Why generative AI can make creative destruction more creative but less destructive

Pehr-Johan Norbäck and Lars Persson

Small Business Economics, 2024, vol. 63, issue 1, No 13, 349-377

Abstract: Abstract The application of machine learning (ML) to operational data is becoming increasingly important with the rapid development of artificial intelligence (AI). We propose a model where incumbents have an initial advantage in ML technology and access to (historical) operational data. We show that the increased application of ML for operational data raises entrepreneurial barriers that make the creative destruction process less destructive (less business stealing) if entrepreneurs have only limited access to the incumbent’s data. However, this situation induces entrepreneurs to take on more risk and to be more creative. Policies making data generally available may therefore be suboptimal. A complementary policy is one that supports entrepreneurs’ access to ML, such as open source initiatives, since doing so would stimulate creative entrepreneurship.

Keywords: Machine learning; Big data; Generative AI; Open source; Creative destruction; Entrepreneurship; Operational data (search for similar items in EconPapers)
JEL-codes: L1 L2 M13 O3 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11187-023-00829-4

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