Growth paths in early machine learning ventures: technical configuration and competitive advantage over time

Journal article

Machine learning capabilities have emerged as critical strategic assets, yet little is known about how their technical characteristics affect the competitive advantage of the firms that build them. Through an inductive, multicase study tracking six early-stage machine learning ventures in an accelerator program, we explore how their phases of specialization and exploration may be constrained by properties of the underlying technology. Our findings show how training data, learning paradigms, and model outputs depend on one another and on the applications they serve, and how these relationships change over time. This interdependence shapes firms’ growth paths, alternately expanding or limiting the strategic options available to them. Characteristics that confer an early competitive advantage can later constrain later growth. We discuss the implications for strategy research on competitive advantage and path dependence in machine learning ventures.

Authors: Ozcan, P; Fraser, J

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: Institute for Operations Research and Management Sciences
• Journal: Organization Science More from this journal
• Acceptance date: 2026-10-22
• EISSN: 1526-5455
• ISSN: 1047-7039
• Language: English