A Scaling Perspective on AI Startups

dc.contributor.author Schulte-Althoff, Matthias
dc.contributor.author Fürstenau, Daniel
dc.contributor.author Lee, Gene Moo
dc.date.accessioned 2020-12-24T20:21:23Z
dc.date.available 2020-12-24T20:21:23Z
dc.date.issued 2021-01-05
dc.description.abstract 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 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 scope of AI startups related to decision-making and prediction.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.784
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71404
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Strategy, Information, Technology, Economics, and Society (SITES)
dc.subject artificial intelligence
dc.subject growth
dc.subject scale
dc.subject scope
dc.subject startups
dc.title A Scaling Perspective on AI Startups
prism.startingpage 6515
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