A Scaling Perspective on AI Startups

Mattias Schulte-Althoff, Daniel Fürstenau, Gene Moo Lee

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 54th Hawaii International Conference on System Sciences
Number of pages10
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2021
Pages6515-6524
ISBN (Electronic)9780998133140
DOIs
Publication statusPublished - 2021
EventThe 54th Hawaii International Conference on System Sciences. HICSS 2021 - Online, United States
Duration: 5 Jan 20218 Jan 2021
Conference number: 54
https://www.insna.org/events/54th-hawaii-international-conference-on-system-sciences-hicss

Conference

ConferenceThe 54th Hawaii International Conference on System Sciences. HICSS 2021
Number54
LocationOnline
CountryUnited States
Period05/01/202108/01/2021
Internet address
SeriesProceedings of the Annual Hawaii International Conference on System Sciences
ISSN1060-3425

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