Growth Paths in Early Machine Learning Ventures: Technical Configuration and Competitive Advantage Over Time

Machine learning capabilities have become important 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 constrain later growth. We discuss the implications for strategy research on competitive advantage and path dependence in machine learning ventures.