Deep Geography: Implications of the Socio-Spatial Structure in Artificial-Intelligence Research for Financial Institutions

30 Pages Posted: 23 Oct 2018

See all articles by Dane Rook

Dane Rook

Stanford University

Ashby H. B. Monk

Stanford University - Global Projects Center

Date Written: September 30, 2018

Abstract

As a leading branch of artificial intelligence, deep learning promises competitive differentiation and more efficient operations for financial institutions. But the geographic and social structure of the deep-learning community complicates access to its most cutting-edge research for many financial institutions. We explore how this socio-spatial structure presents obstacles for incumbent institutions, which disadvantage them against new entrants to financial markets – from whom incumbent institutions are facing threats of disruption. We also probe how geographic distribution of deep-learning research affects the hegemony of the New York-London axis in financial innovation. Consequently, we find a need to revisit how the importance of financial centers is measured and viewed in economic geography. Finally, we investigate how ‘at-a-distance’ involvement by incumbent financial institutions in deep learning generates black-box risks that could harbor significant ramifications for both regulatory policies and systemic stability.

Keywords: Deep Learning, Disruption, Financial Centers, Industrial Organization, Innovation

Suggested Citation

Rook, Dane and Monk, Ashby H. B., Deep Geography: Implications of the Socio-Spatial Structure in Artificial-Intelligence Research for Financial Institutions (September 30, 2018). Available at SSRN: https://ssrn.com/abstract=3258050 or http://dx.doi.org/10.2139/ssrn.3258050

Dane Rook (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Ashby H. B. Monk

Stanford University - Global Projects Center ( email )

United States

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