A Scaling Perspective on AI Startups

Mattias Schulte-Althoff, Daniel Fürstenau, Gene Moo Lee

Research output: Contribution to conferencePaperResearchpeer-review


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 revenueemployee 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 scope of AI startups related to decisionmaking and prediction.
Original languageEnglish
Publication date2021
Number of pages10
Publication statusPublished - 2021
EventThe 54th Hawaii International Conference on System Sciences. HICSS 2021 - Grand Wailea, Maui, United States
Duration: 5 Jan 20218 Jan 2021
Conference number: 54


ConferenceThe 54th Hawaii International Conference on System Sciences. HICSS 2021
LocationGrand Wailea
CountryUnited States
Internet address

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