered in May 2016 by OSTP to foster 

interagency coordination and provide technical and policy advice on topics related to AI, and to 

monitor the development of AI technologies across industry, the research community, and the 

Federal Government. This report also follows a series of public-outreach activities as a part of 

the White House Future of Artificial Intelligence Initiative, designed to allow government 

officials to learn from experts and from the public, which included five co-hosted public 

workshops, and a public Request for Information (RFI).2

This report more deeply examines the impact of AI-driven automation on the economy and 

policy responses to it. It considers the economic evidence to better understand the lessons from 

past waves of automation, the impact already caused by the current wave of AI-driven 

automation and its prospects for the near future, and how AI-driven automation may affect 

workers in the future. The report also considers policy steps that are needed to address the 

economic dislocation caused by the arrival of these technologies and to prepare for longer-term 

trends in the economy caused by AI, automation, and other factors that are systemically 

disadvantaging certain workers. The report lays out three broad strategies for policymakers to 

consider.

 

2 Ed Felten and Terah Lyons, “Public Input and Next Steps on the Future of Artificial Intelligence,” Medium,

September 6 2016 (https://medium.com/@USCTO/public-input-and-next-steps-on-the-future-ofartificialintelligence-458b82059fc3). Further details on the public workshops and the RFI can be found in the

October 2016 report, Preparing for the Future of Artificial Intelligence.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

6

Introduction

Recent progress in Artificial Intelligence (AI) has brought renewed attention to questions about 

automation driven by these advances and their impact on the economy. The current wave of 

progress and enthusiasm for AI began around 2010, driven by three mutually reinforcing factors: 

the availability of big data from sources including e-commerce, businesses, social media, 

science, and government;3 which provided raw material for dramatically improved machine 

learning approaches and algorithms; which in turn relied on the capabilities of more powerful 

computers.4 During this period, the pace of improvement surprised AI experts. For example, on a 

popular image recognition challenge that has a 5 percent human error rate according to one error 

measure, 5the best AI result improved from a 26 percent error rate in 2011 to 3.5 percent in 

2015. This progress may enable a range of workplace tasks that require image understanding to 

be automated, and will also enable new types of work and jobs. Progress on other AI challenges 

will drive similar economic changes.

Technical innovation has been expanding the American economy since the country’s founding. 

American ingenuity has always been one of the Nation’s greatest resources, a key driver of 

economic growth, and a source of strategic advantage for the United States. Remarkable 

homegrown innovations have improved quality of life, created jobs, broadened understanding of 

the world, and helped Americans approach their full potential. At the same time, they have 

forced Americans to adapt to changes in the workplace and the job market. These 

transformations have not always been comfortable, but in the long run—and supported by good

public policy—they have provided great benefits.

The current wave of AI-driven automation may not be so different. For example, robots have 

made the economy more efficient. A 2015 study of robots in 17 countries found that they added

an estimated 0.4 percentage point on average to those countries’ annual GDP growth between 

1993 and 2007, accounting for just over one-tenth of those countries’ overall GDP growth during 

that time.6 Some of that growth has been achieved by U.S. manufacturers adopting robots, 

allowing more goods to be produced while employing fewer workers at some facilities. AI in its 

many manifestations also holds promise to transform the basis of economic growth for countries 

across the world; a recent analysis of 12 developed economies (including the United States) 

 

3

“Big Data: Seizing Opportunities, Preserving Values,” Executive Office of the President, May 2014,

https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf.

4 For more information about AI and its policy implications, see: The White House, “Preparing for the Future of

Artificial Intelligence,” October

2016.(https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_fut

ure_of_ai.pdf).

5 The ImageNet Large Scale Visual Recognition Challenge provides a set of photographic images and asks for an 

accurate description of what is depicted in each image. Statistics in the text refer to the “classification error” metric 

in the “classification+localization with provided training data” task. See http://image-net.org/challenges/LSVRC/.

6 Georg Graetz and Guy Michaels, “Robots at Work,” CEPR Discussion Paper No. DP10477, March 2015 

(http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2575781).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

7

found that AI has the potential to double annual economic growth rates in the countries analyzed 

by 2035.7

Some experts have characterized the rise of AI-driven automation as one of the most important 

economic and social developments in history. The World Economic Forum has characterized it 

as the lynchpin of a Fourth Industrial Revolution.

8 Furthermore, the economist Andrew McAfee 

wrote, “Digital technologies are doing for human brainpower what the steam engine and related 

technologies did for human muscle power during the Industrial Revolution. They’re allowing us 

to overcome many limitations rapidly and to open up new frontiers with unprecedented speed. 

It’s a very big deal. But how exactly it will play out is uncertain.”9

At the same time, AI-driven automation has yet to have a quantitatively major impact on 

productivity growth. In fact, measured productivity growth over the last decade has slowed in 

almost every advanced economy. It is plausible, however, that the pace of measured productivity 

growth will pick up in the coming years. To the degree that AI-driven automation realizes its 

potential to drive tremendous positive advancement in diverse fields, it will make Americans 

better off on average. But, there is no guarantee that everyone will benefit. AI-driven changes in 

the job market in the United States will cause some workers to lose their jobs, even while

creating new jobs elsewhere. The economic pain this causes will fall more heavily upon some 

than on others. Policymakers must consider what can be done to help those families and 

communities get back on their feet and assemble the tools they need to thrive in the transformed 

economy and share in its benefits.

 

7 Paul Daugherty and Mark Purdy, “Why AI is the Future of Growth,” 2016

(https://www.accenture.com/t20161031T154852__w__/us-en/_acnmedia/PDF-33/Accenture-Why-AI-is-the-Futureof-Growth.PDF#zoom=50).

8 Klaus Schwab, “The Fourth Industrial Revolution: what it means, how to respond,” World Economic Forum,

January 2016 (https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-howto-respond/). (The first three industrial revolutions are listed as those driven by steam power, electricity, and

electronics.)

9 Amy Bernstein and Anand Raman, “The Great Decoupling: An Interview with Erik Brynjolfsson and Andrew

McAfee,” Harvard Business Review, June 2015 (https://hbr.org/2015/06/the-great-decoupling).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

8

Economics of AI-Driven Automation

Accelerating AI capabilities will enable automation of some tasks that have long required human 

labor. Rather than relying on closely-tailored rules explicitly crafted by programmers, modern AI 

programs can learn from patterns in whatever data they encounter and develop their own rules 

for how to interpret new information. This means that AI can solve problems and learn with very 

little human input. In addition, advances in robotics are expanding machines’ abilities to interact 

with and shape the physical world. Combined, AI and robotics will give rise to smarter machines 

that can perform more sophisticated functions than ever before and erode some of the advantages 

that humans have exercised. This will permit automation of many tasks now performed by 

human workers and could change the shape of the labor market and human activity. 

These transformations may open up new opportunities for individuals, the economy, and society,

but they may also foreclose opportunities that are currently essential to the livelihoods of many 

Americans. This chapter explores the important role that AI-driven automation is likely to have 

in growing the economy and potential effects on labor markets and communities. It draws on 

economic theory and empirical studies of past technological transformations and applies these 

lessons to the current context. While there are many reasons to think that changes in the labor 

market prompted by AI-driven automation will be similar to what has been observed in the past, 

this chapter will also discuss arguments for how the current period could be different from 

previous technological revolutions.

Critically, technology alone will not determine the economic outcomes in terms of growth, 

inequality or employment. The advanced economies all have had access to similar levels of 

technology but have had very different outcomes along all of these dimensions because they 

have had different institutions and policies. But understanding the technological forces is critical 

to shaping the continued evolution of these policies.

AI and the Macroeconomy: Technology and Productivity Growth 

To the extent that AI-driven automation resembles past forms of technological advancement, it 

will make important contributions to aggregate productivity growth.

For centuries, the American economy has adjusted to and evolved with technology. Many jobs 

that existed 150 years ago do not exist today, and jobs no one could have imagined then have 

taken their place. For example, in 1870, almost 50 percent of American employees worked in 

agriculture, supplying the Nation’s food.10 Today, thanks in large part to technological change, 

agriculture employs less than 2 percent of American workers and American food production 

exceeds domestic demand.11 In this case, technological innovations, from McCormick harvesters 

 

10 Patricia A. Daly, “Agricultural Employment: Has the Decline Ended?” Monthly Labor Review, November 1981

(http://www.bls.gov/opub/mlr/1981/11/art2full.pdf).

11 Bureau of Labor Statistics, “Employment Projections: Employment by major industry sector,” December 2015

(http://www.bls.gov/emp/ep_table_201.htm).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

9

to today’s self-driving tractors, increased the productivity of the agricultural sector and 

contributed to increases in standard of living.12

One of the main ways that technology increases productivity is by decreasing the number of 

labor hours needed to create a unit of output. Labor productivity increases generally translate 

into increases in average wages, giving workers the opportunity to cut back on work hours and to 

afford more goods and services. Living standards and leisure hours could both increase, although 

to the degree that inequality increases—as it has in recent decades—it offsets some of those 

gains. The expectation that productivity increases would be accompanied by wage growth is 

what led John Maynard Keynes to predict in his 1930 essay on “Economic Possibilities for our 

Grandchildren” that, given the rates of technical progress, we might have achieved a 15-hour 

workweek by now.13 While that prediction remains far off, over the last 65 years, most 

developed economies saw annual hours worked decline substantially (Figure 1). In the United 

States uniquely, however, this decline stopped in the late 1970s, and hours per worker has 

remained flat since then.

Technology has been one of the main drivers of this productivity growth. Indeed, changes in 

technology help explain permanent productivity increases throughout the 1990s.14 There is also 

 

12 USDA Economic Research Service, “Table 1. Indices of farm output, input, and total factor productivity for the

United States, 1948-2013” (https://www.ers.usda.gov/data-products/agricultural-productivity-in-the-us/).

13 John M. Keynes, “Economic Possibilities for our Grandchildren.” In Essays in Persuasion, New York:

W.W.Norton & Co., pp. 358-373, 1930. (http://www.econ.yale.edu/smith/econ116a/keynes1.pdf).

14 Susanto Basu, John G. Fernald, and Matthew D. Shapiro, “Productivity growth in the 1990s: technology,

utilization, or adjustment?” Carnegie-Rochester Conference Series on Public Policy 55(1): 117-65, 2001

(https://ideas.repec.org/a/eee/crcspp/v55y2001i1p117-165.html).

1,000

1,200

1,400

1,600

1,800

2,000

2,200

2,400

1950 1960 1970 1980 1990 2000 2010

United States

United Kingdom

Canada

France

Italy

Japan

Germany

Figure 1: Average Annual Hours Worked per Worker, G-7 

Countries, 1950-2015

Average annual hours worked

Source: OECD Stat
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

10

evidence that industrial robotic automation alone increased labor productivity growth by 0.36 

percentage points across 17 countries between 1993 and 2007.

15

The potential positive impact of AI-driven automation on productivity is particularly important 

given recent trends in productivity. In the last decade, despite technology’s positive push, 

measured productivity growth has slowed in 30 of the 31 advanced economies, slowing in the 

United States from an average annual growth rate of 2.5 percent in the decade after 1995 to only 

1.0 percent growth in the decade after 2005 (Figure 2). While a considerable amount of this 

slowdown in many countries—including the United States—is due to a slowdown in investment 

in capital stock, the slowdown in total factor productivity growth (the component influenced by

technological change) has also been important.16 This has contributed to slower growth in real 

wages and if continued will slow improvements in living standards.

AI-driven automation could help boost total factor productivity growth and create new potential 

to improve the lives of Americans broadly. The benefits of technological change and economic 

growth, however, are not necessarily shared equally. This can depend on both the nature and 

speed of the technological change as well as the ability of workers to negotiate for the benefits of 

their increased productivity, as discussed below.

AI and the Labor Market: Diverse Potential Effects

Few would dispute that the industrial revolution largely made society better off, but the transition 

led to severe disruptions to the lives and communities of many agricultural workers, with 

 

15 Georg Graetz and Guy Michaels, “Robots at Work,” Centre for Economic Policy Research (CEPR) Discussion

Paper No. DP10477, 2015 (http://cep.lse.ac.uk/pubs/download/dp1335.pdf).

16 Jason Furman, “Productivity Growth in the Advanced Economies: The Past, the Present, and Lessons for the

Future,” Speech at the Peterson Institute for International Economics, Washington, July 9 2015

(https://www.whitehouse.gov/sites/default/files/docs/20150709_productivity_advanced_economies_piie.pdf).

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

United

States

Canada Japan Germany France United

Kingdom

Italy

1995-2005 2005-2015

Figure 2: Labor Productivity Growth, G-7 Countries

Percent, Annual Rate

Source: Conference Board, Total Economy Database; CEA calculations.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

11

industrialization inducing many Americans to move to new communities where they could 

acquire new skills and put their time to new uses. Even during these periods of great 

technological change, America has maintained high employment. Over long periods, between 90 

and 95 percent of the people in the United States who want a job at a given point in time can find 

one, and the unemployment rate is currently less than 5 percent.17

Historical Effects of Technical Change

Technological advances have historically had varied impacts on the labor market. New 

technologies may substitute for some skills while complementing others, and these trends change 

over time.

18 At times, new technologies have raised the productivity and increased employment 

opportunities for workers with little education, and other times for workers with more. To 

illustrate the diversity of potential impacts and provide a framework for understanding today, this 

section discusses historical examples of how innovations affected workers in different ways.

The 19th century was characterized by technological change that raised the productivity of lowerskilled workers and reduced the relative productivity of certain higher-skilled workers.

19 This 

kind of innovation has been called unskill-biased technical change. Highly-skilled artisans who 

controlled and executed full production processes saw their livelihoods threatened by the rise of 

mass production technologies that used assembly lines with interchangeable parts and lowerskilled workers. In reaction, some English textile weavers participated in the Luddite Riots of the 

early 1800s by destroying looms and machinery that threatened to undercut their highly-skilled, 

highly-paid jobs with lower-wage roles. Ultimately, the protesters’ fears came true, and many 

skilled crafts were replaced by the combination of machines and lower-skill labor. There were 

also new opportunities for less-skilled workers and output per hour rose. As a result, average 

living standards could rise, but certain high-skill workers were no longer as valuable in the 

market.

Technological change tended to work in a different direction throughout the late 20th century. 

The advent of computers and the internet raised the relative productivity of higher-skilled 

workers, an example of skill-biased technical change. Routine-intensive occupations that 

focused on predictable, easily-programmable tasks—such as switchboard operators, filing clerks, 

travel agents, and assembly line workers—have been particularly vulnerable to replacement by 

new technologies.20 Some entire occupations were virtually eliminated and demand for others 

reduced. Indeed, Nir Jaimovich and Henry Siu argue that the decline in manufacturing and other 

 

17 Bureau of Labor Statistics, Civilian Unemployment Rate, 1948-2016.

18 Daron Acemoglu and David Autor, “Skills, tasks and technologies: Implications for employment and earnings,”

2011, Handbook of labor economics 4(2011): 1043-171, (http://economics.mit.edu/files/5571).

19 Daron Acemoglu, NBER reporter article, 2002; David Hounshell, From the American system to mass production,

1800-1932: The development of manufacturing technology in the United States, Baltimore: JHU Press, 1985; John

A. James and Jonathan S. Skinner, “The Resolution of the Labor-Scarcity Paradox,” The Journal of Economic

History, 45(3): 513-40, 1985 (http://www.jstor.org/stable/2121750?seq=1#page_scan_tab_contents); Joel Mokyr,

“Technological inertia in Economic History.” The Journal of Economic History, 52(2): 325-38, 1992

(http://www.jstor.org/stable/2123111?seq=1#page_scan_tab_contents).

20 David H. Autor, Frank Levy, and Richard J. Murnane, “The Skill Content of Recent Technological Change: An

Empirical Exploration,” Quarterly Journal of Economics 118(4): 1279-1333, 2003.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

12

routine jobs is largely responsible for recent low labor demand for less educated workers.21

Research suggests that technological innovation over this period increased the productivity of 

those engaged in abstract thinking, creative ability, and problem-solving skills and, therefore, is 

partially responsible for the substantial growth in jobs employing such traits.22 Autor, Levy, and 

Murnane find that about 60 percent of the estimated relative demand shift favoring collegeeducated labor from 1970 to 1998 can be explained by the reduced labor input needed for routine 

manual tasks and the increased labor input for non-routine cognitive tasks, which tended to be 

more concentrated in higher-skilled occupations.23 Given that college-educated labor was already 

more highly compensated, shifting demand towards college-educated labor and raising their 

relative pay contributed to rising income inequality.24

Like these past waves of technological advancements, AI-driven automation is setting off labormarket disruption and adjustment. Economic theory suggests that there must be gains from 

innovations, or they would not be adopted. Market forces alone, however, will not ensure that the

financial benefits from innovations are broadly shared.

Market disruptions can be difficult to navigate for many. Recent empirical research highlights

the costs of the adjustment process. In recent decades, U.S. workers who were displaced from 

their jobs—due to, for example, a plant closing or a company moving—experienced substantial 

earnings declines.25 Autor, Dorn, and Hanson find that negative shocks to local economies can 

have substantial negative and long-lasting effects on unemployment, labor force participation, 

and wages.26 Perhaps more significantly, over time, displaced workers’ earnings recover only 

slowly and incompletely. Even ten or more years later, the earnings of these workers remain 

depressed by 10 percent or more relative to their previous wages.27 These results suggest that for 

many displaced workers there appears to be a deterioration in their ability either to match their 

current skills to, or retrain for, new, in-demand jobs. AI-driven automation can act—and in some 

cases has already acted—as a shock to local labor markets that can initiate long-standing 

disruptions. Without some form of transfers and safety net, the economic benefits of AI-driven 

 

21 Nir Jaimovich and Henry E. Siu, “The Trend is the Cycle: Job Polarization and Jobless Recoveries.” NBER

Working Paper No. 18334, 2012 (http://www.nber.org/papers/w18334.pdf); Kerwin Kofi Charles, Erik Hurst, and

Matthew J. Notowidigdo, “Housing Booms, Manufacturing Decline, and Labor Market Outcomes,” Working Paper,

2016 (http://faculty.wcas.northwestern.edu/noto/research/CHN_manuf_decline_housing_booms_mar2016.pdf).

22 Lawrence F. Katz and Kevin M. Murphy, “Changes in Relative Wages, 1963-1987: Supply and Demand Factors,”

Quarterly Journal of Economics, 107(Feb): 35-78, 1992; Daron Acemoglu, “Technical change, inequality, and the

labor market,” Journal of economic literature 40(1): 7-72, 2002; Autor, Levy, and Murnane 2003; Jaimovich and

Siu 2012; David H. Autor and David Dorn, “The Growth of Low-Skill Service Jobs and the Polarization of the US

Labor Market,” American Economic Review 103(5): 1553-97, 2013 (http://www.ddorn.net/papers/Autor-DornLowSkillServices-Polarization.pdf).

23 Autor, Levy, and Murnane, 2003.

24 Katz and Murphy, 1992.

25 Steven J. Davis and Till Von Wachter, “Recessions and the Costs of Job Loss,” Brookings Papers on Economic

Activity, Economic Studies Program, The Brookings Institution, 43(2): 1-72, 2011

(http://www.econ.ucla.edu/workshops/papers/Monetary/Recessions%20and%20the%20Costs%20of%20Job%20Los

s%20with%20Appendix.pdf).

26 David H Autor, David Dorn, and Gordon H. Hanson, “The China Syndrome: Local Labor Market Effects of

Import Competition in the United States,” American Economic Review 103(6): 2121-68, 2013

(http://gps.ucsd.edu/_files/faculty/hanson/hanson_publication_it_china.pdf).

27 Davis and Von Wachter, 2011.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

13

automation may not be shared by all, and some workers, families, and communities may be 

persistently negatively affected.

AI and the Labor Market: The Near Term

Today, it may be challenging to predict exactly which jobs will be most immediately affected by 

AI-driven automation. Because AI is not a single technology, but rather a collection of 

technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the 

economy. Some work tasks will be more easily automated than others, and some jobs will be 

affected more than others.

Some specific predictions are possible based on the current trajectory of AI technology. For 

example, driving jobs and housecleaning jobs are both jobs that require relatively less education 

to perform. Advancements in computer vision and related technologies have made the feasibility 

of fully automated vehicles (AVs), which do not require a human driver, appear more likely, 

potentially displacing some workers in driving-dominant professions. AVs rely upon, among 

other things, capabilities of navigating complex environments, analyzing dynamic surroundings, 

and optimization. Seemingly similar capabilities are required of a household-cleaning robot, for 

which the operational mandate is less specific (i.e. “clean the house,” as opposed to the objective 

of navigating to a specific destination while following a set of given rules and preserving safety). 

And yet the technology that would enable a robot to navigate and clean a space as effectively as 

a human counterpart appears farther off. In the near to medium term, at least, drivers will 

probably be impacted more by automation than will housecleaners. The following sections lay 

out a framework for more general predictions of the effect of AI-driven automation on jobs.

Continued skill-biased technical change?

Recent research suggests that the effects of AI on the labor market in the decade ahead will 

continue the trend toward skill-biased change that computerization and communication 

innovations have driven in recent decades. Researchers differ on the possible magnitude of this 

effect. Carl Frey and Michael Osbourne asked a panel of experts on AI to classify occupations by 

how likely it is that foreseeable AI technologies could feasibly replace them over roughly the 

next decade or two.

28 Based on this assessment of the technical properties of AI, the relationship 

between those properties to existing occupations, and employment levels across occupations, 

they posit that 47 percent of U.S. jobs are at risk of being replaced by AI technologies and 

computerization in this period. Researchers at the Organisation for Economic Cooperation and 

Development (OECD), however, highlighted the point that automation targets tasks rather than 

occupations, which are themselves particular combinations of tasks.29 Many occupations are

likely to change as some of their associated tasks become automatable, so the OECD analysis 

 

28 Carl Frey and Michael Osborne, “The Future of Employment: How Susceptible are Jobs to Computerization,”

Oxford University, 2013

(http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf).

29 Melanie Arntz, Terry Gregory, and Ulrich Zierahn, “The Risk of Automation for Jobs in OECD Countries: A

Comparative Analysis,” OECD Social, Employment and Migration Working Papers No. 189, 2016

(http://www.oecd-ilibrary.org/docserver/download/5jlz9h56dvq7-

en.pdf?expires=1480994298&id=id&accname=guest&checksum=6DC4B241A91EE860DC391585FF43C51C).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

14

concludes that relatively few will be entirely automated away, estimating that only 9 percent of 

jobs are at risk of being completely displaced. If these estimates of threatened jobs translate into 

job displacement, millions of Americans will have their livelihoods significantly altered and 

potentially face considerable economic challenges in the short- and medium-term.

In addition to understanding the magnitude of the overall employment effects, it is also important 

to understand the distributional implications. CEA ranked occupations by wages and found that, 

according to the Frey and Osbourne analysis, 83 percent of jobs making less than $20 per hour 

would come under pressure from automation, as compared to 31 percent of jobs making between 

$20 and $40 per hour and 4 percent of jobs making above $40 per hour (Figure 3a). Furthermore,

the OECD study estimates that less-educated workers are more likely to be replaced by 

automation than highly-educated ones (Figure 3b). Indeed, the OECD study’s authors estimate 

that 44 percent of American workers with less than a high school degree hold jobs made up of 

highly-automatable tasks while 1 percent of people with a bachelor’s degree or higher hold such 

a job. To the degree that education and wages are correlated with skills, this implies a large 

decline in demand for lower-skilled workers and little decline in demand for higher-skilled 

workers. These estimates suggest a continuation of skill-biased technical change in the near term.

Nevertheless, humans still maintain a comparative advantage over AI and robotics in many

areas. While AI detects patterns and creates predictions, it still cannot replicate social or general 

intelligence, creativity, or human judgment. Of course, many of the occupations that use these 

types of skills are high-skilled occupations, and likely require higher levels of education. Further, 

given the current dexterity limits of the robotics that would be needed to implement mass AIdriven automation, occupations that require manual dexterity will also likely remain in demand 

in the near term. 

83

31

4

0

10

20

30

40

50

60

70

80

90

100

Less than 20 Dollars 20 to 40 Dollars More than 40 Dollars

Figure 3a: Share of Jobs with High Probability of Automation, 

by Occupation's Median Hourly Wage

Median Probability of Automation, Percent

Median Hourly Wage in 2010

Source: Bureau of Labor Statistics; Frey and Osborne (2013); CEA calculations.

44

19

8

6

1 0

0

5

10

15

20

25

30

35

40

45

50

Less than

High School

High School

Degree or

Equivalent

Trade

School

Certificate

Associates

Degree

Bachelors

Degree

Graduate

Degree

Figure 3b: Share of Jobs with Highly Automatable Skills, by 

Education

Percent

Source: Arntz, Gregory, and Zierahn (2016) calculations based on the PIAAC 2012.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

15

Box 1: Automated Vehicle Case Study

A helpful case in understanding the types of effects AI may have on productivity and labor 

demand is the development of automated vehicles (AVs). Like other forms of technological 

disruption, AV technology will likely cause disruptions in the labor market as the economy 

adapts to new paradigms. 

CEA estimates that 2.2 to 3.1 million existing part- and full-time U.S. jobs may be threatened 

or substantially altered by AV technology. Importantly, this is not a net calculation—it does 

not include the types of new jobs that may be developed—but rather a tally of currently held 

jobs that are likely to be affected by AI-enabled AV technology. A second caveat is that these 

changes may take years or decades to occur because there will be a further lag between 

technological possibility and widespread adoption.

This estimate of the number of current jobs likely displaced or substantially altered by AVs 

starts by identifying occupations that involve substantial driving and relatively few other 

responsibilities to lead and coordinate others, drawing occupation descriptions from the 

Bureau of Labor Statistics (BLS) and the Occupational Information Network (O*NET). For 

each of these occupations, an analysis of what non-driving tasks the occupation also requires

yields an estimate of the share of jobs that will be displaced: driving jobs that also involve 

less-automatable tasks have a lower chance of disappearing. For example, the job of schoolbus driver mixes both the tasks of driving and of attending to children. This job will not 

disappear, though it may evolve to focus heavily on the task of attending to children. As a 

result, AV technology may replace only a modest share of school-bus driver jobs, but child 

care workers will still be required. On the other hand, non-driving tasks are less important in 

inter-city bus driver jobs, and AV technology will likely replace a large share of these jobs. 

Many jobs involve limited amounts of driving. These jobs are not included in the analysis 

below as individuals in these occupations would likely see a productivity boost, not a threat of 

displacement, as their time allocated to driving is freed up to focus on other critical tasks. 

In addition to occupations identified from BLS and O*NET, CEA’s estimate of jobs 

threatened or likely to be substantially altered by AVs also includes approximately 364,000 

self-employed individuals driving either part- or full-time with ride-sharing services as of 

May 2015 that may find AV technology substituting for their services.

Table 1, below, presents the identified occupation categories. 
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

16

 

30 Calculations based on Hall and Krueger, 2016, (http://www.nber.org/papers/w22843.pdf), who note the number of

active driver-partners for Uber more than doubled every six months in the period 2012-2014, and expect this growth

rate to continue into 2015. CEA uses data from Harris and Krueger (2015)

(http://www.hamiltonproject.org/assets/files/modernizing_labor_laws_for_twenty_first_century_work_krueger_harr

is.pdf) for only Uber and Lyft. Extrapolating to May 2015 with Hall and Krueger’s method, CEA calculates

approximately 324,000 individuals were active driver-partners with Uber in May 2015.

31 Mean hourly earnings reported in Hall and Krueger (2016).

Table 1

Occupation BLS Code # Total Jobs 

(BLS, May 2015)

Mean 

Hourly 

Wage

O*NET Occupation 

Description

Bus Drivers, 

Transit and 

Intercity

53-3021 168,620 $19.31 Drive bus or motor coach, 

including regular route 

operations, charters, and private 

carriage. May assist passengers 

with baggage. May collect fares 

or tickets.

Light Truck or

Delivery 

Services 

Drivers

53-3033 826,510 $16.38 Drive a light vehicle, such as a 

truck or van, with a capacity of 

less than 26,000 pounds Gross 

Vehicle Weight (GVW), 

primarily to deliver or pick up 

merchandise or to deliver 

packages. May load and unload 

vehicle.

Heavy and 

TractorTrailer Truck 

Drivers

53-3032 1,678,280 $20.43 Drive a tractor-trailer 

combination or a truck with a 

capacity of at least 26,000 

pounds Gross Vehicle Weight 

(GVW). May be required to 

unload truck. Requires 

commercial drivers' license.

Bus Drivers, 

School or 

Special Client

53-3022 505,560 $14.70 Transport students or special 

clients, such as the elderly or 

persons with disabilities. 

Ensure adherence to safety 

rules. May assist passengers in 

boarding or exiting.

Taxi Drivers 

and 

Chauffeurs

53-3041 180,960 $12.53 Drive automobiles, vans, or 

limousines to transport 

passengers. May occasionally 

carry cargo. Includes hearse 

drivers.

Self-employed 

Drivers

N/A 364,00030 $19.3531 N/A

As the above occupations involve other, critical non-driving tasks to varying degrees, Table 2, 

below, presents a weighted estimate of jobs threatened or substantially altered by AV 

technology in each occupation. For example, a weight of 0.60 indicates that approximately 60 

percent of jobs in an occupation are threatened. These weights are generated by CEA using 
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

17

detailed job descriptions, case studies, and surveys of existing and planned technologies for 

each occupation. 

Table 2

Occupation # Total Jobs 

(BLS, May 

2015)

Range of 

Replacement 

Weights

Range of # Jobs 

Threatened

Bus Drivers, Transit 

and Intercity

168,620 0.60 – 1.0 101,170 – 168,620

Light Truck or 

Delivery Services 

Drivers

826,510 0.20 – 0.60 165,300 – 495,910

Heavy and TractorTrailer Truck Drivers

1,678,280 0.80 – 1.0 1,342,620 – 1,678,280

Bus Drivers, School or 

Special Client

505,560 0.30– 0.40 151,670 – 202,220

Taxi Drivers and 

Chauffeurs

180,960 0.60 – 1.0 108,580 – 180,960

Self-employed drivers 364,000 0.90 – 1.0 328,000 – 364,000

TOTAL JOBS 3,723,930 2,196,940 – 3,089,990

AV technology could enable some workers to focus time on other job responsibilities, 

boosting their productivity, and actually fostering wage growth among those still holding the 

reshaped jobs. For example, salespeople who currently spend a considerable amount of time 

driving could find themselves able to do other work while a car drives them from place to 

place, or inspectors and appraisers could fill out paperwork while their car drives itself. This 

should make these workers more productive, with AV technology serving as a complement, 

not a substitute. New jobs will also likely be created, both in existing occupations—cheaper 

transportation costs will lower prices and increase demand for goods and all the related 

occupations such as service and fulfillment—and in new occupations not currently 

foreseeable. Conversely, even occupations that have little connection to driving and divorced 

from the threat of automation could face pressure on wages due to an increased supply of 

similar, displaced workers. 

In cases where downward pressure on wages or consolidation result in displacement of 

workers, private-sector solutions and public policy should aim to ensure smooth and quick 

transitions to new opportunities for these workers. CEA analysis finds that a share of workers 

in a few isolated occupations—truck drivers and delivery service drivers, in particular—

currently enjoy a wage premium over others in the labor market with the same level of 

educational attainment. They may not be able to regain this wage premium if displaced 
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

18

What kind of jobs will AI create?

Predicting future job growth is extremely difficult, as it depends on technologies that do not exist 

today and the multiple ways they may complement or substitute for existing human skills and 

jobs. To form an intelligent guess about what these jobs might be like, CEA has synthesized and 

extended existing research on jobs that would be directly created by AI. It is important to 

understand, however, that AI will also lead to substantial indirect job creation—to the degree it 

raises productivity and wages it may also lead to higher consumption that would support 

additional jobs across the economy in everything from high-end craft production to restaurants 

and retail.

CEA has identified four categories of jobs that might experience direct AI-driven growth in the 

future. Employment in areas where humans engage with existing AI technologies, develop new 

AI technologies, supervise AI technologies in practice, and facilitate societal shifts that 

accompany new AI technologies will likely grow. Current limits on manual dexterity of robots 

and constraints on generative intelligence and creativity of AI technologies likely mean that 

employment requiring manual dexterity, creativity, social interactions and intelligence, and 

general knowledge will thrive. Below are descriptions and potential examples of future 

employment for each category. 

Engagement. Humans will likely be needed to actively engage with AI technologies throughout

the process of completing a task. Many industry professionals refer to a large swath of AI 

technologies as “Augmented Intelligence,” stressing the technology’s role as assisting and 

expanding the productivity of individuals rather than replacing human work. Thus, based on the 

biased-technical change framework, demand for labor will likely increase the most in the areas 

where humans complement AI-automation technologies. For example, AI technology such as 

IBM’s Watson may improve early detection of some cancers or other illnesses, but a human 

healthcare professional is needed to work with patients to understand and translate patients’ 

symptoms, inform patients of treatment options, and guide patients through treatment plans. 

Shipping companies may also partner workers who pickup and deliver goods over the last 100 

feet with AI-enabled autonomous vehicles that move workers efficiently from site to site. In such 

cases, AI augments what a human is able to do and allows individuals to either be more effective 

in their specialty task or to operate on a larger scale.

Development. In the initial stages of AI, development jobs are crucial and span multiple 

industries and skill levels. Most intuitively, there may be a great need for highly-skilled software 

developers and engineers to put these capacities into practical use in the world. To a certain 

extent, however, AI is only as good as the data behind it, so there will likely be increased 

demand for jobs in generating, collecting, and managing relevant data to feed into AI training 

without intervention to help them re-skill. Job search assistance, education, training and 

apprenticeships to build and certify new skills, and wage insurance could provide valuable 

support to them as they transition to finding new jobs.

Policy responses to these challenges are discussed later in this report.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

19

processes. Applications of AI can range from high-skill tasks such as recognizing cancer in x-ray 

images to lower-skill tasks such as recognizing text in images. Finally, to an increasing degree, 

development may include those specializing in the liberal arts and social sciences, such as 

philosophers with frameworks for ethical evaluations and sociologists investigating the impact of 

technology on specific populations, who can give input as the new technologies grapple with 

more social complexities and moral dilemmas. 

Supervision. This category encompasses all roles related to the monitoring, licensing, and repair 

of AI. For example, after the automated vehicle development phase, the need for human 

registration and testing of such technology to ensure safety and quality control on the roads will 

still likely exist. As a widespread new technology, AV will require regular repair and 

maintenance, which may expand mechanic and technician jobs in this space as well. Real-time 

supervision will also be required in exceptional, marginal, or high-stakes cases, especially those 

involving morality, ethics, and social intelligence that AI may lack. This might take the form of 

quality control of recommendations made by AI or online moderation when sensitive subjects 

are discussed. The capacity for AI-enabled machines to learn is one of the most exciting aspects 

of the technology, but it may also require supervision to ensure that AI does not diverge from 

originally intended uses. As machines get smarter and have improved ability to make practical 

predictions about the environment, the value of human judgement will increase because it will be 

the preferred way to resolve competing priorities.32

Response to Paradigm Shifts. The technological innovation surrounding AI will likely reshape 

features of built environment. In the case of AVs, dramatic shifts in the design of infrastructure 

and traffic laws—which are currently built with the safety and convenience of human drivers in 

mind—may be needed. The advent of self-driving cars may result in higher demand for urban 

planners and designers to create a new blueprint for the way the everyday travel landscape is 

built and used. Paradigm shifts in adjacent fields such as cybersecurity—demanding, for 

instance, new methods of detecting fraudulent transactions and messages—may also necessitate 

new occupations and more employment.

 

32Ajay Agrawal, Joshua Gans, and Avi Goldfarb, “The Simple Economics of Machine Intelligence,” Harvard

Business Review, November 17, 2016 (https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

20

Box 2: The End of Work?

In addition to the arguments that AI and future technologies will broadly follow the same path 

as past technological revolutions, others make a more radical argument about the possible 

longer-run effects. They posit that AI could prove different from previous technological 

change because it has the ability to replicate something previously exclusive to humans: 

intelligence. There have long been fears that technology—the machines, the assembly lines, or 

the robots—would replace all human labor, but AI-driven automation has unique features that 

may allow it to replace substantial amounts of routine cognitive tasks in which humans 

previously maintained a stark comparative advantage. Initial waves of technology, such as the 

wheel and lever, allowed humans to do more by replacing or augmenting physical strength. 

Other processes allowed work to take place faster or more efficiently in a factory. Computers 

allowed calculations or pattern recognition to take place faster and augment humans’ capacity 

to think or reason. 

AI, though, may allow machines to operate without humans to such a degree that they 

fundamentally change the nature of production and work.33 It may be that the question is no 

longer which segment of the population will technology complement, but whether the new 

technology will complement many humans at all, or if AI will substitute completely for much 

of human work. The skills in which humans have maintained a comparative advantage are 

likely to erode over time as AI and new technologies become more sophisticated. Some of this

is evident today as AI becomes more capable at tasks such as language processing, translation, 

basic writing, or even music composition.

AI-driven technological change could lead to even larger disparities in income between 

capital owners and labor. For example, Brynjolfsson and McAfee argue that current trends in 

the labor market, such as declining wages in the face of rising productivity, are indicative of a 

more drastic change in the distribution of economic benefits to come. Rather than everyone 

receiving at least some of the benefit, the vast majority of that value will go to a very small 

portion of the population: “superstar-biased technological change.” Superstar-biased 

technological change is somewhat similar to skill-biased technological change, but the 

benefits of technology accrue to an even smaller portion of society than just the highly-skilled 

workers. The winner-take-most and winner-take-all nature of the information technology 

market means that the fortunate few are likely to emerge as victors of the market. This would 

exacerbate the current trend in the rising fraction of total income going to the top 0.01 percent 

(Figure 4). 

 

33 Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of

Brilliant Technologies, WW Norton & Company, 2014.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

21

In theory, AI-driven automation might involve more than temporary disruptions in labor 

markets and drastically reduce the need for workers. If there is no need for extensive human 

labor in the production process, society as a whole may need to find an alternative approach to 

resource allocation other than compensation for labor, requiring a fundamental shift in the 

way economies are organized. 

Although this scenario is speculative, it is included in this report to foster discussion and shed 

light on the role and value of work in the economy and society. Ultimately, AI may develop in 

the same way as the technologies before it, creating new products and new jobs such that the 

bulk of individuals will be employed as they are today.34

Technology is Not Destiny—Institutions and Policies Are Critical

A key determinant of how AI-induced technological change will affect people in the future is the 

ability of workers to extract the benefits of their increased productivity. For decades after World 

War II, the share of income going to the bottom 90 percent of workers was roughly unchanged. 

But since the late 1970s, the bottom 90 percent of households have seen their income fall from 

two-thirds of the total to about one half of the total share of U.S. income. For much of this 

period, moreover, productivity growth did not translate to higher real wages for low-income and 

even middle-income American workers.

This reduced share of income is partly the result of the fact that labor compensation is being 

increasingly unevenly distributed. But since 2000, it is also because the distribution of benefits 

going to capital and labor have also been diverging. Starting in about 2000, corporate profits as a 

share of GDP (a measure of the capital share of GDP) started to increase and labor share of GDP 

 

34 For more discussion, see John W. Budd, The Thought of Work, Cornell University Press, 2011.

1920s high

0

1

2

3

4

5

6

7

1913 1923 1933 1943 1953 1963 1973 1983 1993 2003 2013

Figure 4: Top 0.01% Income Share, 1913-2015

Share of income going to top 0.01% (including capital gains)

Source: Piketty and Saez (2003), data update as of June 2016.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

22

began decreasing (Figure 6). The labor share of GDP reached a historical low, though it has 

trended up somewhat over the last 2 years.

How AI and AI-driven automation will shape the distribution of gains in coming years depends 

on non-technical factors including aspects of both the broader economy and policy institutions. 

First, the direction of innovation is not a random shock to the economy but the product of 

decisions made by firms, governments, and individuals. Economic factors can drive the direction 

of technological change. Second, there is a role for policy to help amplify the best effects of 

automation and temper the worst. 

Technological advancement is generated and adopted into the economy as the product of choices 

of entrepreneurs, workers, and firms looking to better serve a market or streamline a production 

process, in the context created by public investments in basic and applied research, 

infrastructure, and other public goods. In a process of directed technical change, incentives draw 

investment towards more potentially profitable innovations and so the types of technological 

change that are likely to occur, among those which are technologically feasible, are those which 

are most profitable.35 Research examining firms’ decisions to innovate argues that the tendency 

towards unskill-biased technical change in the 1800s came about because it was profitable to 

create technologies that replaced expensive and scarce resources (skilled artisans) with relatively 

cheap and abundant resources (machines and low-skilled workers). 

In contrast, research suggests that skill-biased technical change of the 1990s was a function of 

increases in the supply of educated workers, which made innovations that raised their 

productivity more profitable because they could be used widely.36 The Frey and Osborne study

 

35 Daron Acemoglu, “Directed Technological Change, Review of Economic Studies 69(4): 781-809, 2003

(http://restud.oxfordjournals.org/content/69/4/781.short).

36 Acemoglu, 2003.

Corporate Profits 

(left axis)

Non-Farm Labor 

Share (right axis)

55

57

59

61

63

65

67

0

2

4

6

8

10

12

14

1950 1960 1970 1980 1990 2000 2010

Figure 5: Non-Farm Labor and Corporate Profits Share of GDP,

% of GDP 1950-2016

Source: Bureau of Economic Analysis, Bureau of Labor Statistics; CEA calculations

% of GDP
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

23

and the OECD study suggest that this trend of skill-biased technical change may continue with 

AI, as the most automatable occupations tend to be low-wage and low-skill.

On the other hand, the wage premium for higher-skilled labor has increased over time (Figure 6), 

which creates a countervailing incentive to invest in innovations that might raise the productivity 

of lower-skill, lower-price workers. For example, automation technology that embodies expertise 

in medical imaging hardware and software allows middle-skill personnel to make medical 

diagnosis, reducing demand for more-expensive specialists. Similarly, the introduction of 

proprietary tax preparation software has allowed less-skilled tax preparers to replace certified 

accountants in some situations. In both cases, the demand for high-skill computer programmers 

also increases slightly but their work diffuses widely and scales cheaply. Thus technological 

change does not happen in a vacuum. The trajectory of AI may shift and change depending on 

non-technical, competitive incentives.

Policy plays a large role in shaping the effects of technological change. Therefore, even if Frey 

and Osborne’s predictions that almost 50 percent of occupations are threatened by new 

automation technologies are accurate, the labor market impacts also depend on a country’s

institutions and policies. While relative wages depend on the demand for different levels of skill, 

which is partially a function of technology, wages also depend on the supply of labor at various 

skill levels, which is influenced by the distribution of educational opportunity and attainment.37

Relative wages also depend on collective bargaining,38 minimum wage laws, and other 

institutions and policies that affect wage setting. 

Over the last 4 decades, other major advanced countries have experienced technological changes 

similar to the United States, yet the United States has seen both a greater increase in income 

inequality and higher overall levels of inequality, as shown in Figure 7. While most other 

 

37 Claudia Goldin and Lawrence F. Katz, The Race between Education and Technology, 2008.

38 Bruce Western and Jake Rosenfeld, “Unions, norms, and the rise in US wage inequality,” American Sociological

Review 76(4): 513-37, 2011.

College 

Earnings 

Premium

2015

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 6: College Earnings Premium Over Time

Earnings Ratio

Note: The earnings ratio compares the median full-time, full-year worker over age 25 

with a bachelor’s degree only to the same type of worker with just a high school degree. 

Prior to 1992, bachelor's degree is defined as four years of college.

Source: CPS ASEC.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

24

advanced economies have seen declines in prime-age male labor force participation, moreover, 

the decline in the United States has been steeper than in almost every other advanced economy, 

as shown in Figure 8. 

If the difference between the United States and the other countries in inequality and labor force 

participation pictured above cannot be explained by the rate of technological change and which 

tasks are automatable, then it suggests that differences in a country’s policies and institutions 

may mediate these changes. For example, other countries tend to invest far more resources on 

active labor market programs that help workers navigate job transitions, such as training 

programs and job-search assistance. While OECD member countries outside of the United States

spent, on average, 0.6 percent of GDP on active labor market policies in 2014, spending by the 

2015

4

6

8

10

12

14

16

18

20

1975 1980 1985 1990 1995 2000 2005 2010 2015

United States United Kingdom

Canada France

Italy Japan

Germany

Figure 7: Share of Income Earned by Top 1 Percent, 1975-2015

Percent

Source: World Wealth and Income Database.

70

75

80

85

90

95

100

Israel

Italy

United States

Norway

Finland

Ireland

Turkey

Australia

Denmark

Poland

Canada

Belgium

Hungary

Korea

Austria

Portugal

Chile

Estonia

Netherlands

Slovenia

United Kingdom

New Zealand

Spain

Germany

Iceland

Greece

France

Sweden

Slovak Republic

Mexico

Luxembourg

Japan

Czech Republic

Switzerland

2014 1990

Figure 8: Prime-Age Male Labor Force Participation Rates Across the OECD

Percent
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

25

United States was just 0.1 percent of GDP (Figure 9). The United States, moreover, now spends 

less than half of what it did on such programs 30 years ago as a share of GDP (Figure 10).

39

It is possible for the economy to generate high levels of employment with more advanced 

automation and higher levels of productivity than there are today. But Federal policies will need 

to play a role in helping Americans to navigate transitions in labor market demand caused by 

changes in technology over time.

 

39 OECD, "Labour market programmes: expenditure and participants", OECD Employment and Labour Market

Statistics (database), 2016 (http://stats.oecd.org/viewhtml.aspx?datasetcode=LMPEXP&lang=en#).

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

Mexico

Chile

United States

Israel

Japan

Estonia

Slovak Republic

Canada

United Kingdom

Australia

New Zealand

Italy

Czech Republic

Slovenia

Korea

Poland

Norway

Spain

Portugal

Switzerland

Luxembourg

Germany

Belgium

Austria

Netherlands

Hungary

Ireland

France

Finland

Sweden

Denmark

Figure 9: Public Expenditure on Active Labor Market Programs (% of GDP)

Percent of GDP

Note: Data for Ireland, Poland, and Spain from 2013; Data for UK for 2011.

Source: OECD Statistics (2014)

2014

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

1985 1990 1995 2000 2005 2010

Figure 10: U.S. Public Expenditure on Active Labor Market 

Programs Over Time as Percent of GDP

Percent of GDP

Source: OECD Statistics (2014)
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

26

Policy Responses

AI-driven automation stands to transform the economy over the coming years and decades. The 

challenge for policymakers will be to update, strengthen, and adapt policies to respond to the 

economic effects of AI.

Although it is difficult to predict these economic effects precisely with a high degree of 

confidence, the economic analysis in the previous chapter suggests that policymakers should 

prepare for five primary economic effects:

 Positive contributions to aggregate productivity growth;

 Changes in the skills demanded by the job market, including greater demand for higherlevel technical skills;

 Uneven distribution of impact, across sectors, wage levels, education levels, job types, 

and locations;

 Churning of the job market as some jobs disappear while others are created; and

 The loss of jobs for some workers in the short-run, and possibly longer depending on 

policy responses.

There is substantial uncertainty about how strongly these effects will be felt, and how rapidly 

they will arrive. It is possible that AI will not have large, new effects on the economy, such that

the coming years are subject to the same basic workforce trends seen in recent decades—some 

which are positive, and others which are worrisome and may require policy changes. At the other 

end of the range of possibilities, the economy might potentially experience a larger shock, with 

accelerating changes in the job market, and significantly more workers in need of assistance and 

retraining as their skills are no longer valued in the job market. Given presently available 

evidence, it is not possible to make specific predictions, so policymakers must be prepared for a 

range of potential outcomes. At a minimum, some occupations such as drivers and cashiers are 

likely to face displacement from or restructuring of their current jobs, leading millions of 

Americans to experience economic hardship in the short-run absent new policies.

Because the effects of AI-driven automation will likely be felt across the whole economy, and 

the areas of greatest impact may be difficult to predict, policy responses must be targeted to the 

whole economy. In addition, the economic effects of AI-driven automation may be difficult to 

separate from those of other factors such as other technological changes, globalization, reduction 

in market competition and worker bargaining power, and the effects of past public policy 

choices. Even if it is not possible to determine how much of the current transformation of the 

economy is caused by each of these factors, the policy challenges raised by the disruptions

remain, and require a broad policy response.

In the cases where it is possible to direct mitigations to particular affected places and sectors, 

those approaches should be pursued. But more generally, this report suggests and discusses

below three broad strategies for addressing the impacts of AI-driven automation across the whole 

U.S. economy:
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

27

1. Invest in and develop AI for its many benefits;

2. Educate and train Americans for jobs of the future; and

3. Aid workers in the transition and empower workers to ensure broadly shared growth.

Strategy #1: Invest In and Develop AI for its Many Benefits

If care is taken to responsibly maximize its development, AI will make important, positive 

contributions to aggregate productivity growth, and advances in AI technology hold incredible 

potential to help America stay on the cutting edge of innovation. Indeed, CEA Chair Jason 

Furman has written that his biggest worry about AI is “that we do not have enough [of it].”40 AI 

technology itself has opened up new markets and new opportunities for progress in critical areas 

such as health, education, energy, economic inclusion, social welfare, transportation, and the 

environment. Substantial innovation in AI, robotics, and related technology areas has taken place 

over the last decade, but the United States will need a much faster pace of innovation in these 

areas to significantly advance productivity growth going forward.41 With the right investment in 

AI and policies to support a larger and more diverse AI workforce, the United States has the 

potential to accelerate productivity and maintain the strategic advantages that result from 

American leadership in AI.

42

Invest in AI research and development

Government has an important role to play in advancing the AI field by investing in research and 

development. Throughout the public outreach on AI conducted by OSTP, government officials 

heard calls from business leaders, technologists, and economists for greater government 

investment in AI research and development. Leading researchers in AI were optimistic about 

sustaining the recent rapid progress in AI and its use in an ever wider range of applications. A 

strong case can be made in favor of increased Federal funding for research in AI.

The Administration published its Artificial Intelligence Research and Development Strategic 

Plan in October 2016, laying out a detailed strategy and roadmap for government-funded AI 

research and development. 

Develop AI for cyberdefense and fraud detection

Currently, designing and operating secure systems requires a large investment of time and 

attention from experts. Automating this expert work, partially or entirely, may enable strong 

security across a much broader range of systems and applications at dramatically lower cost, and 

 

40 Jason Furman, “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence.” Remarks at

AI Now: The Social and Economic Implications of Artificial Intelligence Technologies in the Near Term. New York

University, New York, July 7, 2016.

(https://www.whitehouse.gov/sites/default/files/page/files/20160707_cea_ai_furman.pdf).

41 Ibid.

42 More detailed policy recommendations for investments in AI research and development, and development of the

AI workforce, see the previous Administration report, Preparing for the Future of Artificial Intelligence

(https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_o

f_ai.pdf), and the accompanying Artificial Intelligence Research and Development Strategic Plan

(https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

28

may increase the agility of cyber defenses. Using AI may help maintain the rapid response 

required to detect and react to the landscape of ever evolving cyber threats. There are many 

opportunities for AI and specifically machine-learning systems to help cope with the sheer 

complexity of cyberspace and support effective human decision making in response to 

cyberattacks. 

Future AI systems could perform predictive analytics to anticipate cyberattacks by generating 

dynamic threat models from available data sources that are voluminous, ever-changing, and often 

incomplete. These data include the topology and state of network nodes, links, equipment, 

architecture, protocols, and networks. AI may be the most effective approach to interpreting 

these data, proactively identifying vulnerabilities, and taking action to prevent or mitigate future 

attacks. Results to-date in DARPA’s Cyber Grand Challenge competition demonstrate the 

potential of this approach.43 The Cyber Grand Challenge was designed to accelerate the 

development of advanced, autonomous systems that can detect, evaluate, and patch software 

vulnerabilities before adversaries have a chance to exploit them. The Cyber Grand Challenge

Final Event was held on August 4, 2016. To fuel follow-on research and parallel competition, all 

of the code produced by the automated systems during the Cyber Grand Challenge Final Event

has been released as open source to allow others to reverse engineer it and learn from it.

AI also has important applications in detecting fraudulent transactions and messages. AI is 

widely used in the industry to detect fraudulent financial transactions and unauthorized attempts 

to log in to systems by impersonating a user. AI is used to filter email messages to flag spam, 

attempted cyberattacks, or otherwise unwanted messages. Search engines have worked for years 

to maintain the quality of search results by finding relevant features of documents and actions, 

and developing advanced algorithms to detect and demote content that appears to be unwanted or 

dangerous. In all of these areas, companies regularly update their methods to counter new tactics 

used by attackers and coordination among attackers.

Companies could develop AI-based methods to detect fraudulent transactions and messages in 

other settings, enabling their users to experience a higher-quality information environment. 

Further research is needed to understand the most effective means of doing this.

Develop a larger, more diverse AI workforce

The rapid growth of AI has dramatically increased the need for people with relevant skills to 

support and advance the field. The AI workforce includes AI researchers who drive fundamental 

advances in AI and related fields, a larger number of specialists who refine AI methods for 

specific applications, and a great number of users who operate those applications in specific 

settings. For researchers, AI training is inherently interdisciplinary, often requiring a strong 

background in computer science, statistics, mathematical logic, and information theory.44 For 

 

43 Cyber Grand Challenge (https://www.cybergrandchallenge.com).

44 There is also a need for the development of a strong research community in fields outside of technical disciplines

related to AI, to examine the impacts and implications of AI on economics, social science, health, and other areas of

research.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

29

specialists, training typically requires a background in software engineering as well as in the 

application area. For users, familiarity with AI technologies is needed to apply them reliably. 

All sectors face the challenge of how to diversify the AI workforce. The lack of gender and racial 

diversity in the AI-specific workforce mirrors the significant and problematic lack of diversity in 

the technology industry and the field of computer science more generally. Unlocking the full 

potential of the American people, especially in entrepreneurship, as well as science, technology, 

engineering, and mathematics (STEM) fields, has been a priority of the Administration. The 

importance of including individuals from diverse backgrounds, experiences, and identities, 

especially women and members of racial and ethnic groups traditionally underrepresented in 

STEM, is one of the most critical and high-priority challenges for computer science and AI.

Research has shown that diverse groups are more effective at problem solving than 

homogeneous groups, and policies that promote diversity and inclusion will enhance our ability 

to draw from the broadest possible pool of talent, solve our toughest challenges, maximize 

employee engagement and innovation, and lead by example by setting a high standard for 

providing access to opportunity to all segments of our society.45

Policymakers also will need to address new potential barriers stemming from any algorithmic 

bias. Firms are beginning to use consumer data, including data sets collected by “third-party”

data services companies, to determine individuals’ fitness for credit, insurance and even 

employment. Other firms are pioneering the use of games, simulations, and electronic tests to 

determine the “fit” of job applicants to a team. To do this, automated algorithms may be trained 

with data about successful team members in order to look for applicants that resemble them. One 

important benefit of these innovations is enabling companies to recruit and hire candidates based 

on demonstrated skills and abilities rather than pedigree, which will become even more critical 

as people gain skills on the job. But if such training sets are based on a less diverse current 

workforce, the biases of the existing group may be built into the resulting decisions and may 

unfairly exclude new potential talent. While employment laws and the Fair Credit Reporting Act 

currently impose certain restrictions on the use of credit history in making employment 

decisions, additional statutory or regulatory protections may be need to be explored in this space.

The previous report, Preparing for the Future of Artificial Intelligence, discusses workforce 

needs, including the strong case for increasing diversity, and lays out a detailed plan for AI 

workforce development.46

Support market competition

Competition from new and existing firms has always played an important role in the creation and 

adoption of new technologies and innovations, and this is no different in the case of AI. Startups 

 

45 Aparna Joshi and Hyntak Roh, “The Role of Context in Work Team Diversity Research: A Meta-Analytic

Review,” Academy of Management Journal 52: 599-627, 2009; Vivian Hunt, Dennis Layton, and Sara Prince, “Why

Diversity Matters,” McKinsey & Company, 2015 (http://www.mckinsey.com/business-functions/organization/ourinsights/why-diversity-matters).

46 The White House, Preparing for the Future of Artificial Intelligence,

(https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_o

f_ai.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

30

are a critical pathway for the commercialization of innovative new ideas and products. Startups, 

or the possibility of entry by a startup, also incentivize established firms to innovate and reduce 

costs. Competition pushes firms to invest in new technologies that help to lower costs, and also 

to invest in innovations that can lead to improvements in the quality of existing products. 

The rapid evolution of technology can pose challenges for developing sound pro-competition 

policies, both in terms of defining the scope of the market as well as assessing the degree of 

contestability or the possibilities for disruption. For example, while it is probably too early to 

assess the role of AI in competition policy, one might imagine that when a large incumbent has 

access to most of the customer data in the market, it is able to use AI to refine its products better 

than any potential entrant could hope, and can thereby effectively foreclose entry. 

Strategy #2: Educate and Train Americans for Jobs of the Future

As AI changes the nature of work and the skills demanded by the labor market, American 

workers will need to be prepared with the education and training that can help them continue to 

succeed. If the United States fails to improve at educating children and retraining adults with the 

skills needed in an increasingly AI-driven economy, the country risks leaving millions of 

Americans behind and losing its position as the global economic leader.

The United States led the world in economic gains from the industrial revolution in part due to 

major investments in its workforce. In the 20th century, America shifted from a mostly agrarian 

economy to an industrial economy. During this period, as today, the type of work and skills 

required to do work underwent a major transformation. To meet the needs of the new economy, 

the United States rapidly expanded access to education through high school, and by 1930 

America was far ahead of European countries in terms of widely available, free, and publicallyprovided secondary education.47 The average American born in 1951 had 6.2 more years of 

schooling than an American born in 1876. These increases in schooling led to tangible economic 

gains: economists estimate that educational attainment explains 14 percent of annual increases in 

labor productivity during that period.48 Americans used education to adapt to new jobs which 

could not even have been imagined in prior decades. Today’s AI-driven transformation will 

likely require similar realignments. College- and career-ready skills in math, reading, computer 

science, and critical thinking are likely to be among the factors in helping workers successfully 

navigate through unpredictable changes in the future labor market. Providing the opportunity to 

obtain those skills will be a critical component of preparing children for success in the future.49

Educate youth for success in the future job market

A key step towards preparing individuals for the economy of the future is providing quality 

education opportunities for all. 

 

47 Ajay Chaudry, “The Case for Early Education in the Emerging Economy,” Roosevelt Institute-The New American

Economy’s Learning Series, August 2016, p. 3 (http://rooseveltinstitute.org/wp-content/uploads/2016/08/The-Casefor-Early-Education-in-the-Emerging-Economy.pdf).

48 Claudia Dale Goldin and Lawrence Katz, The Race between Education and Technology, 2008.

49 The White House, Economic Report of the President 2016, Chapter 4.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

31

While, in the past, many jobs paying decent wages could be done with low levels of skill, 

continuing changes in technology, including AI, will make such jobs less common in the future. 

While it is unclear exactly how progress in AI and other technologies will affect the jobs of the 

future, policymakers must address the low levels of proficiency in basic math and reading for 

millions of Americans. Despite strong progress over the past 8 years, the United States is still 

falling behind rather than leading the world in key dimensions for successfully navigating this 

transition. Children from low-income families start kindergarten over 1 year behind peers in 

language skills.

50 Student performance in mathematics in China is on average 2 years above U.S. 

students.51 American students from lower socioeconomic backgrounds score 15 percent lower on 

international assessments than higher income peers.52 And year after year, a stubborn gap 

persists between how well white students are doing compared to their African American and 

Latino classmates.

53

 

For all students, coursework in STEM, and specifically in areas such as computer science, will 

likely be especially relevant to work and citizenship in an increasingly AI-driven world. To 

respond to these shifts, the United States must make real investments in high-quality education, 

at all levels of education. 

All Children Get Off to the Right Start with Access to High-Quality Early Education

In a world of AI-driven skill-biased technological change, people with low levels of even basic 

skills such as reading and math are at higher risk of displacement. On average, children from 

poor families score far below their peers from higher-income families in early vocabulary and 

literacy development, in early math, and in the social skills they need to get along well in their 

classrooms.54 Studies indicate that kids who start off with deficits in basic skills fail to catch up 

to peers by later grades.55 Therefore, it is all the more important that the United States make key 

investments in getting kids from all income backgrounds off to the right start. To achieve these 

goals, the United States must catch up to the rest of the world in pre-school enrollment—the 

United States is ranked 28

th out of 38 OECD countries in the share of 4-year-olds enrolled in 

early education programs.56 

 

50 U.S. Department of Education, “A Matter of Equity: Preschool in America,” April 2015

(https://www2.ed.gov/documents/early-learning/matter-equity-preschool-america.pdf).

51 OECD, “Program for International Student Assessment 2012 Results (https://www.oecd.org/unitedstates/PISA2012-results-US.pdf).

52 Ibid.

53 The White House, “Remarks of the President to the United States Hispanic Chamber of Commerce,” March 2009

(https://www.whitehouse.gov/the-press-office/remarks-president-united-states-hispanic-chamber-commerce).

54 U.S. Department of Education, “A Matter of Equity: Preschool in America,” April 2015

(https://www2.ed.gov/documents/early-learning/matter-equity-preschool-america.pdf).

55 Connie Juel, “Learning to read and write: A longitudinal study of 54 children from first through fourth grades,”

Journal of Educational Psychology 80.4:437, 1988; David J. Francis, et al., “Developmental lag versus deficit

models of reading disability: A longitudinal, individual growth curves analysis,” Journal of Educational

Psychology, 88.1:3, 1996.

56 OECD, “Education at a Glance: OECD Indicators 2012,” Country Note: United States, 2012

(https://www.oecd.org/unitedstates/CN%20-%20United%20States.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

32

All Students Graduate from High School College- and Career-Ready

Students are much better positioned for jobs that benefit from AI instead of being replaced by it 

if they graduate from high school with the necessary skills. In the Obama Administration, the 

U.S. high school graduation rate has reached a record high of 83 percent for the 2014-2015 

school year.57 Too many students, however, are not college- and career-ready when they finish 

high school. According to the National Assessment of Education Progress, the largest nationally 

representative and continuing assessment of what America’s students know and can do, fewer

than 40 percent of graduating students scored at college- and career-ready levels in 2013.

58 A 

strategy to dramatically speed up school improvement to keep pace with the accelerating rate of 

change in the global economy should include attracting and retaining the best teachers, making 

sure all schools have the resources required for success, and holding all students to high 

standards with rigorous coursework.

Additionally, it will require building on the President’s Computer Science for All initiative, 

which seeks to give all students at the K-12 level access to coursework in computing and 

computational thinking. A bipartisan coalition of governors, mayors, and other public- and 

private-sector leaders has supported the creation of new standards, courses, and investments in 

teacher professional development, as well as supplementary extracurricular programs and 

resources to make this a reality. Further effort is needed to make computer science education 

available to all children.

The United States has made unprecedented progress ensuring more schools and libraries can 

access the digital tools to dramatically improve educational outcomes, particularly in delivering 

technology skills. From 2013 to 2015, 20 million more students gained access to high-speed 

broadband and wireless in schools, halving the connectivity divide, and the United States is on 

track to connect 99 percent of students in the near future. But while significant progress has been 

made, there is still more work to be done to ensure that all schools, libraries, and homes have 

access to broadband-enabled devices. And educators will need more support and professional 

development to deliver high-quality learning tailored to their students’ needs. 

All Americans Have Access to an Affordable Post-Secondary Education that Prepares 

Them for Good Jobs 

Projections show that in the coming years nearly three-quarters of the fastest growing 

occupations will require at least some postsecondary education beyond high school.59 Despite

these growing needs, state funding for higher education is down by 18 percent on average since 

the start of the recession, and tuition at four-year public colleges is up by 33 percent since the 

 

57 National Center for Education Statistics, “The Condition of Education: Public High School Graduation Rates,”

May 2016.

58 U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National

Assessment of Educational Progress (NAEP), 2013 Mathematics and Reading Assessments.

59 Bureau of Labor Statistics, “Employment Projections—2014-24,” December 2015 

(http://www.bls.gov/news.release/pdf/ecopro.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

33

2007-2008 school year.60 To further improve college access, affordability, and completion, the 

President has proposed making 2 years of community college free for hard-working students

through America’s College Promise. If all states participated in America’s College Promise, an 

additional 9 million students could benefit.61 This could be critical for workers seeking to gain 

new skills as a response to, or in order to avoid, dislocation from AI-driven automation.

Expand access to training and re-training

A commitment to preparing Americans to adapt to continuous and rapid technological change in 

the future, whether in AI or other fields, requires pursuing policy changes that would 

significantly expand the availability of high-quality job training to meet the scale of need; help 

people more successfully navigate job transitions; and target resources to programs that are 

producing strong results. But despite the clear challenges facing U.S. workers, the current level 

of investment in active labor market policies, such as training programs, by the United States is 

low by both international and historical standards. Through the Workforce Innovation and 

Opportunity Act—the Federal Government’s largest job training investment program—only 

about 175,000 people are trained per year.62 As shown above in Figures 9 and 10, while the 

member countries of the OECD spent, on average, 0.6 percent of GDP on active labor market 

policies in 2014, spending by the United States was just 0.1 percent of GDP. Relative to the 

overall economy, the United States now spends less than half of what it did on such programs 30 

years ago. 

The steps described below should be taken to put the United States ahead of, rather than behind,

the curve in these critical areas.

Significantly Expand Availability of Job-Driven Training and Lifelong Learning to Meet 

the Scale of the Need

Increasing funding for job training by six-fold—which would match spending as a percentage of 

GDP to Germany, but still leave the United States far behind other European countries—would 

enable retraining of an additional 2.5 million people per year.63 Over the last 8 years, the 

Administration has taken important steps to move in this direction. The Administration has made 

investments in half of community colleges in the country to create job-driven training programs 

in fields such as healthcare, information technology, energy and other in-demand fields that have 

trained nearly 300,000 people so far. The Administration also launched the POWER Initiative, a 

 

60 Center on Budget and Policy Priorities, “Funding Down, Tuition Up: State Cuts to Higher Education Threaten

Quality and Affordability at Public Colleges,” August 2016 (http://www.cbpp.org/research/state-budget-andtax/funding-down-tuition-up).

61 The White House, “Fact Sheet—White House Unveils America’s College Promise Proposal: Tuition-Free

Community College for Responsible Students,” January 2015 (https://www.whitehouse.gov/the-pressoffice/2015/01/09/fact-sheet-white-house-unveils-america-s-college-promise-proposal-tuitio).

62 Department of Labor, Employment and Training Administration, 2015.

63 This assumes $6,000 per person training/reemployment cost, and an increase in Workforce Innovation and

Opportunity Act funding from today’s $3B to $18B, to match Germany’s spending as a fraction of GDP, with all

new funding spent on training.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

34

new interagency effort to assist communities negatively impacted by changes in the coal industry 

and power sector with coordinated Federal economic and workforce-development resources.

Target Resources to Effective Education and Training Programs 

Directing funding to training that produces results starts with information about whether 

programs are placing people into in-demand jobs that pay good salaries. Historically, very little 

data on the employment outcomes of education and training institutions has existed. Federal and 

State policymakers should build on Administration initiatives, such as the College Scorecard and 

the Workforce Innovation and Opportunity Act, by continuing to make investments in collecting 

and analyzing data on employment and earnings outcomes to hold programs accountable and 

direct funding to strategies producing results. 

Expand Access to Apprenticeships

Job-driven apprenticeships grow the economy and can provide American workers from all 

backgrounds with the skills and knowledge they need to adapt to a changing economy. 

Research suggests that apprentices earn a significant premium for their skills—as much as 

$300,000 more than their peers over a lifetime.64 The Obama Administration has prioritized 

expanding apprenticeship programs, and in 2014, called for doubling the number of U.S. 

registered apprenticeships over the next 5 years. The Administration awarded $175 million to 

expand apprenticeship in 2015 and announced $50.5 million in grants to support the expansion 

of new apprenticeship opportunities across the country in all major industry sectors in October 

2016.65

Strategy #3: Aid Workers in the Transition and Empower Workers to Ensure 

Broadly Shared Growth

As AI-driven automation changes the economy, empowered workers can be one of the Nation’s 

greatest assets. They can drive and spread innovation, lift consumer demand, and invest in the 

next generation.66 This strategy explores how to ensure that workers and job seekers are able to 

pursue the job opportunities for which they are best qualified, able to bounce back successfully 

 

64 Debbie Reed, et al. An Effectiveness Assessment and Cost-Benefit Analysis of Registered Apprenticeship in 10

States. Mathematica Policy Research, 2012

(https://wdr.doleta.gov/research/FullText_Documents/ETAOP_2012_10.pdf).

65 The White House, “Fact Sheet: Investing More than $50 Million through ApprenticeshipUSA to Expand Proven

Pathways onto the Middle Class.”

(https://www.dol.gov/sites/default/files/newsroom/newsreleases/WhiteHouseFactSheet-ApprenticeshipUSAFY2016.pdf)

66 Ragnhild Balsvik, "Is labor mobility a channel for spillovers from multinationals? Evidence from Norwegian

manufacturing," The Review of Economics and Statistics 93.1: 285-297, 2011; April M. Franco and Matthew F.

Mitchell, "Covenants not to compete, labor mobility, and industry dynamics." Journal of Economics & Management

Strategy 17.3: 581-606, 2008; Florence Honore, “From Common Ground to Breaking New Ground: Founding

Teams’ Prior Shared Experience and Start-up Performance.” Univ. of Minnesota, 2014; Council of Economic

Advisers, “Labor Market Monopsony: Trends Consequences, and Policy Responses,” 2016; Council of Economic

Advisers, “Worker Voice in a Time of Rising Inequality,” 2015; The White House, “Economic Report of the

President,” Chapter 4. February 2016 (http://go.wh.gov/dM4yPt).
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35

from job loss, and be well-positioned to ensure they receive an appropriate return for their work 

in the form of rising wages.

Modernize and strengthen the social safety net

Changes to how people work and the dislocation of some workers due to automation heightens 

the need for a robust safety net to ensure that people can still make ends meet, retrain, and 

potentially transition careers. That means strengthening critical supports such as unemployment 

insurance, Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary 

Assistance for Needy Families (TANF), and putting in place new programs such as wage 

insurance and emergency aid for families in crisis. It also means exploring whether programs 

such as Trade Adjustment Assistance should be expanded to help those displaced by automation. 

In addition, with the rise of part-time and contingent work, and a more mobile workforce in 

which individuals do not spend their entire career at a single company, policymakers will need to 

ensure that workers can access retirement, health care, and other benefits whether or not they get 

them on the job. For example, the Affordable Care Act expanded eligibility for Medicaid and 

reformed individual health insurance market to ensure that Americans who do not get coverage 

on the job can still find affordable, high-quality coverage, while simultaneously introducing 

reforms to improve coverage for people who are offered coverage at work.

Strengthen Unemployment Insurance

Job displacement is likely to be one of the most serious negative consequences of AI-driven 

automation, impacting entire industries and communities. Since its inception, unemployment 

insurance has been a powerful tool to prevent a job loss from hurtling a family into poverty. Last 

year alone, more than 7 million working Americans relied on the program to get by in tough 

times. Yet its protections have weakened over time, and today coverage by the program is at its 

lowest level in at least 50 years.67 Fewer than one in three unemployed Americans now receive 

unemployment insurance benefits, and benefits replace a smaller percentage of wages than 

before for those who do qualify. In 2009, the American Recovery and Reinvestment Act built a 

foundation for modernizing the program by making $7 billion available to states to expand 

coverage. Since then, more than 30 states have implemented and sustained important reforms.

The program will need to be further strengthened, as laid out in a proposal to Congress from the 

President, to ensure that the program can offer a more secure safety net for workers displaced by 

AI-driven automation and to provide a countervailing force against regional spikes in 

unemployment. Because workers may be unemployed for longer periods of time as they retrain 

or shift occupations, benefits should be restored to 26 weeks across the country. The program 

should also provide up to 52 weeks of additional benefits in states experiencing high levels of 

unemployment or rapid job loss to dampen the effects of mass layoffs on local economies. This 

would minimize the chance that these layoffs would lead to broader regional or national 

recessions. Finally, with fewer than 20 states having sufficient reserves to weather even a single 

 

67 Internal OMB calculations, with unemployment insurance coverage measured as UI claims divided by the number

of unemployed individuals.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

36

year of recession, the system’s long-term solvency must be ensured so that states are fully 

prepared for the potential increased cost of benefits stemming from job losses.

Some new tools could also help. Work-sharing programs can help employers hold on to their 

workers by reducing hours instead of laying them off, with workers whose hours are cut 

receiving partial unemployment benefits. States could also adopt temporary work-based training 

programs and allow workers to continue receiving unemployment benefits while participating in 

on-the-job training.

Experienced workers who lose their jobs and have to start over find themselves, on average,

earning wages at least 10 percent less than what they earned in the jobs they lost, and workers 

with more than 20 years of experience in their prior job face wages that are nearly a quarter less 

than they had previously been making.68 For this reason, the President has proposed providing

wage insurance to workers who were displaced from jobs if they take a job earning less, 

replacing up to half of their lost wages. Such a program would help soften the blow of the lower 

wages some displaced workers would have to accept, and would encourage workers to put their 

skills back to work quickly so they do not join the ranks of the long-term unemployed or leave 

the workforce.

These proposals represent steps that can be taken to prepare for a continuation of the job impacts 

that are already occurring in the economy. If, however, the economic impact of AI is relatively 

intense or comes on relatively quickly, and if the number of jobs affected approaches the 

estimates of Frey and Osbourne, then the unemployment insurance system may need to be 

significantly upgraded to match the magnitude of economic disruption and ensure that displaced 

workers do not leave the labor force.

Give Workers Improved Guidance to Navigate Job Transitions

With the AI revolution, guidance about how to effectively navigate this transition will be all the 

more critical. Simple and relatively inexpensive services such as job-search assistance, advice 

about education and training, and access to labor-market information have been found to be quite 

effective at helping individuals looking for work find employment more quickly. Evaluations 

typically find that employment services speed up employment by one to two weeks.69

Additionally, more intensive counseling services have been shown to increase recipients’ 

earnings and decrease the time they spend unemployed.70 AI can also be applied, to help workers 

find information that is best suited to their particular skills and circumstances. 

 

68 The White House, “Fact Sheet: Improving Economic Security by Strengthening and Modernizing the

Unemployment Insurance System,” Executive Office of the President, January 2016

(https://www.whitehouse.gov/the-press-office/2016/01/16/fact-sheet-improving-economic-security-strengtheningand-modernizing).

69 Louis Jacobson and Ian Petta, “Measuring the Effect of Public Labor Exchange (PLX) Referrals and Placements

in Washington and Oregon,” OWS Occasional Paper 6, 2000

(https://wdr.doleta.gov/research/FullText_Documents/owsop_2000_06.pdf).

70 Marios Michaelides, Eileen Poe-Yamagata, Jacob Benus, and Dharmendra Tirumalasetti, “Impact of the

Reemployment and Eligibility Assessment (REA) Initiative in Nevada,” IMPAQ International, 2012
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37

Strengthen Other Safety Net Programs

Other programs, such as SNAP and TANF, can provide critical safeguards for individuals who 

have lost their jobs or seen a substantial drop in income, by supplementing low-income 

households with food and monetary assistance. SNAP has played an important role in lifting 

millions of Americans out of poverty over the past five decades by providing key nutrition in 

times of need, with research showing that its benefits go beyond alleviating hunger to improving 

short-run and long-run health, educational attainment, and economic self-sufficiency.71

The TANF program, in conjunction with SNAP, was originally intended to help needy families 

achieve self-sufficiency. If automation leads to a rise in the number of families needing basic 

assistance due to growing inequality and poverty, it will be important to have a strong TANF 

program in place to help especially hard-hit families get back on their feet and work toward selfsufficiency. The President put forward a package of proposals to strengthen TANF, shoring up a 

system that has eroded over time, which would better support families affected by AI-driven 

automation.

In addition, the President’s budget includes a new $2 billion in funds to test and scale innovative 

state and local approaches to aid families facing financial crisis. For families on the brink—

including those affected by the entry of automation into a sector or occupation—a temporary 

illness or broken-down car could put them over the edge into a cycle of poverty. The funding 

would provide families with the emergency help they need to avert or reverse a downward spiral, 

and then connect those who need it with longer-term assistance so that families are stabilized.

 

(http://www.impaqint.com/sites/default/files/projectreports/ETAOP_2012_08_REA_Nevada_Follow_up_Report.pdf).

71 The White House, “Long-Term Benefits of the Supplemental Nutrition Assistance Program,” Executive Office of

the President, December 2015

(https://www.whitehouse.gov/sites/whitehouse.gov/files/documents/SNAP_report_final_nonembargo.pdf).
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38

Box 3: Replacing the Current Safety Net with a Universal Basic Income Could Be 

Counterproductive

(Excerpt from a speech by CEA Chair Jason Furman, in New York, July 7, 2016)

Fears of mass job displacement as a result of automation and AI, among other motivations, 

have led some to propose deep changes to the structure of government assistance. One of the 

more common proposals has been to replace some or all of the current social safety net with a 

universal basic income (UBI): providing a regular, unconditional cash grant to every man, 

woman, and child in the United States, instead of, say, Temporary Assistance to Needy 

Families (TANF), the Supplemental Nutrition Assistance Program (SNAP), or Medicaid.

While the exact contours of various UBI proposals differ, the idea has been put forward from 

the right by Charles Murray (2006), the left by Andy Stern and Lee Kravitz (2016), and has 

been a staple of some technologists’ policy vision for the future (Rhodes, Krisiloff, and 

Altman 2016). The different proposals have different motivations, including real and 

perceived deficiencies in the current social safety net, the belief in a simpler and more 

efficient system, and also the premise that we need to change our policies to deal with the 

changes that will be unleashed by AI and automation more broadly.

The issue is not that automation will render the vast majority of the population unemployable. 

Instead, it is that workers will either lack the skills or the ability to successfully match with 

the good, high paying jobs created by automation. While a market economy will do much of 

the work to match workers with new job opportunities, it does not always do so successfully, 

as we have seen in the past half-century. We should not advance a policy that is premised on 

giving up on the possibility of workers’ remaining employed. Instead, our goal should be first 

and foremost to foster the skills, training, job search assistance, and other labor market 

institutions to make sure people can get into jobs, which would much more directly address 

the employment issues raised by AI than would UBI.

Increase wages, competition, and worker bargaining power

As the Council of Economic Advisers recently discussed in its report on labor market 

monopsony,72 there is growing concern about a general reduction in competition among firms for 

workers and a commensurate shift in the balance of bargaining power toward employers. Market 

concentration resulting from the development of AI has the potential to worsen these trends. A 

displacement of workers from industries being disrupted by AI-driven automation would also 

create slack in the labor market in the short to medium-run, which is likely to depress wages. 

 

72 Council of Economic Advisers, “Issue Brief: Labor Market Monopsony: Trends, Consequences, and Policy

Responses,” October 2016

(https://www.whitehouse.gov/sites/default/files/page/files/20161025_monopsony_labor_mrkt_cea.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

39

Fortunately, there are several approaches described below that can counterbalance these trends 

and boost Americans’ wages and working conditions.

Raise the Minimum Wage

The minimum wage plays a critical role in reducing inequality, increasing consumption, and 

strengthening the workforce. Raising it could lift at least 4.6 million people out of poverty.73

Adjusted for inflation, the value of the minimum wage has fallen by nearly a quarter from its 

peak value in 1968 and is about one-fifth less than it was when President Reagan took office. 

While Congress has not acted, 22 states and the District of Columbia enacted legislation raising 

their minimum wage since that time—including the direct passage of new increases by residents 

of four states (Arizona, Colorado, Maine, and Washington) in the November 2016 elections. 

Modernize Overtime and Spread Work

Offering overtime is one of the single most important steps to help grow middle-class wages and 

spread jobs to more workers. In May 2016, the Department of Labor finalized revisions to its 

regulations that would extend overtime protections to 4.2 million more Americans and boost 

wages for workers by $12 billion over the next decade.74

Strengthen Unions, Worker Voice, and Bargaining Power

Growing and sustaining the middle class requires strong labor unions. Labor unions help to build 

the middle class and have been critical in restoring the link between hard work and opportunity 

so the benefits of economic growth can be more broadly shared.75 Unions have been at the 

forefront of establishing the 40-hour work week and the weekend, eliminating child labor laws, 

and establishing fair benefits and decent wages. Policymakers should explore ways to empower 

worker voice in the workplace through strengthening protections for organizing and creating new 

and innovative ways for workers to make their voices heard.

Protect Wages

Given the unique transformation that may be brought by AI, policymakers may also want to 

consider whether additional wage protections are needed for low- and middle-skilled workers if 

automation further “hollows out” middle-skill jobs. One of the most powerful upward pressures 

on wages is a tight labor market, as demand for labor can drive up wages.

 

73 Arindrajit Dube, “Minimum Wages and the Distribution of Family Incomes,” 2014

(http://sites.utexas.edu/chasp/files/2015/04/MinimumWagesandDistributionofFamilyIncomes.pdf)

74 The White House, “Fact Sheet: Growing Middle Class Paychecks and Helping Working Families Get Ahead by

Expanding Overtime Pay,” Executive Office of the President, May 2016 (https://www.whitehouse.gov/the-pressoffice/2016/05/17/fact-sheet-growing-middle-class-paychecks-and-helping-working-families-0). This rule was

challenged in court and is currently subject to an injunction.

75 Council of Economic Advisers, “Issue Brief: Worker Voice in a Time of Rising Inequality,” October 2015

(https://www.whitehouse.gov/sites/default/files/page/files/cea_worker_voice_issue_brief.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

40

Identify strategies to address differential geographic impact

Automation will happen more quickly in some places than in others, because of local policies, 

access to capital, innovative thinkers, the skill set of the workforce, proximity to urban centers, 

the culture of a place, and myriad other reasons. This has the potential to further exacerbate 

geographic disparities in income and wealth. Many of the places that are already grappling with 

structural changes in the economy, overall economic shifts, and poverty—and that therefore 

seem left behind from today’s economy—may fall even further behind, cementing an alreadypresent divide.

Below are two ways to address the uneven geographic impact: reduce the geographic barriers to 

work and pursue “place-based” solutions.

Reduce Geographic Barriers to Work

Geographic inequality can be reduced if workers can move to areas with more opportunity. If the 

new jobs spawned by AI-driven automation continue the trend of urbanization, though, a lack of 

affordable housing could make it difficult for lower-income families to access them. Over the 

past 3 decades, a growing number of barriers—including zoning, land-use regulations, and 

lengthy development approval processes—have made it increasingly difficult for housing 

markets to respond to growing demand by increasing the supply of housing. Reducing these 

barriers to affordable housing, expanding broadband access in poor and rural areas, and 

improving public transit would all serve to reduce geographic barriers to work. 

Another barrier is occupational licensing. Nearly one-quarter of all U.S. workers need a 

government license to do their jobs.76 While licensing can offer important health and safety 

protections to consumers, as well as benefits to workers, the current system often requires 

unnecessary training, lengthy delays, or high fees. Research shows that licensing can not only 

reduce total employment in licensed professions, but also that unlicensed workers earn roughly 7 

percent lower wages than licensed workers with similar levels of education and experience.77 In 

addition, the patchwork of state-by-state licensing rules leads to dramatically different 

requirements for the same occupations depending on the state in which one lives, potentially 

burdening workers who aim to move across state lines frequently. Best practices in licensing 

could allow states, working together or individually, to safeguard the well-being of consumers 

while maintaining a modernized regulatory system that meets the needs of workers and 

businesses. For example, groups of states could harmonize regulatory requirements as much as 

possible, and where appropriate enter into inter-state compacts that recognize licenses from other 

states to increase the mobility of skilled workers.78

 

76 The White House, “Occupational Licensing: A Framework for Policymakers,” July 2015

(https://www.whitehouse.gov/sites/default/files/docs/licensing_report_final_nonembargo.pdf).

77 Bureau of Labor Statistics. CEA calculations (https://www.whitehouse.gov/blog/2016/06/17/new-data-showroughly-one-quarter-us-workers-hold-occupational-license).

78 The White House, “Occupational Licensing: A Framework for Policymakers,” July 2015

(https://www.whitehouse.gov/sites/default/files/docs/licensing_report_final_nonembargo.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

41

Pursue Place-Based Solutions

To the extent that AI will differentially affect certain cities, policies should be appropriately 

targeted and can build on and learn from existing Administration policies. The Administration 

has launched a series of “place-based” initiatives, often focused on economically challenged 

areas, that empower local leaders in participating communities by helping to connect them with 

the resources they need to enact their own homegrown solutions for the challenges facing their 

communities.

Initiatives such as Choice Neighborhoods and Promise Zones take a big-picture approach to 

development. Their work helps improve communities’ access to and delivery of a wide range of 

services and activities, from building housing and creating jobs, to supporting after-school 

programs and improving the health of local residents. Agencies across the Federal Government 

have worked together to cut through red tape and expand opportunity for the people they serve. 

Another example, TechHire, is a national initiative to create pathways for more Americans to 

access well-paying technology jobs and expand local technology sectors in communities across 

the country. Originally announced by President Obama in 2015, TechHire enables employers to 

fill entry-level, career-path, skilled technology jobs, by hiring trained job seekers with the ability 

to do the job—but who are overlooked by typical hiring practices or underrepresented in the IT 

field.

Modernize tax policy

Tax policy plays a critical role in combating inequality, including income inequality that may be 

exacerbated by changes in employment from AI-based automation. A progressive tax system 

helps ensure that the benefits of economic growth are broadly shared, pushing back against 

increased inequality in pre-tax income. Progressive taxation is critical for raising adequate 

revenue to fund national security and domestic priorities, including supporting and retraining 

workers who may be harmed by increased automation, and will only grow more important if 

outsized gains continue to accrue at the top while other workers are left struggling. It is also 

critical to maintain and strengthen tax credits that encourage and reward work while helping 

families make ends meet, such as the Earned Income Tax Credit and Child Tax Credit.

Advanced AI systems could reinforce trends of national income shifting from labor to capital, as 

discussed above. With investment income heavily concentrated among high-income individuals, 

this shift could significantly exacerbate the rise in income inequality seen over the past few 

decades, absent an appropriate policy response. Taxing capital can be a highly progressive form 

of taxation, yet under the current tax system, individuals’ capital income currently enjoys lower 

tax rates than labor income, and often goes untaxed. President Obama has proposed reforming 

capital taxation and raising revenue by ensuring that inherited assets are subject to capital gains 

tax (ending so-called “stepped up basis”); increasing the top rate on capital gains and dividends 

for high-income households to 28 percent, the rate under President Reagan; and increasing the 

estate tax by restoring its 2009 parameters and closing loopholes. Some experts have proposed 

other reforms, including taxing capital gains on a mark-to-market basis and further reforming 

taxes on wealth transfers. 
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

42

Preparing for all contingencies 

If job displacements from AI are considerably beyond the patterns of technological change 

previously observed in economic history, a more aggressive policy response would likely be 

needed, with policymakers potentially exploring countervailing job creation strategies, new 

training supports, a more robust safety net, or additional strategies to combat inequality.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

43

Conclusion

Responding to the economic effects of AI-driven automation will be a significant policy 

challenge for the next Administration and its successors. AI has already begun to transform the 

American workplace, changing the types of jobs available and the skills that workers need to 

thrive. All Americans should have the opportunity to participate in addressing these challenges, 

whether as students, workers, managers, or technical leaders, or simply as citizens with a voice 

in the policy debate.

AI raises many new policy questions, which should be continued topics for discussion and 

consideration by future Administrations, Congress, the private sector, and the public. Continued 

engagement among government, industry, technical and policy experts, and the public should 

play an important role in moving the Nation toward policies that create broadly shared 

prosperity, unlock the creative potential of American companies and workers, and ensure the 

Nation’s continued leadership in the creation and use of AI.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

44

References

Daron Acemoglu, “Directed Technological Change, Review of Economic Studies 69(4): 781-809,

2003 (http://restud.oxfordjournals.org/content/69/4/781.short).

______, NBER reporter article, 2002.

______, “Technical change, inequality, and the labor market,” Journal of economic literature

40(1): 7-72, 2002.

Daron Acemoglu and David Autor, “Skills, tasks and technologies: Implications for employment

and earnings,” 2011, Handbook of labor economics 4(2011): 1043-171,

(http://economics.mit.edu/files/5571).

Ajay Agrawal, Joshua Gans, and Avi Goldfarb, “The Simple Economics of Machine

Intelligence,” Harvard Business Review, November 17 2016 (https://hbr.org/2016/11/thesimple-economics-of-machine-intelligence).

Melanie Arntz, Terry Gregory, and Ulrich Zierahn, “The Risk of Automation for Jobs in OECD

Countries: A Comparative Analysis,” OECD Social, Employment and Migration

Working Papers No. 189, 2016 (http://www.oecdilibrary.org/docserver/download/5jlz9h56dvq7-

en.pdf?expires=1480994298&id=id&accname=guest&checksum=6DC4B241A91EE860

DC391585FF43C51C).

David H. Autor and David Dorn, “The Growth of Low-Skill Service Jobs and the Polarization of

the US Labor Market,” American Economic Review 103(5): 1553-97, 2013

(http://www.ddorn.net/papers/Autor-Dorn-LowSkillServices-Polarization.pdf).

David H Autor, David Dorn, and Gordon H. Hanson, “The China Syndrome: Local Labor

Market Effects of Import Competition in the United States,” American Economic Review

103(6): 2121-68, 2013

(http://gps.ucsd.edu/_files/faculty/hanson/hanson_publication_it_china.pdf).

David H. Autor, Frank Levy, and Richard J. Murnane, “The Skill Content of Recent

Technological Change: An Empirical Exploration.” Quarterly Journal of Economics

118(4): 1279-1333, 2003.

Ragnhild Balsvik, "Is labor mobility a channel for spillovers from multinationals? Evidence from

Norwegian manufacturing," The Review of Economics and Statistics 93.1: 285-297, 2011.

Susanto Basu, John G. Fernald, and Matthew D. Shapiro, “Productivity growth in the 1990s:

technology, utilization, or adjustment?” Carnegie-Rochester Conference Series on Public

Policy 55(1): 117-65, 2001 (https://ideas.repec.org/a/eee/crcspp/v55y2001i1p117-

165.html).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

45

Amy Bernstein and Anand Raman, The Great Decoupling: An Interview with Erik Brynjolfsson

and Andrew McAfee, Harvard Business Review, June 2015 (https://hbr.org/2015/06/thegreat-decoupling).

Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and

Prosperity in a Time of Brilliant Technologies, WW Norton & Company, 2014.

______, CEA calculations (https://www.whitehouse.gov/blog/2016/06/17/new-data-showroughly-one-quarter-us-workers-hold-occupational-license).

John W. Budd, The Thought of Work, Cornell University Press, 2011.

Bureau of Labor Statistics, Civilian Unemployment Rate, 1948-2016. 

______, “Employment Projections—2014-24,” December 2015 

(http://www.bls.gov/news.release/pdf/ecopro.pdf).

______, “Employment Projections: Employment by major industry sector,” December 2015 

(http://www.bls.gov/emp/ep_table_201.htm).

Center on Budget and Policy Priorities, “Funding Down, Tuition Up: State Cuts to Higher

Education Threaten Quality and Affordability at Public Colleges,” August 2016

(http://www.cbpp.org/research/state-budget-and-tax/funding-down-tuition-up).

Kerwin Kofi Charles, Erik Hurst, and Matthew J. Notowidigdo, “Housing Booms,

Manufacturing Decline, and Labor Market Outcomes.” Working Paper, 2016

(http://faculty.wcas.northwestern.edu/noto/research/CHN_manuf_decline_housing_boom

s_mar2016.pdf).

Ajay Chaudry, “The Case for Early Education in the Emerging Economy,” Roosevelt InstituteThe New American Economy’s Learning Series, August 2016, p. 3

(http://rooseveltinstitute.org/wp-content/uploads/2016/08/The-Case-for-Early-Educationin-the-Emerging-Economy.pdf).

Council of Economic Advisers, “Labor Market Monopsony: Trends Consequences, and Policy

Responses,” 2016.

______, “The Economic Record of the Obama Administration: Reforming the Health Care

System,” December 2016

(https://www.whitehouse.gov/sites/default/files/page/files/20161213_cea_record_healh_c

are_reform.pdf).

______, “Worker Voice in a Time of Rising Inequality,” 2015.

Cyber Grand Challenge (https://www.cybergrandchallenge.com).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

46

Patricia A. Daly, “Agricultural Employment: Has the Decline Ended?” Monthly Labor Review

November 1981 (http://www.bls.gov/opub/mlr/1981/11/art2full.pdf).

Paul Daugherty and Mark Purdy. “Why AI is the Future of Growth.” 2016

(https://www.accenture.com/t20161031T154852__w__/us-en/_acnmedia/PDF33/Accenture-Why-AI-is-the-Future-of-Growth.PDF#zoom=50).

Steven J. Davis and Till Von Wachter, “Recessions and the Costs of Job Loss,” Brookings

Papers on Economic Activity, Economic Studies Program, The Brookings Institution,

43(2): 1-72, 2011 

(http://www.econ.ucla.edu/workshops/papers/Monetary/Recessions%20and%20the%20C

osts%20of%20Job%20Loss%20with%20Appendix.pdf).

Arindrajit Dube, “Minimum Wages and the Distribution of Family Incomes,” 2014

(http://sites.utexas.edu/chasp/files/2015/04/MinimumWagesandDistributionofFamilyInco

mes.pdf).

Ed Felten and Terah Lyons, “Public Input and Next Steps on the Future of Artificial

Intelligence,” Medium, September 6 2016 (https://medium.com/@USCTO/public-inputand-next-steps-on-the-future-of-artificialintelligence-458b82059fc3).

David J. Francis, et al., “Developmental lag versus deficit models of reading disability: A

longitudinal, individual growth curves analysis,” Journal of Educational Psychology,

88.1:3, 1996.

April M. Franco and Matthew F. Mitchell, "Covenants not to compete, labor mobility, and

industry dynamics." Journal of Economics & Management Strategy 17.3: 581-606, 2008.

Carl Frey and Michael Osborne, “The Future of Employment: How Susceptible are Jobs to

Computerization,” Oxford University, 2013

(http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pd

f).

Jason Furman, “Productivity Growth in the Advanced Economies: The Past, the Present, and 

Lessons for the Future.” Speech at the Peterson Institute for International Economics, 

Washington, July 9 2015 

(https://www.whitehouse.gov/sites/default/files/docs/20150709_productivity_advanced_e

conomies_piie.pdf). 

______, “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence.”

Remarks at AI Now: The Social and Economic Implications of Artificial Intelligence

Technologies in the Near Term. New York University, New York, July 7, 2016.

(https://www.whitehouse.gov/sites/default/files/page/files/20160707_cea_ai_furman.pdf).

Claudia Goldin and Lawrence F. Katz, The Race between Education and Technology, 2008.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

47

Georg Graetz and Guy Michaels, “Robots at Work,” CEPR Discussion Paper No. DP10477, 

March 2015 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2575781).

Jonathan V. Hall and Alan B. Krueger, “An Analysis of the Labor Market for Uber's DriverPartners in the United States,” 2016, (http://www.nber.org/papers/w22843.pdf). 

Seth D. Harris and Alan B. Krueger, “A Proposal for Modernizing Labor Laws for Twenty-FirstCentury Work: The ‘Independent Worker,’” 2015, 

(http://www.hamiltonproject.org/assets/files/modernizing_labor_laws_for_twenty_first_c

entury_work_krueger_harris.pdf).

Florence Honore, “From Common Ground to Breaking New Ground: Founding Teams’ Prior 

Shared Experience and Start-up Performance.” Univ. of Minnesota, 2014.

David Hounshell, From the American system to mass production, 1800-1932: The development 

of manufacturing technology in the United States, Baltimore: JHU Press, 1985.

Vivian Hunt, Dennis Layton, and Sara Prince, “Why Diversity Matters,” McKinsey & Company,

2015 (http://www.mckinsey.com/business-functions/organization/our-insights/whydiversity-matters).

Louis Jacobson and Ian Petta, “Measuring the Effect of Public Labor Exchange (PLX) Referrals

and Placements in Washington and Oregon,” OWS Occasional Paper 6, 2000

(https://wdr.doleta.gov/research/FullText_Documents/owsop_2000_06.pdf).

Nir Jaimovich and Henry E. Siu, “The Trend is the Cycle: Job Polarization and Jobless 

Recoveries.” NBER Working Paper No. 18334, 2012 

(http://www.nber.org/papers/w18334.pdf).

John A. James and Jonathan S. Skinner, “The Resolution of the Labor-Scarcity Paradox,” The 

Journal of Economic History, 45(3): 513-40, 1985 

(http://www.jstor.org/stable/2121750?seq=1#page_scan_tab_contents).

Aparna Joshi and Hyntak Roh, “The Role of Context in Work Team Diversity Research: A

Meta-Analytic Review,” Academy of Management Journal 52: 599-627, 2009.

Connie Juel, “Learning to read and write: A longitudinal study of 54 children from first through

fourth grades,” Journal of Educational Psychology 80.4:437, 1988.

Lawrence F. Katz and Kevin M. Murphy, “Changes in Relative Wages, 1963-1987: Supply and

Demand Factors,” Quarterly Journal of Economics, 107(Feb): 35-78, 1992.

John M. Keynes, “Economic Possibilities for our Grandchildren.” In Essays in Persuasion, New

York: W.W.Norton & Co., pp. 358-373, 1930.

(http://www.econ.yale.edu/smith/econ116a/keynes1.pdf).
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

48

Marios Michaelides, Eileen Poe-Yamagata, Jacob Benus, and Dharmendra Tirumalasetti,

“Impact of the Reemployment and Eligibility Assessment (REA) Initiative in Nevada,”

IMPAQ International, 2012 (http://www.impaqint.com/sites/default/files/projectreports/ETAOP_2012_08_REA_Nevada_Follow_up_Report.pdf).

Joel Mokyr, “Technological inertia in Economic History.” The Journal of Economic History,

52(2): 325-38, 1992

(http://www.jstor.org/stable/2123111?seq=1#page_scan_tab_contents).

National Center for Education Statistics, “The Condition of Education: Public High School

Graduation Rates,” May 2016.

National Science and Technology Council, National Artificial Intelligence Research and 

Development Strategic Plan, October 2016 

(https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf).

OECD, "Labour market programmes: expenditure and participants", OECD Employment and

Labour Market Statistics (database), 2016 

(http://stats.oecd.org/viewhtml.aspx?datasetcode=LMPEXP&lang=en#).

______, Program for International Student Assessment 2012 Results 

(https://www.oecd.org/unitedstates/PISA-2012-results-US.pdf)

Debbie Reed, et al. An Effectiveness Assessment and Cost-Benefit Analysis of Registered

Apprenticeship in 10 States. Mathematica Policy Research, 2012

(https://wdr.doleta.gov/research/FullText_Documents/ETAOP_2012_10.pdf).

Klaus Schwab, The Fourth Industrial Revolution: what it means, how to respond, World 

Economic Forum, January 2016 (https://www.weforum.org/agenda/2016/01/the-fourthindustrial-revolution-what-it-means-and-how-to-respond/).

USDA Economic Research Service, “Table 1. Indicies of farm output, input, and total factor 

productivity for the United States, 1948-2013” (https://www.ers.usda.gov/dataproducts/agricultural-productivity-in-the-us/).

U.S. Department of Education, Institute of Education Sciences, National Center for Education

Statistics, National Assessment of Educational Progress (NAEP), 2013 Mathematics and

Reading Assessments.

______, “A Matter of Equity: Preschool in America,” April 2015

(https://www2.ed.gov/documents/early-learning/matter-equity-preschool-america.pdf).

Bruce Western and Jake Rosenfeld, “Unions, norms, and the rise in US wage inequality,”

American Sociological Review 76(4): 513-37, 2011.
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE ECONOMY

49

The White House, Big Data: Seizing Opportunities, Preserving Values, May 2014

(https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_20

14.pdf).

______, “Economic Report of the President,” February 2016 (http://go.wh.gov/dM4yPt).

______, “Fact Sheet: Growing Middle Class Paychecks and Helping Working Families Get

Ahead by Expanding Overtime Pay,” Executive Office of the President, May 2016

(https://www.whitehouse.gov/the-press-office/2016/05/17/fact-sheet-growing-middleclass-paychecks-and-helping-working-families-0).

______, “Fact Sheet: Improving Economic Security by Strengthening and Modernizing the

Unemployment Insurance System,” Executive Office of the President, January 2016

(https://www.whitehouse.gov/the-press-office/2016/01/16/fact-sheet-improvingeconomic-security-strengthening-and-modernizing).

______, “Fact Sheet: Investing More than $50 Million through ApprenticeshipUSA to Expand

Proven Pathways onto the Middle Class.”

(https://www.dol.gov/sites/default/files/newsroom/newsreleases/WhiteHouseFactSheetApprenticeshipUSA-FY2016.pdf)

______, “Fact Sheet—White House Unveils America’s College Promise Proposal: Tuition-Free

Community College for Responsible Students,” January 2015

(https://www.whitehouse.gov/the-press-office/2015/01/09/fact-sheet-white-houseunveils-america-s-college-promise-proposal-tuitio).

______, “Long-Term Benefits of the Supplemental Nutrition Assistance Program,” Executive

Office of the President, December 2015

(https://www.whitehouse.gov/sites/whitehouse.gov/files/documents/SNAP_report_final_

nonembargo.pdf).

______, “Occupational Licensing: A Framework for Policymakers,” July 2015

(https://www.whitehouse.gov/sites/default/files/docs/licensing_report_final_nonembargo.

pdf).

______, “Preparing for the Future of Artificial Intelligence,” October 2016 

(https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/p

reparing_for_the_future_of_ai.pdf).

______, “Remarks of the President to the United States Hispanic Chamber of Commerce,”

March 2009 (https://www.whitehouse.gov/the-press-office/remarks-president-unitedstates-hispanic-chamber-commerce).