Algorithmic Bilinguals

37 Pages Posted: 24 Mar 2021 Last revised: 12 Apr 2024

See all articles by Prasanna Tambe

Prasanna Tambe

Wharton School, U. Pennsylvania

Date Written: November 1, 2018

Abstract

Using US workforce data, this study tests the hypothesis that generating value from data, algorithms, and AI requires domain experts who can effectively interact with data and algorithms. This decentralization of technical expertise stands in contrast to earlier generations of business technologies for which the complementary skills were primarily embodied in IT specialists and it is due to the task complementarities that arise when integrating decision-making algorithms into production. Using two different workforce data sets, I show that 1) employers have been shifting hiring towards requiring greater expertise with algorithms from domain experts, 2) technical human capital in frontier firms has become more dispersed across occupations, and 3) the market assigns higher value to firms' investments in algorithms when they have made these workforce adjustments, indicating the presence of valuable intangible assets that can yield future productivity benefits. Finally, I show that the recent advance of no-code and natural language tools that make it easier to perform technical work accelerates these changes by lowering the costs of bundling these forms of expertise together. Implications for training, education, and algorithmic decision-making are discussed.

Keywords: skills, algorithms, AI, jobs, occupations

JEL Classification: O32, O33

Suggested Citation

Tambe, Prasanna, Algorithmic Bilinguals (November 1, 2018). Available at SSRN: https://ssrn.com/abstract=3776492 or http://dx.doi.org/10.2139/ssrn.3776492

Prasanna Tambe (Contact Author)

Wharton School, U. Pennsylvania ( email )

Philadelphia, PA 19104
United States

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