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The Determinants of Venture Capital investments in Artificial Intelligence start-ups: landscape, investors’ and investees’ profiles

Irene Besso

The Determinants of Venture Capital investments in Artificial Intelligence start-ups: landscape, investors’ and investees’ profiles.

Rel. Elisa Ughetto. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2022

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Given the important impact of the AI firms' development on the innovation and growth of economies, and the growing attention of VC funds in the AI sector, this thesis aims at investigating the landscape of VC investment in AI, analysing both the determinants of those investments and the profile of investors and investees. The dataset used for the analysis comes from the merge of two main data sources: CrunchBase, containing information regarding VC deals and Market Inspection, a panel containing macroeconomic variables used as control. In addition to investments and geographical analysis, the empirical method consists of a series of regression models and difference in means run on Stata software. Concerning investor profiles, VCs investing in AI start-ups tend to be smaller in size and less expert, confirming previous literature suggesting that inexperienced Venture Capital are more likely to heavily invest during boom periods without prior market cycle expertise. Moreover, the number of AI investors per deal is significantly higher if compared to non-AI counterparty, suggesting a syndication behaviour to share the intrinsic risk associated with the innovativeness of the industry and to reduce asymmetry of information / adverse selection bias. In addition, the analysis demonstrates that, despite receiving a higher round amount, due to the lack of VC expertise, AI-start-ups are less likely to result in a successful exit strategy, underlining VC expertise as a key success factor. Additional investigations demonstrate that AI industry has to be considered an investment driver especially during the early stages investment rounds, implying that investors are confident about the future growth and profits from AI expansion when other information are limited. Finally, the geographical distribution of AI start-ups tends to be concentrated in countries and cities perceived as innovation hubs, characterized by elevated technological levels, and developed economies. When analysing the impact of innovation hubs on investments, AI start-ups receive more funds than their non-AI counterparty, despite the geographical location. However, AI start-ups located in an innovation hub are less likely to receive heavy rounds, than those settled in other locations, suggesting that the over-proliferation of businesses operating in the same industry increases the competition among them, reducing the cluster beneficial effect.

Relators: Elisa Ughetto
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 91
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/24310
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