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Building the algorithm commons: Who discovered the algorithms that underpin computing in the modern enterprise?
Author(s) -
Thompson Neil C.,
Ge Shuning,
Sherry Yash M.
Publication year - 2021
Publication title -
global strategy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.814
H-Index - 24
eISSN - 2042-5805
pISSN - 2042-5791
DOI - 10.1002/gsj.1393
Subject(s) - algorithm , ibm , competitor analysis , computer science , competitive advantage , power (physics) , big data , productivity , database , economics , data mining , management , materials science , physics , macroeconomics , quantum mechanics , nanotechnology
Research Summary The reach of the modern enterprise relies on the power of information technology (IT) tools such as sensors, databases, and machine learning. But tool improvements must be fueled by increased computing power (e.g., faster hardware) or getting more productivity from existing systems (e.g., through better computer algorithms). New research has uncovered that this second source, algorithm progress, is more important than previously realized—sometimes orders of magnitude more important than hardware—and thus could be an important technological stepping‐stone to give competitive advantage to a country's firms. Analyzing this “Algorithm Commons” reveals that the United States has been the largest contributor to algorithm progress, with universities and large private labs (e.g., IBM) leading the way, but that U.S. leadership has faded in recent decades. Managerial Summary Companies are increasingly tackling problems with big data and sophisticated analysis techniques (e.g., Machine Learning). To meet the increased computational demands of these approaches, the capability of computers must improve. One important technique for doing this is to redesign algorithms, the recipes that computers follow to perform calculations. As a result, firms that develop better algorithms, or get access to them first, can get important advantages over their competitors. This paper shows that it is U.S. corporations and U.S. universities that have produced most of the important algorithm improvements, and thus suggests that better algorithms may have been a source of advantage for U.S. multinationals.