Pre-entry experience and the heterogeneity in startup performance: Evidence from the nascent artificial intelligence industry

Abstract

We examine the performance differences among startups in nascent industries, taking account of the distinct knowledge contexts from which they arise. Specifically, we investigate the effect of pre-entry experience on the performance of startups originating within the same industry (i.e. inside–industry spinouts) and those from related knowledge contexts along the value chain (i.e. outside–industry spinouts). Analyzing a novel dataset that includes all U.S. artificial intelligence industry startup entrants during the period 1980 to 2014, we find that inside–industry spinouts and outside–industry spinouts have comparable survival and successful exit rates, outperforming startups with no pre-entry experience related to AI. Exploring the heterogeneity among outside–industry spinouts, we also find that the higher survival rate of this category of entrants is driven by startups founded by individuals who previously worked for firms operating in upstream supplier industries. We discuss the implications of our findings for research on strategy and industry evolution. • We examine the effect of pre-entry experience on the performance of startup entrants in nascent industries. • We focus on startups originating within the focal industry or from vertically related knowledge contexts. • We find that inside-industry spinouts and outside-industry spinouts have comparable survival and success rates and outperform other startups. • We find that the higher survival rate of outside-industry spinouts is primarily driven by supplier industry spinouts.

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