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Toward understanding the impact of artificial
intelligence on labor
Morgan R. Franka, David Autorb, James E. Bessenc, Erik Brynjolfssond,e, Manuel Cebriana, David J. Demingf,g,
Maryann Feldmanh, Matthew Groha, José Loboi, Esteban Moroa,j, Dashun Wangk,l, Hyejin Younk,l,
and Iyad Rahwana,m,n,1
Edited by Jose A. Scheinkman, Columbia University, New York, NY, and approved February 28, 2019 (received for review January
18, 2019)
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly
disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace
the work done by others and will likely transform almost all occupations at least to some degree. Rising
automation is happening in a period of growing economic inequality, raising fears of mass technological
unemployment and a renewed call for policy efforts to address the consequences of technological change. In this
paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future
of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic
requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill
substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies
interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international
trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data,
as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to
quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally,
given the fundamental uncertainty in predicting technological change, we recommend developing a decision
framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
automation employment economic resilience future of work
Artificial Intelligence (AI) is a rapidly advancing form of
technology with the potential to drastically reshape US
employment (1, 2). Unlike previous technologies, examples of AI have applications in a variety of highly educated,
well-paid, and predominantly urban industries (3), including medicine (4, 5), finance (6), and information technology (7). With AI’s potential to change the nature of work,
how can policy makers facilitate the next generation of
employment opportunities? Studying this question is
made difficult by the complexity of economic systems
and AI’s differential impact on different types of labor.
While technology generally increases productivity,
AI may diminish some of today’s valuable employment opportunities. Consequently, researchers and
policy makers worry about the future of work in both
advanced and developing economies worldwide. As
an example, China is making AI-driven technology the
centerpiece of its economic development plan (8).
Automation concerns are not new to AI, and examples
date back even to the advent of written language. In
ancient Greece (ca. 370 BC), Plato’s Phaedrus (9) described how writing would displace human memory
Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; bDepartment of Economics, Massachusetts Institute of
Technology, Cambridge, MA 02139; cTechnology & Policy Research Initiative, School of Law, Boston University, Boston, MA 02215; dSloan School
of Management, Massachusetts Institute of Technology, Cambridge, MA 02139; eNational Bureau of Economic Research, Cambridge, MA 02138;
Harvard Kennedy School, Harvard University, Cambridge, MA 02138; gGraduate School of Education, Harvard University, Cambridge, MA 02138;
Department of Public Policy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599; iSchool of Sustainability, Arizona State
University, Tempe, AZ 85287; jGrupo Interdisciplinar de Sistemas Complejos, Departmento de Matematicas, Escuela Politécnica Superior,
Universidad Carlos III de Madrid, 28911 Madrid, Spain; kKellogg School of Management, Northwestern University, Evanston, IL 60208;
Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208; mInstitute for Data, Systems, and Society, Massachusetts
Institute of Technology, Cambridge, MA 02139; and nCenter for Humans and Machines, Max Planck Institute for Human Development, 14195
Berlin, Germany
Author contributions: M.R.F., D.A., J.E.B., E.B., M.C., D.J.D., M.F., M.G., J.L., E.M., D.W., H.Y., and I.R. designed research; M.R.F. performed
research; M.R.F. and M.G. analyzed data; and M.R.F., D.A., J.E.B., E.B., M.C., D.J.D., M.F., M.G., J.L., E.M., D.W., H.Y., and I.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
To whom correspondence should be addressed. Email:
This article contains supporting information online at
Published online March 25, 2019.
PNAS | April 2, 2019 | vol. 116 | no. 14 | 6531–6539
and reading would substitute true knowledge with mere data.
More commonly, historians point to the Industrial Revolution
and the riots of 19th-century Luddites (10) as examples where
technological advancement led to social unrest. Two examples
from the recent past echo these concerns.
Wassily Leontief, winner of the 1973 Nobel Prize in Economics,
noted in 1952, “Labor will become less and less important. . .
More workers will be replaced by machines. I do not see that
new industries can employ everybody who wants a job” (11).
Similarly, US Attorney General Robert F. Kennedy commented
in 1964, “Automation provides us with wondrous increases of
production and information, but does it tell us what to do with
the men the machines displace? Modern industry gives us the
capacity for unparalleled wealth—but where is our capacity to
make that wealth meaningful to the poor of every nation?” (12).
However, despite these long-lasting and oft-recurring concerns, society underwent profound transformations, the economy
continued to grow, technology continues to advance, and workers
continue to have jobs. Given this history of concern, what makes
human labor resilient to automation? Is AI a fundamentally new
concern from technologies of the past?
Answering these questions requires a detailed knowledge that
connects AI to today’s workplace skills. Each specific technology
alters the demand for specific types of labor, and thus the varying
skill requirements of different job titles can obfuscate technology’s impact. In general, depending on the nature of the job, a
worker may be augmented by technology or in competition with it
(13–15). For example, technological advancements in robotics can
diminish wages and employment opportunities for manufacturing
workers (16, 17). However, technological change does not necessarily produce unemployment, and, in the case of AI, cognitive
technology may actually augment workers (18, 19). For instance,
machine learning appears to bolster the productivity of software
developers while also creating new investment and manufacturing opportunities (e.g., autonomous vehicles). Complicating matters further, the skill requirements of occupations do not remain
static, but instead change with changing technology (19, 20).
In the remainder of this article, we describe how these
competing dynamics combined with insufficient data might allow
contrasting perspectives to coexist. In particular, we argue that
the limitations into data about workplace tasks and skills restricts
the viable approaches to the problem of technological change
and the future of work. We offer suggestions to improve data
collection with the goal of enriching models for workplace skills,
employment, and the impact of AI. Finally, we suggest insights
that improved data could provide in combination with a methodological focus on resilience and forecasting.
Contrasting Perspectives
Doomsayer’s Perspective. Technology improves to make human
labor more efficient, but large improvements may yield deleterious effects for employment. This obsoletion through labor substitution leads many to worry about “technological unemployment”
and motivates efforts to forecast AI’s impact of jobs. One study
assessed recent developments in AI to conclude that 47% of current
US employment is at high risk of computerization (23), while a contrasting study, using a different methodology, concluded that a less
alarming 9% of employment is at risk (24). Similar studies have
looked at the impact of automation on employment in other
countries and reached sobering conclusions: Automation will
affect 35% of employment in Finland (25), 59% of employment in
Germany (26), and 45 to 60% of employment across Europe (27).
6532 |
Critics have complained that prospective studies lack validation,
but retrospective studies also find that robotics are diminishing
employment opportunities in US manufacturing (17, 28) [although
not in Germany (29)].
Optimist’s Perspective. Optimists suggest that technology may
substitute for some types of labor but that efficiency gains from
technological augmentation outweigh transition costs (30–34),
and, in many cases, technology increases employment for workers
who are in not direct competition with it (19, 35) [although recent
follow-up work suggests these are temporary gains (28)]. Furthermore, the skill requirements of each job title are not static and
actually evolve over time to reflect evolving labor needs. For example, workers may require more social skills because those skills
remain difficult to automate (20). Even if technology depresses
employment for some types of labor, it can create new needs and
new opportunities through “creative destruction” (36–38). For
instance, the replacement of equestrian travel with automobiles
spurred demand for new roadside amenities, such as motels, gas
stations, and fast food (39).
Unifying Perspectives. On one hand, multiple dynamics accompany technological change and create uncertainty about the
future of work. On the other hand, experts agree that occupations
are best understood as abstract bundles of skills (18, 40) and that
technology directly impacts demand for specific skills instead of
acting on whole occupations all at once (16, 19, 35, 41). Therefore,
a detailed framework that connects specific skill types to career
mobility (18, 42) and to whole urban workforces (40) may help to
unify competing perspectives (Fig. 1C). Existing studies have argued theoretically that different skill types underpin aggregate
labor trends, such as job polarization (16) and urban migration (43,
44), but robust empirical validation is made difficult by the specificity of modern skills data and their temporal sparsity.
Overcoming Barriers to Forecasting the Future of Work
In this section we identify barriers to our scientific modeling of
technological change and the future of work. Along with each
barrier, we propose a potential solution that could enable improvement in forecasting labor trends. We provide a summary of
these barriers and solutions in Table 1.
Barrier: Sparse Skills Data. Forecasting automation from AI requires skills data that keep pace with rapidly advancing technology [e.g., Moore’s Law (45), robots in manufacturing (17), and
patent production (46–48)]. While skill types inform the theory of
labor and technological change (1, 18, 21, 49), standard labor
data focus on aggregate statistics, such as wage and employment
numbers, and can lack resolution into the specifics that distinguish
different job titles and different types of work. For example, previous studies have empirically observed a “hollowing” of the
middle-skill jobs described by increasing employment share for
low-skill and high-skill occupations at the expense of middle-skill
occupations (16, 35) (reproduced in Fig. 1A). These studies use
skills to explain labor trends but are limited empirically to measuring
annual wages instead of skill content directly. While wages may
correlate with specific skills, wage alone fails to capture the defining
features of an occupation, and models focused on only cognitive and
physical labor fail to explain responses to technological change (21).
As another approach, data on educational requirements can
add resolution to employment trends (50–52). For instance, jobs
that require a bachelor’s degree may identify cognitive workers
Frank et al.
Fig. 1. Motivating and describing a framework to study technology’s impact on workplace skills. (A) Following ref. 21, we use American
Community Survey national employment statistics to compare the change in employment share (y axis) of occupations according to their average
annual wage (x axis) during two time periods. Employment share is increasing for low- and high-wage occupations at the expense of middle-wage
occupations. (B) Following ref. 15, we use data from the Federal Reserve Bank of St. Louis to compare US productivity (real output per hour) and
workers’ income (real median personal income), which have traditionally grown in tandem. The efficiency gains of automating technologies are
thought to contribute to this so-called great decoupling starting around the year 2000. (C) A framework for studying technological change,
workplace skills, and the future of work as multilayered network. (Left) Cities and rural areas represent separate labor markets, but workers and
goods can flow between them. (Middle) Each location can be represented as an employment distribution across occupations. Connections
between occupations in a labor market represent viable job transitions. Job transitions are viable if workers of one job can meet the skill
requirements of another job [i.e., “skill matching” (22)]. (Right) Workers’ varying skill sets represent bundles of workplace skills that tend to be
valuable together. Skill pairs that tend to cooccur may identify paths to career mobility. Technology alters demand for specific workplace skills,
thus altering the connections between skill pairs. As an example, machine vision software may impact the demand for human labor for some visual
task. These alterations can accumulate and diffuse throughout the entire system as aggregate labor trends described in A and B.
who are less susceptible to automation. Ideally, educational institutions train workers to possess valuable skills that lead to
higher wages (53). However, looking at education and wages
alone has proven insufficient to explain stagnating returns on
education (16, 54, 55) and slow wage growth despite increases in
national productivity (14, 15, 41) (Fig. 1B).
Improving data on the skills required to perform specific job
tasks may provide better insights than wages and education
alone. For example, previous studies have considered occupations
as routine or nonroutine and cognitive or physical (21, 56–63) or
looked at specific types of skills in relation to augmentation and
substitution from technology (18, 41). Increasing a labor model’s
specificity into workplace tasks and skills might further resolve
labor trends and improve predictions of automation from AI. As an
example, consider that civil engineers and medical doctors are
both high-wage, cognitive, nonroutine occupations requiring
many years of higher education and additional professional certification. However, these occupations require distinct workplace
skills that are largely nontransferable, and these occupations are
likely to interact with different technologies. Wages and education—
and even aggregations of workplace skills—may be too coarse
to distinguish occupations and, thus, may obfuscate the differential impact of various technologies and complicate predictions
of changing skill requirements. In turn, these shortcomings may
Frank et al.
help explain the variability in current automation predictions
that enable contrasting perspectives.
While publicly available skills data are limited, the US Department of Labor’s O*NET database has seen recent use in labor
research (e.g., refs. 23, 41, and 64). O*NET offers many benefits
including a detailed taxonomy of skills and more regular updates
than preceding datasets. In 2014, O*NET began to receive partial
updates twice annually, which is a considerable improvement on
the Dictionary of Occupational Titles, which was published in four
editions in 1939, 1949, 1965, and 1977, with a revision in 1991.
However, employment trends and changing demand for specific
tasks and skills might change faster than O*NET’s temporal resolution and skill categorization can capture. Complicating matters
further, advances in AI and machine learning may be changing the
nature of automation, thereby altering the types of tasks that are
affected by technology (3, 65).
Furthermore, studies often use O*NET data to construct aggregations of skills, such as information input or mental processes
(40), rather than focusing on skills at their most granular level.
Methodological choices aside, O*NET’s relatively static skill taxonomy poses its own problems as well. For instance, according to
O*NET, the skill “installation” is equally important to both computer programmers and to plumbers, but, undoubtedly, workers
in these occupations are performing very dissimilar tasks when
PNAS | April 2, 2019 | vol. 116 | no. 14 | 6533
Table 1. Tabulating the current barriers to forecasting the future of work along with proposed solutions
Potential solution
Sparse skills data

Adaptive skill taxonomies
Connect susceptible skills to new technology
Improve temporal resolution of data collection
Use data from career web platforms

Explore out-of-equilibrium dynamics
Identify workplace skill interdependencies
Connect skill relationships to worker mobility
Relate worker mobility to economic resilience in cities
Explore models of resilience from other academic domains

Labor dependencies between places (e.g., cities)
Identify skill sets of local economies
Identify heterogeneous impact of technology across places
Use intercity connections to study national economic resilience
Limited modeling of resilience
Places in isolation
they are installing things on the job (see Fig. 2A and SI Appendix,
section 1 for calculation). More generally, any static taxonomy for
workplace skills is not ideal for a changing economy: Should
mathematics and programming be two separate workplace skills
given that they are both computational? Conversely, is “programming” too broad given the variety of existing software and
programming languages? Perhaps it is more appropriate to specify
programming tasks or specific programming languages (see Fig. 2B
for an example), especially given the rapid development of AI
and machine learning. Likely, the correct abstraction is situationdependent, but O*NET data offer limited flexibility.
Granular skills data will help elucidate the micro-scale impact
of AI and other technologies in labor systems. For instance, the
specifications of recent patents might suggest automatable types
of labor in the near future (46–48), thus elucidating the impact of
technological change at the granularity of workplace-specific
tasks and skills. The distribution of skill categories within occupations and over individuals’ careers can reveal how occupational
skill requirements evolve. As an example, consider that occupations such as software developer dynamically change the skill
requireme …
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