Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman (2021) “A Scaling Perspective in AI Startups”. Working Paper. [ResearchGate]
- Presented at HICSS 2021 (SITES mini-track)
Digital startups’ use of AI technologies has significantly increased in recent years, bringing to the fore specific barriers to deployment, use, and extraction of business value from AI. Utilizing a quantitative framework regarding the themes of startup growth and scaling, we examine the scaling behavior of AI, platform, and service startups. We find evidence of a sublinear scaling ratio of revenue to age-discounted employment count. The results suggest that the revenue-employee growth pattern of AI startups is close to that of service startups, and less so to that of platform startups. Furthermore, we find a superlinear growth pattern of acquired funding in relation to the employment size that is largest for AI startups, possibly suggesting hype tendencies around AI startups. We discuss implications in the light of new economies of scale and the scope of AI startups related to decision-making and prediction.
Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel Fuerstenau. “On the Heterogeneity of Startup Tech Stacks”. Under Review [HICSS version]
- Presented in HICSS 2020 (Maui, HI)
- Previous title: On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems
Digital infrastructure is the backbone on which digital startups realize business opportunities, and the homogeneity or heterogeneity of the technological base can have significant downstream impacts on business risks, inflexibilities, and growth barriers. On the nexus of digital entrepreneurship and infrastructure studies, we suggest a conception of startup digital infrastructure as organized in tech stacks; tech stacks contain individual technological elements that are combined in a single startup, while the way this is done will be inspired by shared templates within the ecosystem. Given that there is limited understanding of the heterogeneity (or homogeneity) of startup tech stacks, we use public registry datasets from StackShare and Crunchbase to identify common tech stacks of startups. Through our analysis, we identify ten commonly used startup tech stacks, which we use to measure and analyze the heterogeneity of startup tech stacks and its antecedents. OLS regression analysis shows that a startup’s technologies’ interrelatedness, its age, and investor funding are associated with the heterogeneity of startup tech stacks. The overall analysis suggests that while startups may make individual choices regarding technology usage, there could be underlying commonalities and imprinting effects across startups, exposing them to common risks in terms of their digital infrastructures. This could pose important implications for startups, investors, and society at large