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<reference>
Artificial Intelligence, Firm Growth, and Product Innovation∗
Tania Babina†
Alex He§

Anastassia Fedyk‡
James Hodson¶

November 2021
Abstract
We study the use and economic impact of AI technologies among U.S. firms. We propose
a new measure of firm-level AI investments, using a unique combination of worker resume
and job postings datasets. Our measure reveals a stark increase in AI investments across sectors. AI-investing firms see increased growth in sales, employment, and market valuations.
This growth comes primarily through increased product innovation, reflected in trademarks,
product patents, and product updates. AI-powered growth concentrates among ex-ante larger
firms, leading to higher industry concentration and reinforcing winner-take-most dynamics.
Our results highlight that new technologies can contribute to growth through product innovation.
Keywords: artificial intelligence, technological change, technology adoption, economic growth, product
innovation, productivity, human capital, superstar firms, industry concentration
JEL codes: D22, E22, J23, J24, L11, O33
∗ This paper builds on an earlier version of the project titled “Artificial Intelligence, Firm Growth, and Indsutry

Concentration”, which had its first draft on March 16, 2020. The authors are grateful to Daron Acemoglu, Philippe
Aghion, David Autor, Ann Bartel, Jan Bena, Francesco D’Acunto, Nicolas Crouzet, Xavier Giroud, Maarten de Ridder,
Larry Katz, Anton Korinek, David Levine, Max Maksimovic, Gustavo Manso, Filippo Mezzanotti, Thomas Philippon, David Sraer, Tano Santos, Bledi Taska, Laura Veldkamp, Shang-Jin Wei, Michal Zator, and seminar participants
at AFFECT, Babson College, Columbia University, Federal Reserve Board of Atlanta, FOM Conference, Labor and Finance Group Conference, Michigan State University, NBER Corporate Finance, NBER productivity seminar, NBER SI
Macroeconomics and Productivity, NBER CRIW, Northwestern University, NYU WARPFIN, Society for Economic Dynamics, Tilburg University, Triangle Macro-Finance Workshop, Tulane University, Queen Mary University of London,
Sao Paulo School of Economics, UC Berkeley, UNC Junior Roundtable, University of Illinois Chicago, University of
Maryland, University of Oklahoma, University of Washington, and Vienna Graduate School of Finance for helpful
suggestions. The authors thank Cognism Ltd. for providing the employment data; Burning Glass Technologies for
providing the job postings data; Bernhard Ganglmair, W. Keith Robinson, and Michael Seeligson for sharing their data
on patent (product vs. process) classification; and Gerard Hoberg and Gordon Phillips for providing data on changes
in firms’ product mix. Nikita Chuvakhin, Joanna Harris, Derek Ho, Gary Nguyen, and Hieu Nguyen provided excellent research assistance. Fedyk gratefully acknowledges financial support from the Clausen Center for International
Business and Policy at UC Berkeley.
† Columbia University. Email: tania.babina@gsb.columbia.edu.
‡ University of California, Berkeley. Email: fedyk@berkeley.edu.
§ University of Maryland. Email: axhe@umd.edu.
¶ Cognism; AI for Good Foundation. Email: hodson@ai4good.org.

Electronic copy available at: https://ssrn.com/abstract=3651052

Technological change is a key driver of economic growth (Romer, 1990; Aghion and Howitt, 1992).
The past decade has seen a new technological shift: substantial developments in artificial intelligence (AI) technologies and their wide-spread commercial application, driven by rapid accumulation of data, falling costs of computing power, and methodological breakthroughs (Furman and
Seamans 2019; Mihet and Philippon 2019). AI is a prediction technology, and predictions are at
the heart of decision-making under uncertainty (Agrawal et al., 2019). AI algorithms allow firms
to learn better and faster from vast quantities of data, significantly improving the accuracy of
predictions. As such, AI can be a general purpose technology that generates growth through increased productivity and product innovation (Aghion et al., 2017; Cockburn et al., 2018). Indeed,
in a survey of executives at companies investing in AI, 70% anticipate that AI will fundamentally transform their companies and industries within the next five years.1 Yet it remains an open
question whether artificial intelligence can transform economies and spur economic growth, as
lackluster aggregate productivity growth over the past decade has led to concerns that the benefits of AI may be over-hyped or take a much longer time to materialize (Mihet and Philippon,
2019; Brynjolfsson et al., 2019; Haltiwanger, 2019). To date, the lack of comprehensive data on
firms’ use of AI technologies has posed the key challenge to understanding the prevalence and
the economic impact of AI technologies (Seamans and Raj, 2018).
In this paper, we propose a new measure of investments in AI technologies based on firms’
AI-skilled human capital. The heavy reliance of AI on human expertise makes the human-capitalbased approach particularly well-suited in this setting. We take advantage of a unique combination of datasets that capture both the stock of and the demand for AI-skilled employees among
U.S. firms: resume data from Cognism Inc, which offer job histories for 535 million individuals
globally, and job postings data from Burning Glass, which capture 180 million job vacancies. Our
new AI measure allows us to analyze the patterns of AI adoption and its impact on the adopting firms and industries. Our main takeaway is that firms that invest more in AI experience
higher growth through increased product innovation, which can be seen in increased trademarks,
product patents, and updates to firms’ product portfolios (Hoberg and Phillips, 2016). At the
aggregate level, growth in AI investments are associated with increased industry concentration
and winner-take-most dynamics, as larger firms benefit more from AI investments. Overall, our
results suggest that, so far, the first-order effect of AI has been in empowering growth through
product innovation, consistent with AI reducing the costs of product development.
Our work offers several innovations over the existing literature. First, while prior research has
made progress in studying the impact of AI on labor markets and occupations (e.g., Felten et al.,
2019; Acemoglu et al., 2021), we focus on the impact of AI on firms and shed new light on the
1 See here for a survey by Deloitte in 2018.

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ability of AI to drive growth, including the mechanisms through which this growth is achieved.
To do so, we measure granular AI investments and their potential effects for a broad sample of
AI-using firms across a wide range of industries, which complements recent work that focuses on
AI-inventing firms (Alderucci et al., 2020). Second, in the absence of administrative firm-worker
matched U.S. data containing individual workers’ occupations, our Cognism resume data provide
high coverage of U.S. jobs with detailed job descriptions while representing more than 64% of
full-time U.S. employment as of 2018.2 Third, our paper is also the first to cross-validate AI labor
demand identified from job postings with inflows and outflows of AI workers identified from
resumes. Fourth, our rich data on firms’ employees and their jobs allow us to measure and control
for confounding factors, such as the use of non-AI information technologies, and capture the use of
external AI solutions and software (e.g., IPSoft Amelia). Finally, our comprehensive cross-industry
data provide a first glimpse at the aggregate industry-level implications of AI investments.
Even with our detailed data, identifying firms’ investments in AI is challenging due to the multifaceted nature of AI applications.3 We circumvent this challenge by proposing a new data-driven
approach to identify AI-related jobs, which does not depend on pre-specified lists of keywords.
Instead, our algorithm learns the AI-relatedness of each job posting empirically. First, we measure
the AI-relatedness of each skill in the job postings data, based on that skill’s co-occurrence with the
core AI skills—machine learning, computer vision, and natural language processing. Second, we
obtain a measure of AI-relatedness of each job posting by averaging the AI-relatedness of all skills
required by the job posting. Finally, we leverage the most AI-related skills from the job postings
data to classify AI workers in the less structured resume data. For each employee, we consider
whether skills with the highest AI-relatedness (e.g., “deep learning”) appear either in the job title,
in the job description, or in any publications, patents, or awards received during that job. This
gives us a classification of each employee of each firm at each point in time. We aggregate both
job postings data and resume data to the firm level and match to public firms in the Compustat
data. Encouragingly, the two measures of AI investments, although based on two independent
datasets, are highly correlated and yield consistent results throughout the paper.
We confirm that our human-capital-based measures of AI investments display intuitive properties. First, we manually inspect large samples of AI-classified jobs and confirm that our classification picks up highly AI-skilled positions. Second, given that we do not use job titles to filter
AI-skilled jobs, we validate our measure by confirming that the job postings with the highest AI2 For comparison, while the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program
provides firm-worker matched data and worker wages, it does not include any information on workers’ occupations or
their jobs (Abowd et al., 2009; Haltiwanger et al., 2014). Moreover, a typical project using the LEHD data does not have
access to all states due to administrative reasons: for example, Babina (2020) has access to about 40% of employment
and Babina and Howell (2018)—60%.
3 Within a single firm, e.g. Caterpillar Inc., AI can have use cases ranging from improving machinery via computer
vision to offering a new product line of Internet of Things style analytics to machine operators.

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relatedness measures skew heavily towards highly AI-specific job titles. Third, we confirm that
our AI measure does not pick up general data-related skills, only those that are specifically associated with AI implementation. Fourth, we provide detailed case studies of specific applications
of AI within several firms. Fifth, we confirm that AI-investing firms also increase research and
development (R&D) expenditures, consistent with increased experimentation with applying the
new AI technologies. Finally, we enrich our baseline measure by incorporating the use of external
AI solutions and software and find similar results.
We begin our analysis by describing key patterns in AI investments. In both job postings and
employee resume datasets, the fraction of AI jobs has increased dramatically over time, growing
more than seven-fold from 2010 to 2018. The share of AI jobs is highest in the technology sector,
but the rate of increase in AI investments over time is similar across sectors. At the firm level,
growth in AI investments are more pronounced among ex ante larger firms and firms with higher
cash holdings and R&D intensity. Looking at the local labor market conditions, we observe that
higher-wage and more educated areas experience faster growth in AI-skilled hiring.
We next address the fundamental question of whether AI investments are associated with
higher firm growth. As is standard in settings with slow-moving processes like technological
change (e.g., Acemoglu and Restrepo, 2020), our primary specification is a long-differences regression of changes in firm outcomes from 2010 to 2018 on changes in the firm-level share of AI
workers. This strategy is especially well-suited for our setting, where AI investments accumulate
gradually over time and generate effects that may not be immediate. We include a rich set of
controls: industry fixed effects and firm-, industry-, and commuting-zone-level characteristics in
2010. We document a strong and consistent pattern of higher growth among firms that invest more
in AI: a one-standard-deviation increase in the resume-based measure of AI investments over the
8-year period corresponds to a 20.3% increase in sales, a 21.9% increase in employment, and a
22.4% increase in market valuation. The results are ubiquitous across major industry sectors (e.g.,
manufacturing, finance, and retail), supporting the idea that AI is a general purpose technology.
While the long-differences specification controls for time-invariant firm characteristics, we perform several tests to address concerns about omitted variables or reverse causality and buttress a
causal interpretation of our results. First, we exploit firm-level panel data to examine firm growth
dynamically in each year around AI investments using a standard distributed lead-lag model
(Aghion et al., 2020). We find no differential trends in firm growth prior to AI investments and a
positive effect after a lag of two to three years. Second, the results are robust to the inclusion of
controls for past firm and industry growth and future growth opportunities proxied by Tobin’s
q. Third, we confirm that our results reflect specifically investments in AI, rather than other technologies: the effects of AI investments remain unchanged when controlling for contemporaneous

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firm-level investments in robotics, non-AI information technologies, and non-AI data analytics.
We further address concerns regarding unobserved shocks driving both firm growth and AI
investments using a novel instrumental variables strategy. We instrument for growth in firm-level
AI investments using variation in firms’ ex-ante exposure to the future supply of AI talent from
universities that are historically strong in AI research. The core idea is that the scarcity of AItrained labor is one of the most important constraints to firms’ AI adoption (e.g., CorrelationOne,
2019), and universities that are historically strong in AI research have been able to train more AIskilled graduates in recent years, enabling firms that typically hire from those universities to more
readily recruit AI talent. To construct the instrument, we compile two new datasets on (i) the
ex-ante strength of AI research in each university and (ii) firm-university hiring networks prior
to 2010 to measure firms’ exposure to AI-strong universities. Consistent with commercial interest
in AI becoming widespread only since 2012, we show that firms’ connections to AI-strong universities in 2010 were not driven by the need to hire AI-skilled workers. Moreover, we validate
the key assumptions underlying this instrument and show that firms’ ex-ante connections to AIstrong universities strongly predict ex-post hiring of AI-skilled workers. We then show that the
instrumented firm-level growth in AI investments robustly predicts firm growth. Finally, we verify that the results are not driven by other characteristics of AI-strong universities such as strength
in general computer science.
To understand the mechanisms through which AI generates firm growth, we examine two
key non-mutually-exclusive channels: (i) product innovation and (ii) reduction in operating costs.
The first channel is motivated by the extensive literature documenting the importance of product
innovation for firm growth (Klette and Kortum, 2004; Hottman et al., 2016; Argente et al., 2021).
Theoretically, AI can potentially reduce the costs of product innovation in two ways. First, since
product development involves lengthy experimentation with uncertain benefits (Braguinsky et al.,
2020), the ability of AI algorithms to quickly learn from large datasets can reduce the uncertainty
of experimentation in product development and make the process of learning about promising
projects more efficient. For example, at Moderna, AI algorithms have been leveraged in the development of the first COVID-19 vaccine in just 65 days, a process that would previously take
years. Moreover, AI algorithms themselves can constitute improved products (e.g., AI-powered
trading platforms). Second, AI can contribute to increased product scope by improving firms’
ability to learn about customer preferences and tailor product offerings to customer tastes (Mihet
and Philippon, 2019). Empirically, we find that firms with larger growth in AI investments see increased product innovation, reflected in more product patents (Ganglmair et al., 2021), trademarks
(Hsu et al., 2021), and updates to product portfolios (Hoberg et al., 2014).
The second channel through which AI can stimulate growth is by lowering operating costs

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and improving productivity, for example, by replacing human labor for some tasks (Agrawal
et al., 2019) or by increasing operational efficiency through better forecasting and more efficient
processes (Basu et al., 2001; Farboodi and Veldkamp, 2021). Empirically, we do not find support for
this second channel. Growth in AI investments have zero effect on changes in sales per worker,
total factor productivity, and process patents—which may reflect long lags in materializing the
productivity benefits of AI (David, 1990; Brynjolfsson et al., 2019).
The benefits from AI investments are unevenly distributed across firms, consistent with the
hypothesis that AI can increase inequality by favoring large firms with more data, which is a crucial input to AI implementation (Mihet and Philippon, 2019; Farboodi et al., 2019). We estimate
the effect of AI investments within groups of firms by initial size and find that the positive relationship between AI investments and firm growth is much stronger among ex-ante larger firms.
These results provide a new angle to the endogenous growth literature. For example, Akcigit and
Kerr (2018) find that larger firms have higher costs of product innovation, which put constraints
on the ability of large firms to scale. Our evidence shows that AI can help large firms overcome
these previously documented barriers and scale up more easily.
Our final set of results speak to potential aggregate effects of AI on industry dynamics. We first
test whether firm-level growth translates into industry-level growth. It is possible that the positive
effects on AI-investing firms are offset or even dominated by negative spillovers to competitors
within the industry, and previous work shows that the use of technology can be contractionary at
the aggregate level if input use declines (Basu et al., 2006). Nevertheless, we find that industries
that invest more in AI experience an overall increase in sales and employment within the sample
of Compustat firms. Second, growth in AI investments are associated with increased industry
concentration, consistent with our finding that AI favors ex-ante larger firms with more data.
This suggests that AI investments can affect industry dynamics by reinforcing winner-take-most
dynamics.
Overall, we document that AI leads to higher firm growth, and this growth mainly comes
from firms’ use of AI technologies for product innovation. This mechanism reflects the nature
of AI as a prediction technology. Predictions are essential for firms’ decision-making across all
aspects of operations and particularly in product development, which requires experimentation
and learning about promising projects and customer preferences (Braguinsky et al., 2020). The
ability to perform better predictions with AI can create new business opportunities. In this context,
our paper offers micro-level evidence and helps to unpack the black box of where “new projects”
and investment opportunities come from: new technologies like AI, which allow firms to learn
better and faster, can expand the investment opportunity frontier by decreasing firms’ product
development costs.

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Related Literature
Our paper provides one of the first pieces of systematic evidence for the impact of artificial intelligence on firms and economic growth. While some recent work provides evidence on the use
of AI technologies in specific industries such as finance (D’Acunto et al., 2019) and retail (Bajari
et al., 2019),4 our comprehensive data allow us to measure AI investments across a wide range of
industries, offering a new measure of technology adoption based on firms’ human capital (Hall
and Jaffe 2018).5 Recent theoretical literature argues that as a general purpose technology, AI has
the potential to stimulate economic growth across a wide range of sectors (e.g., Aghion et al., 2017;
Mihet and Philippon, 2019). Our empirical evidence supports this view and offers an additional
insight: the mechanism through which AI fuels growth is by empowering product innovation.
Product innovation has been considered a key mechanism for firm growth (e.g., Hottman et al.,
2016; Argente et al., 2021). Our results suggest that AI can contribute to product innovation by
both (i) overcoming supply-side constraints, such as costly experimentation in product development, and (ii) improving firms’ ability to learn about consumer preferences. Our findings are
consistent with Braguinsky et al. (2020), who argue that experimentation and new technologies
are crucial for firm growth, and support the insight by Cockburn et al. (2018) that AI technologies
can spur innovation by allowing for faster accumulation of knowledge. Our findings also complement Rock (2019), who shows that the launch of Google’s TensorFlow expedited the gain in
market valuations associated with firms’ exposure to AI, with null effects on productivity.
Furthermore, our results speak to the literature on technology adoption, diffusion, and implications for growth (e.g., Romer, 1990; Aghion and Howitt, 1992; Parente and Prescott, 1994).
Recent work by Crouzet et al. (2021) exploits demonetization in India to explore the role of complementarities for technology adoption, and Juhász et al. (2020) highlight that experimentation
in applying new technologies can delay their effect on firms. Several previous technologies have
been specifically associated with increased product innovation. For example, Basker and Simcoe (2021) document an increase in trademark activity following the introduction of the universal
product codes. At the same time, previous waves of IT investment were associated with economically large productivity increases but mixed results on firm growth measures such as market share
(e.g., Tambe et al., 2020), and diffusion patterns for these technologies tended to favor smaller
firms (e.g., Hobijn and Jovanovic, 2001). By contrast, our evidence shows that AI technologies can
stimulate firm growth through product innovation, with effects that are especially pronounced
4 Another strand of literature measures workers’ exposure to AI and its impact on labor market outcomes. See Felten
et al.(2018; 2019), Webb (2020), Grennan and Michaely (2020), and Acemoglu et al. (2021), among others.
5 Our firm-level measures of AI investments based on human capital are complementary to recent work that measures technology adoption using survey data (e.g., Brynjolfsson and McElheran 2016; Acemoglu et al. 2022). To foster
further research on the economic impact of AI, all code to generate our AI investment measures and our firm-level AI
data will be publicly available on the authors’ websites.

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in large firms. Given the unique ability of AI to process large amounts of data and the fact that
large firms accumulate more data, AI appears to reduce the costs of product development that are
especially high for large firms (Akcigit and Kerr, 2018), allowing these firms to scale more easily.
Methodologically, our paper offers a new approach to measure firms’ intangible capital based
on human capital, with a specific application to capturing investments in AI. Despite ongoing
efforts to incorporate more comprehensive measures of intangibles in the U.S. at the national level
(Corrado et al. 2016), most firm-level measures of intangible capital use cost items such as R&D
and SG&A (e.g., Eisfeldt and Papanikolaou, 2013; Peters and Taylor, 2017; Crouzet and Eberly,
2019; Eisfeldt et al., 2020). Finer firm-level data on intangible investments, which are available for
some countries (e.g., New Zealand; see Chappell and Jaffe 2018), are generally not available for
U.S. firms. Our methodology offers a new measure of intangibles that is consistent across firms
and sectors and can be applied to measure various forms of intangible assets, especially those
based on human expertise. For example, while our focus is on AI investments, we are also able to
measure firm investments in robotics, non-AI information technology, and non-AI data analytics.
More broadly, our AI measure contributes to the growing literature that uses textual analysis to
construct measures of intangibles such as human capital and innovation. For example, Hoberg
and Phillips (2016) analyze text of 10-K filings to create measures of firms’ product markets, Fedyk
and Hodson (2019) use textual analysis to measure firms’ focus on technical skills, Kogan et al.
(2019) construct occupation-specific indicators of technological change using patent text, Argente
et al. (2020) employ textual analysis to map patents to products, and Babina et al. (2020) uses
patent text to build a new comprehensive measure of technological entrepreneurship.
Finally, we contribute to the active debate on the causes and consequences of rising industry
concentration (e.g., Gutiérrez and Philippon, 2017; Syverson, 2019; Covarrubias et al., 2019; Autor
et al., 2020). One proposed channel is that intangible assets propel growth of the largest firms, contributing to increased industry concentration (e.g., Crouzet and Eberly, 2019). Our results support
this hypothesis and suggest that technologies like AI can contribute to increased concentration
by enabling large firms to grow even larger through increased product innovation, reinforcing
winner-take-most dynamics.

1

Artificial Intelligence: Background and Mechanisms

According to the Organisation for Economic Co-operation and Development (2019), an AI system is defined as a “Machine-based system that can, for a given set of human-defined objectives, make
predictions, recommendations or decisions influencing real or virtual environments.” We provide a brief
overview of the current commercial use and key features of AI, followed by a discussion on eco-

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nomic mechanisms through which AI investments might benefit a broad range of firms.

1.1

Artificial Intelligence: A Brief Overview

Commercial applications and investments in AI have increased exponentially over the past decade.
While there are no systematic data on AI investments by firms, recent estimates hover around $140
billion globally per year with estimated growth of nearly 100% over the next three years.6 Historically, the U.S. is considered to be the leader in both academic research and private investments in
AI, but other regions including China and the E.U. have recently been spearheading their investments (Knight, 2017). There has also been an expansion of AI investments across industry sectors.
While the tech sector was an early adopter of AI, surveys of executives indicate widespread adoption of AI technologies by firms in all industries (see here for a survey by McKinsey).
Academic research in AI has flourished for decades since John McCarthy coined the term in
1955 (McCarthy et al., 1955).7 The recent explosion of commercial interest in AI in the private sector is driven by supply-side factors: rapid accumulation of data, decreasing costs of computation,
and advances in methodologies, including deep learning (Hodson, 2016). In terms of commercial
applications, three key areas of artificial intelligence have captured the bulk of private sector investments: machine learning, natural language processing, and computer vision (see here for a
survey by Deloitte in 2018).8 These core techniques are united by their ability to perform highskilled, non-routine tasks, such as prediction, detection, and classification (Agrawal et al., 2019).
Their main distinction from traditional methods of data analysis consists of these techniques’ ability to learn from vast quantities of high-dimensional data (including text, speech, and image data;
Hauptmann et al., 2015) and significantly improve the accuracy of predictions. For example, the
ImageNet challenge in 2012 led to an almost halving of image recognition error rates (relative to
traditional methods), which launched large corporate interest in the computer vision space.9
AI has several key economic properties. First, AI is a prediction technology, and predictions
are at the heart of decision-making under uncertainty—faced by firms in all aspects of their operations. As a result, the ability to perform better predictions with AI can create new business
opportunities. Second, economists have argued that AI may be a general purpose technology
(GPT): AI can be leveraged across different business segments and sectors to solve a wide range
of business problems. Well-known examples of GPT include the steam engine, electricity, and internal combustion. Third, investments in AI center around human expertise, with complementary
6 See the PitchBook AI & ML Emerging Tech Report 2021 here.
7 A brief history of AI research can be found here.
8 While our focus is on artificial intelligence technologies rather than specific automation technologies like ATMs

and industrial robots, our measure does incorporate relevant recent robotics technologies (e.g., autonomous vehicles,
vision-guided robots) that are highly related to computer vision and machine learning technologies.
9 See ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012)

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investments in computing technology and data infrastructure. This differs from technologies that
require mainly capital investments, such as industrial robots. As such, AI is an intangible asset,
reflecting the broader shift towards intangible capital (Mihet and Philippon, 2019). The fourth key
feature of AI technologies is that they are information goods with non-rival uses: new algorithms
are usually published openly and can be used simultaneously by many firms. However, the extent
to which AI can benefit firms depends to a large extent on who owns big data—the key input to
AI technologies (Fedyk, 2016; Jones and Tonetti, 2020).

1.2

Artificial Intelligence and Firm Growth: Mechanisms

It is an open question whether and how investments in AI technologies benefit firms. On the one
hand, as a potential general purpose technology, AI might spur economic growth. On the other
hand, current attention to AI may be over-hyped (Mihet and Philippon, 2019), or AI may still be
too early in the adoption cycle to have a meaningful impact on firm growth (Brynjolfsson et al.,
2020). Below, we discuss two non-mutually-exclusive channels through which investments in AI
can affect firm growth: (i) by increasing product innovation, e.g. through new product creation,
improved product quality, and product customization, and (ii) by lowering operating costs, e.g.
by replacing labor.
AI as a Driver of Product Innovation. An important mechanism for firm growth is through
product innovation and the expansion of product varieties. For example, Hottman et al. (2016)
quantify that practically all variation in firm growth in sales can be explained by variation in
firms’ product appeal to customers (due to either the quality of firms’ products or customer tastes)
and product scope (the number and variety of products). Importantly, Braguinsky et al. (2020)
point out that product variety and product appeal, which are commonly treated as primitives,
are actually determined endogenously through experimentation by firms, and other work also
stresses the importance of knowledge accumulated through exploration and experimentation for
product innovation (Klette and Kortum, 2004; Bustamante et al., 2020).
As a prediction technology, AI can potentially affect both aspects of product innovation highlighted by the prior literature: (i) innovations that overcome supply-side frictions to create new
products or products of superior quality, and (ii) demand-driven innovations that increase product scope by better tailoring products to customer tastes. According to surveys of executives,
enhancement of existing products and services and the creation of new ones is the top use of AI
to date (see here for a survey by Deloitte).
The potential of AI to help create new or better products comes from the insight that product innovation is costly: it requires (often lengthy) experimentation, and its resulting benefits are
uncertain, creating supply-side barriers to product creation and improvement (Akcigit and Kerr,

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2018; Braguinsky et al., 2020). The ability of AI algorithms to quickly analyze large datasets and
learn about the underlying relationships in the input data can potentially lower these barriers
by reducing the uncertainty of experimentation and making the learning process more efficient,
which leads to more product innovation (Cockburn et al., 2018). In practice, recent years show
a number of ways in which AI has enabled or sped up the product innovation process. An important application is drug development, where AI can shorten the drug development life cycle.
For example, at Moderna, AI algorithms have been leveraged to design and optimize mRNA constructs, contributing to the development and the production of the first dose of the COVID-19 vaccine in just 65 days, a process that would previously take years.10 In addition, AI algorithms can
help innovate on the quality of existing products and services by building AI models directly into
products. For example, in Online Appendix A.1, we offer detailed case studies of the applications
of AI, which show examples of AI empowering the introduction of the AI-driven trading platform DeepX at JPMorgan (which allows for faster and cheaper execution of trades) and “smart”
machinery at Caterpillar (which improves machine safety and flexibility).
At the same time, AI can also contribute to increased product scope by helping firms learn
about customer preferences more efficiently and, therefore, better tailor product and service offerings to customers’ tastes and needs. An extensive literature in trade economics highlights the
importance of information regarding target market conditions—especially firms’ product appeal
to the target customers—for firms’ decisions to enter new export markets (e.g., Dickstein and
Morales 2018; Berman et al. 2019). Analogous hurdles apply to firms’ decisions to launch new
products or expand their product variety in domestic markets: there is uncertainty regarding
what customers want and how customer preferences might change. Using AI to analyze customer data can potentially enable firms to overcome this hurdle, providing “the right product
on a hyper-individualized basis” (Hodson, 2016) and overcoming frictions in firms’ demand accumulation processes (Foster et al., 2016; Argente et al., 2021). For example, data on individual
behaviors, such as web browsing and location history and other digital footprints, can enable better approximations of parameters entering individual demand functions than pure demographic
information, leading to more heterogeneity in products tailored to customers with different tastes
(Mihet and Philippon, 2019). This application of AI mirrors recent theories of big data as generating more precise forecasts regarding which product lines will yield the highest value (Bustamante
et al., 2020; Farboodi and Veldkamp, 2021): by better learning about consumer preferences for different products, AI can help direct firms’ efforts towards the development of the most promising
10 See here, where Dave Johnson, Moderna’s VP of Informatics, Data Science, and AI, explains how Moderna was

able to develop a COVID vaccine so quickly: "We very purposely designed all this infrastructure that we think of as an
AI factory, in order to rapidly deliver algorithms from concept to production, to enable our scientists to leverage the
power of AI in their daily jobs. [...] That allows our scientists to design novel mRNA constructs, use AI algorithms to
optimize them, and then order them from our high throughput preclinical scale production line."

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products.
Overall, AI can theoretically reduce the costs of product innovation and provide a mechanism
for the key drivers of firm growth highlighted by Hottman et al. (2016). AI can potentially help
firms increase both (i) their product appeal via improvements in product quality and the creation
of new products; and (ii) their product scope via improved inference of customer preferences and
the customization of products to those preferences. Empirically, if AI enables product innovation,
we should observe that AI investments are associated with increased product creation and variety.
AI as a Driver of Lower Operating Costs. Technological innovations also often aim at lowering costs of existing operations and improving productivity (e.g., Basu et al. 2001; Cardona et
al. 2013; Acemoglu et al. 2020). When it comes to AI, the technology can lower costs and increase productivity in at least two ways. First, AI can potentially replace human labor for some
tasks (Agrawal et al., 2019), cutting per-unit labor costs. Specifically, the ability of AI to aid in the
decision-making process and in solving complex cognition problems has led to concerns that AI
can disrupt many high-skill and high-wage occupations, in contrast to previous waves of technology adoption (Webb, 2020). Second, AI can increase operational efficiency through better forecasting (Mihet and Philippon, 2019). For example, Bajari et al. (2019) examine the impact of big data in
the context of Amazon’s retail forecasting system. Theoretically, this aspect is explored by Tanaka
et al. (2019), who present a model of firm input choice under uncertainty and costly adjustment,
where forecast errors result in under- or over-investment.
The potential to use AI-based forecasting for streamlining firms’ existing operations can also
be seen in our data. The case studies in Online Appendix A.1 highlight how AI-enabled forecasting improves firm operations across a variety of industries: for example, AI workers at JPMorgan
Chase model default of non-performing loans; Caterpillar leverages AI for inventory management; and UnitedHealth uses AI to support efficient medical billing. Empirically, these potential
improvements can manifest through lower operating costs or higher firm-level productivity.

2

Data

We propose a new measure of firms’ investments in AI based on their intensity of AI-skilled hiring. Relative to the prior literature, which has a dearth of firm-level data on AI investments, we
provide a uniquely comprehensive perspective on firm-level AI investments by simultaneously
measuring firms’ demand for AI workers through job postings and the stock of AI workers through
employment profiles. We detail each dataset and describe our sample construction.

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2.1

Job Postings from Burning Glass

The first dataset we use covers over 180 million job postings in the United States in 2007 and 2010–
2018. The dataset is provided by Burning Glass Technologies (BG in short) and draws from a rich
set of sources. BG examines more than 40,000 online job boards and company websites, aggregates
the job postings data, parses them into a systematic, machine-readable form, and creates labor
market analytic products. The company employs a sophisticated deduplication algorithm to avoid
double counting vacancies that post on multiple job boards. BG data contain detailed information
for each job posting, including job title, job location, occupation, and employer name. Importantly,
the job postings are tagged with thousands of specific skills standardized from the open text in
each job opening. The main advantages of the BG dataset are the breadth of its coverage and the
rich detail of the individual job postings. The dataset captures the near-universe of jobs posted
online and covers approximately 60–70% of all vacancies posted in the U.S., either online or offline.
Hershbein and Kahn (2018) provide a detailed description of the BG data and show that their
representativeness is stable over time at the occupation level.
We focus on jobs with non-missing employer names and at least one required skill. About 65%
of job postings have employer information and 93% of job postings require at least one skill.11 We
also drop job postings that are internships. We then match the employer firms in the remaining
job postings to Compustat firms. This step is necessary to aggregate job postings to the firm level
and merge with other firm-level variables. We perform a fuzzy matching between firm names
in BG and Compustat after stripping out common endings such as “Inc" and “L.P.". For observations that do not match exactly on firm name, we manually assess the top ten potential fuzzy
matches by looking at the firm name, industry, and location. Out of 112 million job postings with
non-missing employer names and skills, 42 million (38%) are matched to Compustat firms. This
slightly overrepresents employees of publicly listed firms, which constitute just over one fourth
of U.S. employment in the non-farm business sector (Davis et al., 2006).

2.2

Employment Profiles from Cognism

We complement job postings with employee resumes, which allow us to measure the actual stock
of AI workers at each firm and help address potential concerns around job postings: for example,
if a firm is not able to hire despite active job postings, if a firm posts numerous job openings for
one job, or if a firm on-boards AI talent through acquisitions rather than through direct hiring. We
leverage a novel dataset of approximately 535 million individual profiles provided by Cognism,
an aggregator of employment profiles for lead generation and client relationship management ser11 Job postings with missing employer names are primarily those listed on recruiting websites that mask the employers’ identities.

12
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vices. Cognism obtains the resumes from a variety of sources, including publicly available online
profiles, collaborations with recruiting agencies, third party resume aggregators, human resources
databases of partner organizations, and direct user contributed data.12 These data are introduced
and described in detail in Fedyk and Hodson (2019). While the data slightly over-represent highskilled employees, they cover approximately 64% of the entire U.S. workforce as of 2018 and offer a
representative breakdown across industries. For each employment record listed by the individual,
we see the start and end dates, the job title, the company name, and the job description. Individuals may also list their patents, awards, and publications. Cognism’s AI Research department
leverages techniques from machine learning and natural language processing, including named
entity disambiguation and graph-based modeling methods, to further enrich the resume data by
normalizing job titles and occupations, associating employees with functional divisions and teams
within each firm, and identifying institutions, degrees, and majors from education records.13
We match employer names in the Cognism data to the names of publicly traded firms using a
similar approach to matching employers in BG data to Compustat firms. Fedyk and Hodson (2019)
provide further details on the procedure as applied to the resume data. The matching of individual
resumes to firm entities is performed dynamically to account for acquisitions and divestitures.
Of the 657 million US-based person-firm-year employment records between 2007 and 2018, 120
million (18%) are matched to U.S. public firms. This is consistent with approximately 26% of
overall U.S. employment being accounted for by publicly listed firms (Davis et al., 2006). The
sample of 120 million person-firm-years matched to U.S. public firms is comprised of 19 million
distinct individual employees.

2.3

Additional Data Sources

We merge the Burning Glass job postings data and the Cognism resume data to several additional data sources. We collect commuting-zone-level wage and education data from the Census
American Community Surveys (ACS), industry-level wages and employment data from the Census Quarterly Workforce Indicators (QWI), and academic publications from the Open Academic
Graph (described in detail in Appendix A). Firm-level operational variables (e.g., sales, employment, market value) come from Compustat.
12 The processing of all profiles is compliant with the applicable GDPR and CCPA regulations.
13 The data snapshot is from July of 2021. Following Tambe et al. (2020), we only use the years through 2018, because

the lag in workers updating their resumes could otherwise add significant noise to our measures.

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3

Methodology and Descriptive Evidence

3.1

AI Investments from Job Postings (Burning Glass)

We take advantage of the detailed information on required skills in the job postings data to propose a new data-driven methodology for identifying AI-related jobs. Other work classifies job
postings based on the presence of key terms from a pre-specified list,14 which is likely to suffer
from both Type I (incorrectly labeling tangentially-related employees as AI-related) and Type II
(missing real AI skills that did not make the initial dictionary) errors due to the arbitrariness of
the list of keywords. This is especially relevant in a quickly-evolving domain such as AI, where
new emerging skills can easily be missed. Our methodology circumvents these challenges by
learning the AI-relatedness of each of approximately 15,000 unique skills directly from the job
postings data, based on their empirical co-occurrence (within required lists of skills across job
postings) with unambiguous core AI skills. We then aggregate the skill-level measure to the job
level by generating a continuous measure of AI-relatedness for each job posting, from which we
can classify employees into AI-skilled workers and non-AI-skilled workers.
To measure the AI-relatedness of each skill, we calculate the skill’s co-occurrence with Artificial Intelligence (AI) and its three main sub-fields: machine learning (ML), natural language
processing (NLP), and computer vision (CV):
wsAI =

# of jobs requiring skill s and (ML, NLP, CV or AI in required skills or in job title)
# of jobs requiring skill s

Intuitively, this measure captures how correlated each skill s is with the core AI skills. For example,
the skill “Tensorflow” has a value of 0.9, which means that 90% of job postings with Tensorflow
as a required skill also require one of the core AI skills or contain one of the core AI skills in the
job title. Hence, a “Tensorflow” requirement in a job posting is highly indicative of that job being
AI-related. On the other hand, the AI-relatedness measure of the skill “Microsoft Office” is only
0.003. We list the skills with the highest AI-relatedness measures in Online Appendix Table A1.
We define the job-level AI-relatedness measure ω jAI for a given job posting j as the mean skilllevel measure wsAI across all skills required by job posting j. We transform the continuous AI
measure into a binary indicator by defining each job posting j as AI-related if the measure ω jAI is
above 0.1, a threshold that captures the full range of AI-related technical jobs while minimizing
false positives based on manual inspection of the data. The firm-level measure Share AI
f ,t is then
defined as the fraction of job postings by firm f in year t that are AI-related (i.e. ω jAI > 0.1).15
14 For example, Hershbein and Kahn (2018) classify jobs as requiring cognitive abilities if any listed skills include at

least one of the following terms: “research,” “analy-,” “decision,” “solving,” “math,” “statistic,” or “thinking.” Similar
bag-of-words approaches with pre-specified search terms are used to identify AI-related employees (e.g., Alekseeva et
al., 2020; Acemoglu et al., 2021).
15 Throughout our empirical analyses, we focus on jobs that are matched to Compustat firms. Online Appendix

14
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We use a discrete classification for ease of interpretability and consistency with the resume-based
measure in Section 3.2, but we show in Section 4.1 that the results are robust to: (i) alternative
cut-offs (e.g., 0.05 and 0.15), and (ii) using the continuous measure ω jAI aggregated to the firm
level.
Online Appendix Table A2 provides examples of AI and non-AI job postings. For each job, the
continuous AI measure is the average AI-relatedness of all required skills. Our measure enables
us to capture a wide range of AI-related jobs, from data scientists to speech recognition scientists
to autonomous vehicle engineers. While many AI-related jobs are data scientists and similar dataanalysis-related jobs, our measure differentiates data-analysis jobs specifically related to AI (job
postings numbered 6-10) from data-analysis jobs that are not specific to AI and that focus on more
traditional statistical methods (job postings numbered 11-15). In addition, we further ensure that
our measure is not picking up general programming or statistics skills not specific to AI by showing (in Section 4.1) the robustness of our results to manually refining our measure. In particular,
we screen out skills that represent general programming languages (e.g., Python) or statistics (e.g.,
linear regression) and only keep skills that relate specifically to AI, including AI methodology or
algorithms (e.g., supervised learning) and AI software (e.g., Tensorflow). This process, curated by
the AI-trained personnel at the AI for Good Foundation, categorizes the 700 skills that have an
AI-relatedness measure above 0.05 and are required in at least 50 job postings into “narrow” and
“broad” AI skills. This refinement mainly leaves out skills with relatively lower AI-relatedness
measures and empirically has little effect on the results.16

3.2

AI Investments from Resumes (Cognism)

In the Cognism resume data, we identify AI-related employees as those whose job positions directly involve AI. We begin with the set of 67 keywords in Online Appendix Table A1, which have
the highest skill-level AI-relatedness measures. We then search for these terms in every employment record of each individual in the resume data to see whether: (i) that job (role and description)
directly includes any of the identified AI terms; (ii) any patents obtained during the year of interest or the two following years (to account for the time lag between the work and the patent grant)
include these AI terms; (iii) any publications during the year of interest or the following year include the AI terms; and (iv) any of the identified AI terms appear in awards received during the
year of interest or the following year. If any of these conditions are met, then that person at that
firm in that year is classified as an AI-related employee. For example, jobs with titles such as
Figure A1 plots the share of all job postings and the share of AI-related job postings that are matched to Compustat in
each year. Although publicly listed firms constitute 38% of all job postings, they account for approximately half of all
AI-related job postings. This suggests that, on average, publicly-listed firms hire more AI workers than private firms.
16 For example, among the 50 skills with the highest AI-relatedness measures, 49 are classified as narrow AI skills (the
single exception is “statsmodels,” a Python package for general statistical analysis).

15
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“senior machine learning developer” or job descriptions such as “develop chatbots using Python
with Tensorflow and deep learning models" are identified as AI jobs.
After classifying each individual in each year, we use the number of AI-related employees and
the number of total employees at each firm in each year to compute the percentage of employees
of that firm in that year who are classified as AI-related. Given that our empirical analyses focus
on U.S.-listed firms, our firm-level measure focuses on the employees who are based in the U.S.

3.3

Summary Statistics and Validation

We examine both of our constructed measures of AI investments, confirm that they display intuitive properties, and discuss how our resume data help address potential limitations of measuring
AI investments through job postings. Validating our novel measure is challenging, given the lack
of existing firm-level measures of AI investments. However, we show that our measure displays
a number of intuitive properties, captures specifically AI investments, and does not suffer from
biases such as firms investing in AI by acquiring AI startups.
First, we document that both measures—based on job postings and resumes—display a natural
rise over time, increasing more than seven-fold from 2010 to 2018. Panel (a) of Figure 1 shows that
the fraction of AI-skilled job postings starts out at 0.1% in 2010, rises monotonically over time
(with the increase speeding up from 2014 to 2018), and peaks at 0.8% in 2018. Panel (b) shows
analogous patterns in the resume data. The fraction of all employees who are classified as AIrelated starts at 0.04% in 2007 and reaches 0.29% in 2018. There is substantial heterogeneity in
the growth in AI-skilled labor across individual firms, which provides the variation needed to
examine the relationship between AI investments and firm outcomes. For the entire sample of
public firms, while a median firm sees an increase of 0% (0%) in the resume-based (job-postingsbased) measure, this increase is 0.35% (1.33%) at the 90th percentile, 0.62% (2.99%) at the 95th, and
2.22% (8.11%) at the 99th percentile.
It is helpful to put into perspective the incidence of AI-skilled workers among U.S. employees.
While AI workers constitute a relatively small fraction of total employment, skyrocketing demand
for AI skills and correspondingly high salaries that they command—on the order of millions of
dollars for prominent AI-researchers (Gofman and Jin, 2020)—suggest that AI-skilled workers are
similar to other specialized, high-skilled, high-wage jobs. For example, in terms of the technological and innovative nature of their work, AI-skilled workers could be compared to inventors. Inventors also tend to be highly paid and represent around 0.13–0.24% of the U.S. workforce, which
is similar in prevalence to AI workers.17 Overall, while AI workers form a small fraction of the
17 These estimates come from the USPTO patent data (Babina et al., 2020), where 0.13% is the share of U.S. workers
who file patents in a given year, and 0.24% is the share of U.S. workers who file patents over a three-year period.

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overall workforce, it is helpful to contextualize their impact against that of executives (Bertrand
and Schoar, 2003) and patent inventors (Kline et al., 2019), both of whom are similarly small, highskilled groups of employees that can nonetheless disproportionately affect firm outcomes.
Second, we document that the increase in AI jobs displays an intuitive distribution across
industries. Panel (a) of Figure 2 plots the average share of AI-related jobs in the job postings data
for public firms in each of the 2-digit NAICS sectors, separately for the years 2007–2014 and 2015–
2018. Panel (b) repeats the same analysis for the share of AI-related employees in the resume data.
The figure highlights that the share of AI job postings (resumes) is highest in the “Information”
sector, growing from 0.57% (0.15%) in the early years of 2007–2014 to 1.68% (0.50%) in the later
period of 2015–2018. However, almost all sectors see a meaningful increase in both measures,
supporting the notion that AI is a general purpose technology (Goldfarb et al., 2019). The ability
of our measures to pick up AI investments in a broad cross-section of economic sectors highlights
a key advantage of our human-capital-based approach.
Third, intuitively, AI investments correlate positively with increased R&D expenditures. For
example, changes in the resume-based share of AI workers from 2010 to 2018 display a correlation
of 0.27 with changes in log R&D expenditures over the same time period, controlling for industry
fixed effects. The pattern of AI-investing firms increasing research and development (R&D) expenditures supports the notion that AI investments involve a great deal of experimentation with
applying the new technology (Braguinsky et al., 2020).
Fourth, digging deeper into the skills and jobs with the highest AI-relatedness measures according to our methodology, we observe that our measure is indeed capturing the essence of AI
investments by firms. The skills with the highest AI-relatedness measures, presented in Online
Appendix Table A1, are highly AI-specific skills, such as “Tensorflow” and “Random Forests,”
while general data-analytics-related skills have low AI-relatedness measures: for example, the
measure is equal to 0.04 for “Data Modeling” and 0.03 for “Quantitative Analysis.” Similarly, Online Appendix Table A3 shows that the job titles associated with the highest job-level measures
of AI-relatedness are all very relevant postings such as “Artificial Intelligence Engineer” (average
AI-relatedness measure of 0.497), “Senior Data Scientist - Machine Learning Engineer” (0.394),
and “AI Consultant” (0.369). Since we do not require information contained in job titles of job
postings to identify AI-related skills and jobs, these patterns provide additional validation that
our measure captures relevant AI positions.
As a further validation, it is worth noting the geographic locations of the identified AI jobs.
We aggregate firm-level AI investments of Compustat firms to the commuting zone level and link
the commuting-zone-level changes in the share of AI workers from 2010 to 2018 to 2010 commuting zone characteristics from the Census American Community Survey. Online Appendix Figure

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A1 displays a heat map of the growth in the average job-level AI relatedness measure from 2010
to 2018 and shows that there is significant variation in AI investments across commuting zones.
Online Appendix Figure A1 (a) shows a strong positive relationship between the change in the
share of AI workers from 2010 to 2018 and the average commuting-zone-level log wage in 2010.
Online Appendix Figure A1 (b) demonstrates that the growth in AI workers is also concentrated in
commuting zones with a large fraction of college-educated workers. These patterns are intuitive,
given that AI employees tend to be high-skilled technologically-oriented workers, and contrast
with investments in robotics, which concentrate in areas with larger shares of manufacturing employment (Acemoglu et al., 2020).
Finally, we observe a high correlation between our two measures of AI investments (see Table
1), which helps address the concern that firms’ job postings may not be sufficient to capture firms’
actual hiring of AI talent. For example, if a firm is unable to fill AI-related vacancies, the job
postings measure will overstate that firm’s investments in AI. In practice, this does not appear to
be a main driver of the job-postings-based measure, because the two measures of AI investments—
using job postings and resumes—yield similar results throughout the remainder of the paper.
High correlations and consistency across our two measures also address the potential concern
that job postings do not reflect firms investing in AI by acquiring other firms (e.g., AI startups).
Human capital on-boarded through acquisitions is captured by the resume data, where employees
of acquisition targets are counted as employees of the acquirer subsequent to the acquisition. This
argument (that AI-skilled labor is not mainly acquired via acquisitions) is also supported by the
smooth increase (without jumps) of most firms’ AI workers (representative time series for a few
large firms are presented in Online Appendix A.1) and by industry reports estimating that 90% of
firms’ investments in AI are internal, with only 10% is spent on acquisitions (Bughin et al., 2017).

3.4

Firm-level Determinants of AI Investments

We consider the determinants of investments in AI technologies and document that larger firms
and firms with higher markups, cash reserves, and R&D tend to invest in AI more aggressively.
Our focus is on understanding the use of AI technologies by a wide range of firms, rather
than the invention of new AI tools. For that reason, we exclude firms in the tech sector (2-digit
NAICS 51 or 54) from our main empirical analyses in this and the following sections.18 Our main
regression sample is comprised as follows. In 2010, there are 3735 U.S.-listed public firms that
have non-missing industry codes, positive sales and employment, and are not in the tech sector.
18 In later analyses, we confirm that the main effects of AI spurring firm-level growth are also present in these indus-

tries. A complementary analysis of the impact of AI on specifically AI-inventing firms is provided by Alderucci et al.
(2020).

18
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Among these firms, 2668 are matched to Cognism,19 and 1933 are matched to Burning Glass. For
the Cognism sample, we further restrict to firms with at least 20 U.S. jobs in both 2010 and 2018 to
ensure good coverage of the firm’s workforce, which leaves us with 1993 firms. For the Burning
Glass sample, we further restrict to firms that are also matched to Cognism, so that we can crossvalidate with the actual hiring, leaving us with 1192 firms.
In Table 2, we examine which ex ante firm characteristics predict future growth in firm-level
AI investments. For each measure of AI investments, we estimate the following specification:
∆ShareAIWorkersi,[2010,2018] = βFirmVariablei,2010 + IndustryFE + ei ,

(1)

where ∆ShareAIWorkersi,[2010,2018] denotes the change in the share of firm i ’s AI-related employees
(job postings) from 2010 to 2018 in Panel 1 (Panel 2). All regressions include 2-digit NAICS industry fixed effects. Here and throughout all subsequent analyses, the ∆ShareAIWorkersi,[2010,2018]
variables are standardized to mean zero and standard deviation one to aid in economic interpretation. FirmVariablei,2010 represents one of the ex ante firm characteristics of interest measured
as of 2010: log firm sales in column 1, the ratio of cash to total assets (Cash/Assets) in column
2, the ratio of R&D expenditures to sales (R&D/Sales) in column 3, revenue total factor productivity (TFP)20 in column 4, log markup measured as the log of the ratio of sales to cost of goods
sold following De Loecker et al. (2020) in column 5, Tobin’s Q defined as market value of assets
divided by book value of assets in column 6, market leverage measured as total debt divided by
market value in column 7, return on assets (ROA) measured as the ratio of net income plus interest
expense to assets in column 8, and firm age in column 9. Column 10 includes all variables in a
multivariate specification. We winsorize all continuous variables at 1% and 99% to limit the influence of outliers, although we confirm in untabulated analyses that, empirically, our results are
little changed by the winsorization. To account for differences in precision in the measurement
of AI investments across firms with different numbers of available observations, the estimating
equation is weighted by each firm’s number of resumes (job postings) in 2010.21
The results reported in Table 2 highlight that ex ante larger firms experience higher growth
in AI investments. For example, using the Cognism-based measure in Panel 1, a one-standarddeviation increase in log sales in 2010 (which equals 2.1) corresponds to the share of AI workers
19 Firms that are not matched to Cognism tend to be either ADRs that do not have many U.S. employees or smaller
firms with few employees.
20 We use standard methodology to calculate revenue TFP as the residual from regressing log real sales on log employment and log capital, controlling for firm fixed effects and year fixed effects: log yit = µi + µt + αls log(lit ) +
αks log(k it−1 ) + ε it . The regression is estimated using OLS separately for each industry. The capital stock is constructed
using the perpetual inventory method. The TFP measure is specific to Cobb-Douglas production functions, while sales
per worker measure labor productivity for more general production functions.
21 Since the numbers of worker resumes and job postings are correlated with firm size, this weighting scheme also
roughly weights firms in accordance to their contribution to the economy.

19
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increasing by 23% of the standard deviation from 2010 to 2018, significant at the 1% level. In
addition, firms with higher starting Cash/Assets, R&D/Sales, and markups also see greater investments in AI, consistent with contemporaneous work of Alekseeva et al. (2020). By contrast,
revenue total factor productivity, firm valuation (Tobin’s Q), market leverage, return on assets, and
firm age do not robustly predict future AI investments. In all further regressions, we control for the
ex-ante firm characteristics that predict firm AI adoption (size, cash, R&D intensity, and markups).
Importantly, the patterns for firm-level demand for AI talent measured with Burning Glass data
are consistent with the results using Cognism data, reinforcing the high correlations documented
in Table 1. This consistency suggests that, in the absence of matched employer-employee data, our
methodology for identifying AI investments from the job postings data can be a good proxy for
firms’ actual AI hiring.

4

AI Investments and Firm Growth

We next document that firms investing in AI technologies grow faster in sales, employment, and
market value, and that this effect cannot be explained by alternative explanations, including reverse causality (e.g., firms on faster growth trajectories investing more in AI) and omitted variables
(e.g., concurrent investments in other technologies or demand shocks driving both firm growth
and AI investments).

4.1

Long-differences Results

We begin the analysis by examining whether firms that invest in AI see faster growth from 2010
to 2018. As is standard in settings with slow-moving processes, such as technological progress
(e.g., Acemoglu and Restrepo, 2020), our primary specification is a long-differences regression of
changes in firm outcomes from 2010 to 2018 on changes in AI investments proxied by the share
of AI workers. This strategy is especially well-suited for our setting because AI investments are
gradual over time (with 70% of firms onboarding AI workers over a span of multiple years), with
effects that may not be immediate. By taking first differences in independent and dependent
variables, the long-differences specification helps to ensure that time-invariant firm characteristics
do not drive the results. In Table 3, we report the estimates from the following regression:
0

∆FirmVariablei,[2010,2018] = β∆ShareAIWorkersi,[2010,2018] + Controlsi,2010 γ + IndustryFE + ei , (2)
where the main independent variable, ∆ShareAIWorkersi,[2010,2018] , captures the change in the
share of AI workers in firm i from 2010 to 2018, standardized to mean zero and standard deviation of one. As in Section 3.4, this analysis focuses on firms in non-tech sectors. IndustryFE are
20
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2-digit NAICS fixed effects.22 Panel 1 reports the coefficients for the resume-based measure of AI
investments, while Panel 2 considers the job-postings-based measure. In columns 1, 3, and 5 we
include only industry fixed effects to examine the unconditional relationship between changes in
AI investments and firm growth. In columns 2, 4, and 6, we include a rich set of controls that
are all measured at the start of the sample period in 2010: (i) the initial firm-level characteristics
that predict changes in AI investments in Section 3.4 (log sales, cash/assets, R&D/Sales, and log
markup) and the log of the firm’s total number of jobs (or job postings)23 ; (ii) characteristics of
the commuting zones (CZ) where the firms are located (the share of workers in IT-related occupations, the share of college-educated workers, log average wage, the share of foreign-born workers,
the share of routine workers, the share of workers in finance and manufacturing industries, and
the share of female workers); and (iii) the log industry-average wage.24 Out of the 1993 (1192)
non-tech firms in the Cognism (Burning Glass) sample in Table 2, 1472 (939) firms have positive
sales and employment in 2018, which are necessary to calculate the dependent variables. We
further restrict the sample to firms with non-missing control variables throughout, to keep the
sample composition stable. This results in a sample of 1052 firms in Cognism and 935 firms in
Burning Glass. The results of the regressions without controls are similar when estimated on the
entire available sample. Summary statistics on key variables for the main regression sample are
provided in Online Appendix Table A4.
In columns 1 and 2 of Table 3, the dependent variable is the firm-level change in log sales from
2010 to 2018. Both measures of changes in AI investments are associated with a significant and
economically meaningful increase in sales growth: a one-standard-deviation increase in the share
of AI workers over an eight-year period corresponds to an additional 15% to 20% growth in sales,
depending on the specification.25 In columns 3 and 4, we find a positive effect on employment
of a similar magnitude to the effect on sales. This suggests that AI is not yet displacing firms’
workforces, at least on net, although we do not rule out the reallocation of labor across different
job functions or tasks. In untabulated analyses, we confirm that the results are similar when using
changes in employee counts in the Cognism resume data rather than Compustat employment.
Columns 5 and 6 show that firms investing in AI also see increases in their stock market valua22 In Online Appendix Table A7, we show that our results are robust to controlling for industry at 3-digit NAICS, 4-

digit NAICS, and 5-digit NAICS level. The coefficient on ∆ShareAIWorkersi,[2010,2018] remains stable, and the standard
error increases as more granular industry controls absorb more of the variation.
23 We control for the log number of jobs to address the concern that the share of AI jobs may be more volatile in firms
with fewer total jobs. This control ensures that the variation in the share of AI jobs is between firms with similar total
numbers of jobs but different numbers of AI jobs.
24 When firms span multiple commuting zones, we calculate commuting-zone-level variables as the weighted average, using numbers of BG job postings in each commuting zone as weights, which restricts the sample in the Cognism
regression analysis to firms that are also matched to the Burning Glass data. The results are similar in magnitude and
economic significance if we only include firm-level controls enumerated in list (i).
25 A one-standard-deviation increase in the share of AI workers is roughly at the 90th percentile of the distribution of
changes in the share of AI workers (see Online Appendix Table A4).

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tions: a one-standard-deviation increase in the share of AI workers is associated with a 15%–23%
increase in the firm’s market value.26 It is worth noting that the inclusion of firm-level, locationlevel, and industry-level controls in even columns (all measured at the start of the sample period
in 2010) generally has little effect on the estimated coefficients. This is consistent with our longdifferences specification already controlling for time-invariant firm characteristics.
The magnitude of the effects in Table 3 is economically meaningful (on the order of a 2% increase in annual sales growth per one-standard-deviation increase in the share of AI workers).
Our results provide initial evidence that AI-skilled labor can have a strong positive relationship
with firm growth. In this context, our results are consistent with prior evidence that certain key,
high-skilled employees—including chief executives, inventors, and entrepreneurs—can have a
disproportionate effect on firm outcomes.
The positive relationship between increases in AI investments and firm growth is ubiquitous
across different sectors of the economy, reinforcing the notion that AI is a general purpose technology. Online Appendix Table A5 displays the results from regressing changes in log sales and log
employment on the change in the share of AI workers, separately for the largest 2-digit NAICS
sectors: (i) Manufacturing, (ii) Wholesale and Retail Trade, (iii) Finance, and (iv) the remaining
non-tech sectors. While we exclude tech sectors from our main analysis, we find that AI also has
an positive relationship with growth for firms in the two tech sectors—Information and Professional and Business Services (see Online Appendix Table A6). Overall, we observe that investments in AI are associated with economically significant increases in firms’ operations, and these
effects are meaningful across key economic sectors.
However, the benefits from AI investments are not evenly distributed across the firm size
distribution. Table 4 shows the relationship between changes in AI investments and firm growth,
across terciles of firms by employment in 2010 (within the firm’s 2-digit NAICS sector), controlling
for initial size and sector-by-size-tercile fixed effects. The effect of AI investments on employment,
sales, and market value is monotonically increasing in the firm’s initial size. The stronger positive
relationship between changes in AI investments and growth among the ex ante larger firms is
consistent with big data and AI technologies having scale effects that favor large firms, which
accumulate large amounts of data as a by-product of their economic activity (Farboodi et al., 2019).
Akcigit and Kerr (2018) highlight that larger firms face constraints on their ability to scale due
to higher costs of new product innovation. The results in Table 4 suggest that AI may provide a
channel through which large firms can combat barriers to innovation and scale by leveraging their
data assets. For example, biotech firms that have accumulated large troves of proprietary samples
of molecular compounds are able to leverage AI tools to obtain an advantage over competitors
26 Market value is defined as total assets (at), minus the book value of common equity (ceq), plus the market value of
common equity (prcc_c times csho).

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(see here for the PitchBook AI & ML Emerging Tech Report 2021).

4.2

Robustness

Our granular data allow us to rule out a number of alternative explanations for our results. We
first discuss measurement strengths of our data and show robustness to using alternative constructions of the AI measure. We then address several identification concerns and end this section
by addressing concerns related to sample selection bias.
Measurement strengths and potential concerns. First, it is worth noting that the resume data
address two important potential measurement concerns regarding job postings data: (i) that the
job-postings-based measure captures only firms’ demand for AI talent and not their actual ability to hire; and (ii) that firms acquire AI expertise through acquisitions, which would not be reflected in job postings. Cognism resume data reflect actual employees, including those onboarded
through acquisitions, and the results are consistent across these richer data and the BG job postings, validating the use of job postings for measuring firms’ AI-skilled human capital.
Second, while our measure is centered on internal AI investments, our rich resume data allow
us to also consider whether firms’ use of external AI solutions might affect the interpretation of
our results. Even external AI software requires internal data management and implementation
guidance by AI-skilled workers to be effective (Fedyk, 2016), and industry reports underscore that
AI-skilled labor is the most critical input to successful deployment of AI programs. Nevertheless,
we leverage the rich detail of the Cognism resume data to confirm that our approach of focusing
on AI workers to identify AI investments is indeed a suitable one. We undertake a deep dive into
case studies of individual firms (see Online Appendix A.1 for examples) and observe that (i) our
measure captures internal AI investments well, and (ii) the use of external AI software solutions
(e.g., IBM Watson, IPSoft Amelia) tends to be complementary to internal AI hiring. In addition, we
process individual job descriptions and job titles in our resume data for any mention of external
AI software (including IBM Watson Studio, Symphony, AyasdiAI, Salesforce Einstein, and about
a hundred other key AI-powered solutions) to construct a proxy for firms’ reliance on external
AI solutions.27 In Online Appendix Table A8, we confirm that our results are robust to directly
including this proxy in our overall measure of AI investments.
Third, we confirm that our results are not sensitive to different methods of constructing firmlevel AI measures. In Online Appendix Table A9, we show that the results are robust to using
firm-level average continuous AI-relatedness measures of job postings based on all skills (Panel
27 The Cognism resume data are especially well-suited to capture the use of external technological solutions, given
Cognism’s emphasis on developing “technographic data” (defined by Cognism as “the technologies that the employee
or company is using”). Cognism advertises these data for two purposes: (i) enhancing technology-providers’ targeted
marketing of their products, and (ii) improving individual firms’ understanding of which technologies are used by
their competitors.

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1) or refined narrow AI skills (Panel 2), which are defined in Section 3.1. The narrow AI measure excludes non-AI-specific skills such as general programming (“Python”) and statistics (e.g.,
“Linear Regressions”); however, Panel 2 shows that this refinement does not affect our results. In
Online Appendix Table A10, we also find similar results when using higher or lower cutoffs for
classifying AI job postings based on their continuous measure of AI-relatedness.
Identification concerns. We conduct several tests to show that our results are not driven by
reverse causality or omitted variables concerns, such as differential growth trends or investments
in other technologies. First, we address the concern that AI-investing firms might already be
on higher growth trajectories, leading to a potential positive bias in our estimates. In Online
Appendix Table A11, we control for past industry-level and firm-level growth in the decade before
our sample period (from 2000 to 2008) and find similar results. In Online Appendix Table A12, we
further confirm that the results are robust to the addition of controls for (i) Tobin’s Q as of 2010,
which proxies for the firm’s future growth opportunities, and (ii) state fixed effects, which control
for growth opportunities and other potential omitted variables at the state level. Furthermore,
Table Online Appendix A13 estimates a predictive regression of firm growth during the later part
of our sample (2015–2020) on growth in AI investments during the earlier part of the sample (20102015).28 The estimates are qualitatively and quantitatively similar to those in Online Appendix
Table 3, with milder magnitudes corresponding to the shorter estimation period (growth from
2015 to 2020 rather than 2010–2018), pointing against reverse causality driving our results.
Second, we leverage our detailed data to address omitted variable concerns related to firms’
potential use of non-AI technologies driving our results—which is an important point, given recent evidence that investments in information technology (IT) are also correlated with firm growth
(Tambe et al., 2020). Our rich data allow us to develop measures of investments in non-AI technologies that parallel the measure of AI investments: for each firm, we measure the percentage
of job postings in each year requiring IT-, robotics- or data-related skills that are not specific to
AI. In Online Appendix Table A14, we test whether our measure of AI investments captures investments in other technologies by estimating the relationship between changes in AI investments
and firm-level growth, controlling for growth in: (i) investments in (non-AI) IT, (ii) investments
in robots, (iii) investments in non-AI data skills (e.g., “Data Cleaning”), and (iv) investments in
non-AI-related data analytics (e.g., “SAS”). The estimated relationship between growth in AI investments and firm growth remains similar with the addition of these controls, confirming that the
documented effects on firm growth are specifically driven by AI rather than by other technologies.
Sample selection bias. A remaining concern is that the long-differences specification requires
AI-investing firms to be present at the beginning (2010) and the end of the sample (2018), and that
28 In these regressions, we can use firm growth estimated through 2020, because this specification does not require
firm AI data (which end in 2018) to go beyond 2015.

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survivorship bias might affect our estimate of the effect of AI. While, by construction, we do not
observe growth of firms that are not present in both 2010 and 2018, we perform the industry-level
analysis in Section 6 for both: (i) only firms in our main sample, and (ii) including entering and
exiting firms. If the composition of firms changes in an important way, then the estimates of the
relationship between growth in AI investments and industry-level growth would be significantly
different across these two samples—which is not the case empirically. Moreover, in the next section we use panel structure of the data—that does not condition on firms being present over the
entire sample period—to show in a dynamic setting that, similar to long-differences specification,
firms grow more following AI investments.

4.3

Dynamic Effects

We augment our long-differences specification by estimating firm growth dynamically following
AI investments. This analysis not only offers additional evidence against reverse causality concerns and AI-investing firms being on differential growth trajectories prior to AI investments, but
also elucidates the lag between AI investments and their realized effects.
We use firm-level panel data to estimate firm growth dynamically around the years of AI investments in a distributed lead-lag model, which allows for continuous variation in the treatment variable (Aghion et al., 2020; Stock and Watson, 2015). This specification is especially wellsuited to our setting, because firms tend to invest in AI on a continuous basis, rather than make
lumpy investments in a single year, which precludes us from examining dynamic effects in a
standard event-study framework with discontinuous treatment (e.g., before and after a lumpy
investment).29 The standard distributed lead-lag model is specified as:
5

Yit =

∑ δk ∆ShareAIWorkersi,t−k + µi + λnt + θst + eit

(3)

k =−2

where ∆ShareAIWorkersi,t−k is the annual change in the share of AI workers from year t − k − 1
to year t − k, normalized to mean zero and standard deviation of one, and Yit is either log sales or
log employment in year t. We include firm fixed effects µi to absorb firm-specific time-invariant
factors, and industry-year fixed effects λnt and state-year fixed effects θst to control for industryspecific and state-specific trends. Each lead-lag coefficient δk captures the cumulative response
of the outcome variable in year t to AI investments in year t − k, holding fixed the path of AI
investments in all other years. As such, specification (3) incorporates both immediate and delayed
responses of firm size to firms’ AI investments.30 The estimated coefficients for the leads can be
29 The percentage of AI-investing firms that only invest in a single year is 29.5%, compared to 70.6% for robotics

(Humlum, 2019).
30 For each firm-year observation of sales or employment between 2010 and 2016, we consider five lags and two leads,

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used as a pre-trend test: if firms investing in AI are on similar growth trends as other firms prior
to AI investments, δk with k < 0 should be statistically indistinguishable from zero.31
Figure 3 reports the coefficients from the lead-lag regressions. The top panel shows that sales
increase following AI investments, but not immediately—it takes two to three years for firms to realize the benefits from AI investments. The cumulative effect of a one-standard-deviation increase
in annual AI investments on log annual sales is 1.5%–2% and remains steady five years out. This
is consistent with the long-differences estimates in Section 4.1, where a one-standard-deviation
increase in AI investments is associated with a 15%–20% increase in sales over eight years. The
bottom panel shows that AI investments have a similar positive effect on firms’ employment. Importantly, there is no evidence of pre-trends in either outcome variable: conditional on the controls
we include, firms that invest more in AI in any given year show comparable sales and employment paths in prior years and start diverging only afterwards. This provides additional evidence
that our results are not capturing the reverse effect of firm growth on AI investments or the effect of omitted variables placing AI-investing firms on differential growth trajectories, helping to
bolster a causal interpretation of our main results.

4.4

Instrumenting AI Investments

To further address endogeneity and measurement concerns, we instrument firm-level changes in
AI investments using variation in firms’ ex-ante exposure to the supply of AI talent from universities that are historically strong in AI research. The core idea is that the scarcity of AI-trained
labor is one of the most important constraints to firms’ AI adoption (e.g., CorrelationOne, 2019),
and universities that are historically strong in AI research have been able to train more AI-skilled
graduates in recent years, enabling firms that typically hire from those universities to more readily
attract AI talent. Since commercial interest in AI became widespread only around 2012, we argue
(and offer empirical support) that firms’ connections to AI-strong universities in 2010 were not
driven by the need to hire AI-skilled workers, especially for the sample of non-tech firms that are
the focus of this paper. To construct the instrument, we compile two datasets on: (i) the ex-ante
strength of AI research in each university, and (ii) firm-university hiring networks. To the best of
our knowledge, there is no comprehensive historical data on either of these two aspects. We now
so that we estimate the cumulative impact of AI investments on firm growth from two years before the investments to
five years after the investments. Since the data on AI investments end in 2018, we include only two leads to keep all
firm-year observations up to 2016. We obtain similar results when including only one lead or no leads at all. Furthermore, the analysis of dynamic effects focuses on the Cognism resume data, because these data offer full coverage of AI
investments going back to 2005. By contrast, Burning Glass job postings data have a more limited time series, where
including 2 leads and 5 lags would restrict the sample to only firm-year observations in 2015 and 2016.
31 It is worth noting that, given that the independent variables in this distributed lead-lag model are changes in
continuous AI investments instead of period dummies as would be the case in a standard event-study framework, we
cannot normalize the estimates to an exact zero for any given period.

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briefly discuss the construction of both datasets, while Appendix A provides a detailed discussion
of these issues.
To identify universities strong in AI research before 2010, we use data from the Open Academic Graph (OAGv2), which provides the most comprehensive openly available repository of
scholarly work since 1870 (Sinha et al., 2015; Tang et al., 2008). We match 689 research institutions in the National Science Foundation’s Higher Education Research and Development Survey
(HERDS) to researchers in the OAGv2 and work with the field experts at the AI for Good Foundation to identify AI-related publications. We classify each AI researcher based on the share of AI
publications in that researcher’s overall portfolio, and we classify universities as AI-strong if their
number of AI researchers is at the top of the distribution over 2005–2009.
A key concern with our instrument is that AI-strong universities are also likely to be strong
in the broader field of computer science (CS), producing more CS-skilled graduates, which might
affect firm outcomes through channels other than AI investments. To address this concern, we also
measure the number of CS researchers in each university to include as a control. In addition, we
verify that AI-strong universities are not strongly correlated with the overall university rankings.
We construct the firm-university hiring networks by leveraging our resume data to observe
the universities granting the degrees of each firm’s employees.32 For the firm-university hiring
networks to provide the necessary variation for our instrumental variable strategy, different firms
need to hire from different sets of universities, and these networks need to be persistent over
time. Our data show evidence of both: each firm tends to concentrate its hiring in a small number
of universities, and ex-ante networks (i.e., which universities each firm hired from before 2010)
strongly predict the universities from which firms hire after 2010 (see Appendix Table 9).
2010 is the share
We define our instrument for each firm i as: IVi = ∑u s2010
iu AIstrongu , where siu

of STEM workers in firm i in 2010 who graduated from university u, and AIstrongu equals one if
university u is identified as an AI-strong university based on pre-2010 publications.33 To control
for the effects of general computer science (and not specifically AI), we construct an analogous
measure of firms’ exposure to ex-ante CS-strong universities: ∑u s2010
iu CSstrongu .
We examine an important identification concern regarding this instrument: if firms anticipated the surge in demand for AI, they could have started building their connections to AI-strong
universities before 2010, making firm-university hiring networks in 2010 endogenous to firms’ de32 Aggregated to the university-year level, our resume data cover, on average, 59% of all degrees conferred by each

university according to IPEDS data, and the number of fresh graduates in the resume data is highly correlated with the
total number of degrees conferred (correlation=0.73) in the IPEDS data. Confirming the relevance of our measure of
AI-strong universities, Appendix Figure 5 shows that the increase in AI-trained graduates during the 2010s was much
more pronounced in ex-ante AI-strong universities than in non-AI-strong universities.
33 We use firm-university hiring networks based on STEM workers to account for potential segmentation in firms’
hiring networks, where business employees may be hired from different universities than technically skilled employees.
However, empirically, firm-university hiring networks constructed from all workers yield similar results.

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mand for AI-trained students. This is unlikely, given the lack of both commercial interest in AI by
firms and AI-skilled graduates by universities prior to 2010 (see Appendix Figure 5). Moreover,
we confirm empirically that firms connected to AI-strong universities in 2010 did not increase their
share of hired fresh graduates from those universities from 2005 to 2010 (see Appendix Table 10).
Appendix Table 11 shows the first-stage results with industry fixed effects and the CS control
included throughout. We sequentially add (i) baseline controls (firm-, industry-, and commutingzone-level controls), (ii) pre-period firm sales and employment growth between 2000 and 2008 to
address unobservable firm characteristics that might simultaneously drive firms’ growth trajectories and their hiring of AI workers, and (iii) state fixed effects to control for local labor market
characteristics that might drive both firms’ AI hiring and and their growth. The instrument has a
strong first stage with F-statistics well above the conventional level of 10 for all specifications using the Cognism resume data. The F-statistics are also above 10 for two out of four specifications
using the Burning Glass job postings data. Intuitively, the first stage is stronger in the Cognism
data because the data generating process for the instrument is based on Cognism resumes and
captures the supply of AI-skilled labor to firms. As a result, we focus on Cognism resume data in
the second stage in Appendix Table 5. The results show a robust and significant effect of AI investments on sales (columns 1–4), employment (columns 5–8), and market value (columns 9–12).
Online Appendix Table A15 shows similar results for the job postings data.

5

Mechanisms

We examine the drivers of AI-fueled firm growth by considering the two non-mutually-exclusive
mechanisms detailed in Section 1. We document that AI-investing firms are able to significantly
increase their product innovation and find no evidence of reductions in operating costs.

5.1

AI as a Driver of Product Innovation

As we outline in Section 1, AI can contribute to firm growth via product innovation by: (i) facilitating the creation of new and improved products, and (ii) increasing product scope through
improved tailoring of products to customer tastes. To explore this empirically, we need firm-level
data on products and services, which are challenging to obtain, especially across different sectors.
We overcome this challenge by using three proxies for firms’ product innovation.
First, we examine whether AI-investing firms experience increases in trademarks, which are
registered whenever new products or services are ready for commercialization and therefore offer a good proxy for the creation of new products and services (Hsu et al., 2021). Columns 1
and 2 in Table 6 present the results from long-differences regressions of changes in firms’ USPTO

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trademarks against growth in their AI investments, showing that AI-investing firms significantly
increase their trademark portfolios.34 Second, columns 3 and 4 reveal a similar relationship between AI investments and the number of product patents, which are patents specifically focusing
on product innovations.35 While trademarks are registered with the creation of new products,
product patents reflect both new product creation and innovations in the quality of existing product lines. We find that a one-standard-deviation increase in the share of AI workers based on
resumes (job postings) over eight years corresponds to a 21% (20%) increase in the number of
product patents.
Finally, we build a measure of changes in firms’ product mix based on the self-fluidity measure
in Hoberg et al. (2014). Using firm 10K filings, Hoberg et al. (2014) take the cosine similarity
between word vectors describing a firm’s product offerings in two adjacent years to measure the
extent to which the firm’s product offerings changed in a given year. These changes reflect both
the creation of new products and the tailoring of existing products to evolving consumer tastes.36
In columns 5 and 6, we find that growth in AI investments is associated with increased changes in
firms’ product mix from 2010 to 2018. For robustness, Online Appendix Table A16 shows that the
instrumented firms’ AI investments also have a positive effect on the number of trademarks, the
number of product patents, and the change in product offerings (although not always significant).
Overall, the results point towards firms utilizing AI to expand product variety and customization,
consistent with surveys of corporate executives, who highlight product improvement and creation
as top uses of AI (see here).
Our findings provide a first piece of evidence for how AI technologies can stimulate growth for
a broad set of firms: the unique reliance of AI on big data reduces the uncertainty of exploration
(Cockburn et al., 2018), facilitates the discovery process for new or better products, and enables
the tailoring of products to customer tastes. Moreover, AI algorithms themselves can be used
as an ingredient in product development and improvement (e.g., AI-powered trading platforms
or self-driving cars). These results are consistent with evidence from technological innovation
during industrialization, where new technologies have been shown to help firms expand through
34 The dependent variable is the change in log(1 + number of trademarks) from 2010 to 2018, so that the regression
takes into account firms with zero trademarks
p in either 2010 or 2018. The results are also robust to using the inverse
hyperbolic sine transformation (i.e., ln( x + (1 + x2 ))). The regression sample is smaller than our baseline sample,
because not all public firms file trademarks (we include firms with at least one trademark in 2009-2018).
35 See Ganglmair et al. (2021) for the methodology to distinguish between product patents and process patents. The
regression sample is smaller than our baseline sample, because not all public firms file patents, and we only include
firms with at least one patent during 2005–2018. The dependent variable is the change in log(1 + number of product
patents) from 2010 to 2018.
36 We use the same word vectors as Hoberg et al. (2014) and construct our measure as follows: for each year, we
calculate the angle between the two word vectors indicating firms’ product offerings in that year and the previous year.
For example, the measure equals 0 if the product offerings remain exactly the same and π/2 if the product offerings
change completely. We sum up of angle of each year over eight years from 2010 to 2018 to measure the total change in
firms’ product portfolios from 2010 to 2018.

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product innovation (Braguinsky et al., 2020).

5.2

AI as a Driver of Lower Operating Costs

We next test whether the increase in firm growth from AI investments could reflects AI technologies lowering firms’ operating costs and increasing firm-level productivity. First, in columns 1 to
4 of Table 7 we look at costs directly by considering how growth in firms’ AI investments relate
to changes in costs of goods sold (COGS) and operating expenses. AI investments are associated
with increases in costs that are similar in magnitude to the growth in firm sales, suggesting that
AI is not associated with lower operating costs.
Second, columns 5 to 8 of Table 7 consider two measures of productivity: sales per worker
(i.e., labor productivity) and revenue total factor productivity (TFP). The relationship between
AI investments and both productivity measures is not significant. The lack of growth in labor
productivity is consistent with the results in Section 4 that AI investments predict similar increases
in sales and employment, challenging the view that the primary effect of AI is to replace jobs.37
Furthermore, in columns 9 and 10, we bring another proxy for efficiency gains that complements
revenue-based measures of productivity: process patents, which reflect process innovations and
potential improvements in efficiency. We find a zero relationship between AI investments and
process innovation, in contrast to the positive increase in product patents documented in Table 6.
Overall, we do not find evidence that investments in AI help firms cut their operating expenses
and achieve productivity improvements. This speaks to the broader debate on the timing of productivity gains from general purpose technologies. Our evidence is consistent with long-standing
arguments in the literature that the adoption of general purpose technologies leads to delayed
productivity benefits (David, 1990; Brynjolfsson et al., 2020). In Online Appendix Table A17, we
examine the effect of changes in AI investments during the first half of the period (2010–2014) on
productivity growth through 2018 and do not find any significant positive effect. Hence, even
with a lag of a few years, AI investments are not yet associated with productivity improvements.

6

AI Investments and Industry-level Outcomes

To shed light on the potential aggregate effects of AI, we examine the relationship between
industry-level variation in AI investments and: (i) industry growth; (ii) industry concentration.
37 It is worth noting that both sales per worker and revenue TFP are revenue-based measures of productivity and may

not fully reflect actual physical productivity. For example, sales per work and revenue TPF may provide downwardbiased estimates of actual productivity changes if quantities produced increase to such an extent that lower prices are
charged (Foster et al., 2008; Garcia-Marin and Voigtländern, 2019; Caliendo et al., 2020). To consider this possibility, in
untabulated analyses we find that there are no changes in AI-investing firms’ markups.

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While AI-investing firms grow faster, the gains in industry sales and employment may be zerosum if the use of AI technologies creates a business-stealing effect on competitors (Bloom et al.,
2013). For example, negative spillovers have been shown to dominate positive firm-level effects
in the case of robotics, leading to an overall negative effect on aggregate employment (Acemoglu
et al., 2020). Hence, signing the aggregate effect of AI investments is an empirical question. We
estimate the following long-differences regression at the industry level:
∆ ln y j,[2010,2018] = γ∆ShareAIWorkers j,[2010,2018] + IndustrySectorFE + e j

(4)

where ∆ ln y j,[2010,2018] is the change in total sales or employment for all Compustat firms (including
those that entered the sample after 2010 or exited before 2018) in 5-digit NAICS industry j, and
∆ShareAIWorkers j,[2010,2018] is the change in the share of AI workers among Compustat firms in
industry j from 2010 to 2018. Analogously to the firm-level tests, the regressions are weighted by
the total number of resumes (or job postings) in each industry in 2010.
Columns 1–4 of Table 8 show that AI investments are associated with a robust increase in employment and sales at the industry level. In both panels (Panel 1: resume-based AI measure, and
Panel 2: job-postings-based AI measure), odd columns estimate the unconditional relationship
(with 2-digit NAICS fixed effects only), and even columns add controls for log employment, log
sales, and log average wages at the industry level in 2010. For example, with the full set of controls, a one-standard-deviation increase in the industry-level share of AI workers in the resume
data is associated with a 19.9% increase in sales and a 23.4% increase in employment. Importantly,
in Online Appendix Table A18, we show that the results remain similar when we restrict the sample to firms that are in the Compustat sample both in 2010 and 2018 (i.e., excluding entrants and
exits). This confirms that sample selection issues are not driving our main results for publicly
traded firms. While we cannot speak to growth effects outside of publicly traded firms (where
sales data are not reported), the fact that our effects concentrate among the largest firms in the
Compustat sample (Table 4) suggests that the net effects on industry growth of all (public and
private) firms are likely milder than those documented in Table 8.38
We next examine whether the higher AI-fueled growth among larger firms is substantial
enough to translate into increased industry concentration. We link industry-level growth in AI
investments to contemporaneous changes in industry concentration from 2010 to 2018. Following
Autor et al. (2020), we use the Herfindahl-Hirschman Index (HHI) to measure industry concentration. To examine winner-take-most dynamics, we also consider the fraction of sales accruing to
38 A caveat with these results is that the Compustat sample assigns each firm to a single main industry, even for

firms that might have operations in several industries. This caveat is unlikely to affect the interpretation of our results,
given that prior research using U.S. Census micro data shows that for a typical U.S. public firm the large majority of its
operations fall within one main industry (Babina, 2020).

31
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the largest firm in each 5-digit NAICS industry among the Compustat firms. Columns 5–8 of Table
8 show a positive relationship between industry-level growth in AI investments and changes in
industry concentration.
Overall, our results support the argument by Crouzet and Eberly (2019) that investments in
intangible assets are responsible for the rise in industry concentration observed in the U.S. data.
Our results suggest that, as a general purpose technology that can be applied across many industries, AI has the potential to further increase concentration across a broad range of industries by
facilitating product innovation and the expansion for the largest firms.

7

Conclusion

In this paper, we study how firms invest in and benefit from one of the most important new technologies of the last decade—artificial intelligence. We introduce a novel measure of investments
in AI technologies at the firm level using two detailed datasets on human capital: job postings
from Burning Glass Technologies, which indicate each firm’s demand for particular skills, and resume data from Cognism, which reveal the actual composition of a firm’s workforce. Our unique
measure allows us to examine both the determinants and the consequences of AI investments by
firms across a wide range of sectors. We find a positive feedback loop between AI investments
and firm size: AI investments concentrate among the largest firms, and as firms invest in AI, they
grow larger, gaining sales, employment, and market share. This AI-fueled growth does not appear to stem from cost-cutting; instead, AI-investing firms expand through product innovation
and increased product offerings.
Our findings highlight important differences between the adoption of AI technologies and the
adoption of information technology (IT) in the 1980s and 1990s.39 Much of the previous literature finds that IT investments were associated with economically large productivity increases but
mixed results on firm growth measures such as market share. By contrast, we observe increased
growth for AI-investing firms, along with increased product innovation, but no evidence (yet) of
higher firm-level productivity. Our results also show higher AI adoption and larger gains from AI
investments for larger firms, which contrasts with prior work on diffusion patterns for IT (Hobijn
and Jovanovic, 2001). These differences underscore the distinctive features of AI relative to previous waves of IT: as a prediction technology, AI facilitates product innovation and creates new
business opportunities by enabling firms to learn better and faster from big data.
Our findings imply that the benefits from AI depend to a large extent on who owns big data—
the key input to AI technologies (Fedyk, 2016). While data are non-rival (data can be used by any
39 See Dedrick et al. (2003) and Cardona et al. (2013) for reviews of that literature.

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number of firms simultaneously), recent theoretical work suggests that, fearing creative destruction, firms may choose to hoard data they own, leading to inefficient use of nonrival data; and
that giving the data property rights to consumers can generate allocations that are close to optimal (Jones and Tonetti, 2020). While our empirical work does not directly speak to the optimality
of data ownership, our results suggest that AI contributes to the increase in industry concentration
and the rise of “superstar” firms documented in recent work (Gutiérrez and Philippon, 2017; Autor et al., 2020). Further understanding how AI affects production processes, corporate strategies,
and organizational structure of firms and assessing the distribution of gains from investing in AI
technologies across firms and workers are fruitful avenues for future research.

33
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[The evaluation harness truncated this reference: showing the first 120000 of 252793 characters.]
</reference>

<statements>
1. Firm-level studies of observed AI investment find faster sales, employment, and market-value growth, mainly through product innovation, but also greater concentration among larger firms.
2. The supplied literature treats AI as a prediction technology that can improve firms’ learning from data and, potentially, act as a general-purpose technology across sectors.
3. The question’s macro-era framing is therefore not directly tested by these sources; the evidence instead supports a more granular view in which AI changes the allocation of tasks, the demand for skills, and the growth paths of adopting firms.
4. The firm-level study by Babina, Fedyk, He, and Hodson builds a measure of AI investment from worker resumes and job postings, using data that include 535 million individual job histories and 180 million job vacancies.
5. The authors report that the fraction of AI jobs grew more than seven-fold from 2010 to 2018, with the highest share in technology but a similar rate of growth across sectors.
6. They also state that their resume data provide high coverage of U.S. jobs and represented more than 64% of full-time U.S. employment as of 2018.
7. A one-standard-deviation increase in the resume-based measure of AI investment over 2010–2018 corresponds to a 20.3% increase in sales, a 21.9% increase in employment, and a 22.4% increase in market valuation.
8. The study addresses causality using long-differences regressions with rich initial controls, robustness to pre-trend controls, predictive tests using earlier AI investment to forecast later growth, and an instrumental-variable strategy based on firms’ ex-ante hiring connections to universities historically strong in AI research.
9. The growth channel is identified primarily as product innovation, reflected in trademarks, product patents, and product updates.
10. AI-powered growth concentrates among ex-ante larger firms, leading to higher industry concentration and reinforcing winner-take-most dynamics.
11. Industry-level growth in AI investments is positively related to changes in industry concentration.
12. In the technology-sector breakdown, growth in AI investment strongly predicts sales and employment growth in the Information sector, but the sales effect in Professional and Business Services is not statistically significant.
13. In non-technology sectors, the positive relationship between AI investment growth and firm growth remains statistically significant across detailed industry fixed effects.
14. Local labor-market analysis finds that higher-wage and more educated areas experience faster growth in AI-skilled hiring.
15. The authors also note that lackluster aggregate productivity growth has raised concerns that AI’s benefits may be over-hyped or may take longer to materialize.
16. The firm-level study finds that observed AI investment predicts faster employment growth at adopting firms
17. Taken together, the evidence suggests that AI can grow firms that adopt it while also reducing hiring in establishments whose task structures make labor substitutable, but neither result yet establishes a first-order aggregate employment effect.
18. Firm-level AI investment grew across sectors, with the highest share in technology but similar growth rates across sectors, and effects persist in non-technology sectors, although the Professional and Business Services sales effect is weaker in the tech-sector breakdown.
19. The strongest industry-level claims are exposure patterns, selected firm-level outcomes, and specific experimental or administrative settings.
20. Firm-level investment study — What the sources establish: Observed AI investment predicts firm sales, employment, and valuation growth, product innovation, and industry concentration
21. Firm-level investment study — What the sources do not establish: Economy-wide net employment effects; the paper notes concerns about over-hyped aggregate productivity benefits
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.