AI and Jobs: Evidence from Online Vacancies

Acemoğlu, Daron; Autor, David H.; Hazell, Jonathon; Restrepo, Pascual

doi:10.3386/w28257

Cited by 63 publications

(36 citation statements)

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“…We believe our approach complements existing approaches and can be useful to researchers, depending on the specific questions being asked. Some researchers have already used the AIOE, including Fossen and Sorgner (2019), who study how advances in AI create opportunities for growth-oriented entrepreneurship, Goldfarb et al (2019), who use the AIOE to examine whether AI has the characteristics of a general-purpose technology within the healthcare industry, and Acemoglu et al (2020) who use the AIOE to study how a firm's AI exposure affects hiring patterns. It is our hope that future work can continue to investigate how the exposure to AI manifests across occupations, geographies, and backgrounds.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the Burning Glass Technologies (BG) data that we use in one of our validation exercises can be used to study occupational differences across firms. Indeed, Acemoglu et al (2020) match our AIOE measure to firm level BG data to study how a firm's AI exposure affects its hiring of AI skilled and non-AI skilled workers. Interestingly, Acemoglu et al also do the same for the Brynjolfsson et al (2018) and Webb (2020) measures and discuss the differences across the three measures.…”

Section: Competitive and Corporate Strategymentioning

confidence: 99%

“…We discuss differences with existing datasets in more detail below. We believe our methodology has the potential to be used in a variety of applications within the field of strategy and, as we describe below, can be used to construct AI exposure at the firm level as well (see, e.g., Acemoglu, Autor, Hazell, & Restrepo, 2020).…”

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confidence: 99%

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Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Felten

Raj

Seamans

2021

Strategic Management Journal

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We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars. Managerial Summary: Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE)

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Section: Discussionmentioning

confidence: 99%

Section: Competitive and Corporate Strategymentioning

confidence: 99%

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Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Felten

Raj

Seamans

2021

Strategic Management Journal

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“…Such a concern and the associated debates have motivated a quickly growing literature. Recent work by Webb (2020), Acemoglu, Autor, Hazell, and Restrepo (2020), Babina, Fedyk, He, and Hodson (2020), and Jiang, Tang, Xiao, and Yao (2021) have all conducted large-sample analyses on the extent of job exposure and vulnerability to AI-related technology as well as the consequences on employment and productivity.…”

Section: Introductionmentioning

confidence: 99%

“…Burning Glass is currently the leading data vendor in job postings in the U.S. The postings are scraped from web sites, news letters, and agency reports, and cover the period of 2007, and then 2010-2019 Acemoglu, Autor, Hazell, and Restrepo (2020). show that Burning Glass data cover 60-80% of all U.S. job vacancies.9 An incomplete list of recent papers include Aghion, Jones and Jones (2017),Acemoglu and Restrepo (2018),Acemoglu and Restrepo (2019),Brynjolfsson, Mitchell and Rock (2018),Webb (2020),Ray and Mookherjee (2020),Cao, Cong, and Yang (2019),Acemoglu, Autor, Hazell, and Restrepo (2020), and Jiang, Tang, Xiao, and Yao (2021).10 An example in a non-finance setting comes from Sports.…”

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confidence: 99%

From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses

et al. 2021

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An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of "man vs machine," the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI's computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of "machine plus human" (instead of human displacement) in high-skill professions.

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What is digital transformation? Core tensions facing established companies on the global stage

Furr

Özcan

Eisenhardt

2022

Global Strategy Journal

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Research summary Digital transformation is a dominant theme in the global economy, but what it means for established companies remains perplexing for both academics and practitioners. As digital erases familiar geographic, industrial, and organizational boundaries, it has led to simplistic characterizations such as “digital changes everything.” Yet while digital changes some things, others remain the same. Here, we identify three core tensions at the heart of digital transformation—products vs platforms, firms vs ecosystems, and people vs tools—and describe their underlying economics, driving forces, and countervailing forces. These tensions frame a concrete discussion of strategic alternatives for global companies. Overall, we emphasize that digital transformation is not an objective state, but rather a strategic choice by executives from an array of alternatives. Managerial summary Digital transformation is a dominant theme in the global economy, but what it means remains perplexing for executives and academics. Pundits claim that “digital changes everything” and that leaders must “disrupt or be disrupted,” but is this really true for established companies serving robust customer needs on the global stage? Understanding what digital transformation means can be challenging as it breaks down familiar geographic, industrial, and organizational boundaries, creating new opportunities and threats. In this paper, we explore three key tensions at the heart of digital transformation—products vs platforms, firms vs ecosystems, and people vs tools—and enumerate their enabling and constraining forces. Building on these concrete constructs provides effective foundations for formulating digital transformation strategy.

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AI and Jobs: Evidence from Online Vacancies

Cited by 63 publications

References 0 publications

Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses

What is digital transformation? Core tensions facing established companies on the global stage

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