Prevailing trends in venture capital funding for startups in the artificial intelligence sector
| Název práce v češtině: | Převládající trendy ve financování rizikového kapitálu pro startupy v sektoru umělé inteligence |
|---|---|
| Název v anglickém jazyce: | Prevailing trends in venture capital funding for startups in the artificial intelligence sector |
| Klíčová slova: | Venture capital, artificial intelligence startups, funding dynamics, investment patterns, signaling effects, funding acceleration, startup funding success, AI sector, temporal evolution, funding predictors |
| Klíčová slova anglicky: | Venture capital, artificial intelligence startups, funding dynamics, investment patterns, signaling effects, funding acceleration, startup funding success, AI sector, temporal evolution, funding predictors |
| Akademický rok vypsání: | 2023/2024 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | doc. PhDr. Martin Gregor, Ph.D. |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 24.06.2024 |
| Datum zadání: | 24.06.2024 |
| Datum a čas obhajoby: | 10.06.2025 09:00 |
| Místo konání obhajoby: | Opletalova, O206, místnost. č. 206 |
| Datum odevzdání elektronické podoby: | 30.04.2025 |
| Datum proběhlé obhajoby: | 10.06.2025 |
| Oponenti: | Mgr. Pavel Neumann |
| Zásady pro vypracování |
| The thesis aims to examine venture capital within the artificial intelligence sector. It will analyze conditions under which venture capital investors are driven to invest in this sector and will investigate the key criteria influencing their investment decisions.
The thesis has the following objectives: 1) Identify the key criteria and priorities that venture capitalists evaluate when making investment decisions in AI banking startups, such as market opportunity, technical risk, regulatory dynamics, team composition, and revenue models. 2) Examine how different AI business models (e.g. B2B, B2C, SaaS) influence venture capitalists' assessment of investment viability, scalability, and return potential. 3) Provide insights from industry experts and practitioners on emerging investment themes, challenges, and future outlooks within this rapidly transforming sector. |
| Seznam odborné literatury |
| Gompers, Paul A. "Optimal Investment, Monitoring, and the Staging of Venture Capital." The Journal of Finance, vol. 50, no. 5, Dec. 1995, pp. 1461-1489. DOI: 10.2307/2329323.
Gompers, Paul, et al. "Venture Capital Investment Cycles: The Impact of Public Markets." Journal of Financial Economics, vol. 87, no. 1, 2008, pp. 1-23. DOI: 10.1016/j.jfineco.2006.12.002. Gottfried, Oliver, et al. "Investigation of Venture Capital Firms' Criteria for Investing into Technology Startups: A Comparative Analysis of Chinese, US, and German Investors." SSRN, 26 June 2023. DOI: 10.2139/ssrn.4223114. Heinze, Georg, and Michael Schemper. "A Solution to the Problem of Separation in Logistic Regression." Statistics in Medicine, vol. 21, no. 16, Aug. 2002, pp. 2409-2419. DOI: 10.1002/sim.1047. Jacobides, Michael G., et al. "The Evolutionary Dynamics of the Artificial Intelligence Ecosystem." Strategy Science, vol. 6, no. 1, May 2021, pp. 412-435. DOI: 10.1287/stsc.2021.0148. Perrone, C. "Drivers of Venture Capitalist's Investments in Artificial Intelligence." Master's thesis, Politecnico di Torino, 2020. Politecnico di Torino Digital Library, webthesis.biblio.polito.it/id/eprint/16792. Rogers, Everett M. Diffusion of Innovations. 5th ed., Free Press, 2003. Shao, Zhendong, et al. "Tracing the Evolution of AI in the Past Decade and Forecasting the Emerging Trends." Expert Systems with Applications, vol. 209, no. 10, July 2022, p. 118221. DOI: 10.1016/j.eswa.2022.118221. Spence, A. Michael. "Signaling in Retrospect and the Informational Structure of Markets." American Economic Review, vol. 92, no. 3, June 2002, pp. 434-459. DOI: 10.1257/00028280260136200. Svetek, Mojca. "Signaling in the Context of Early-Stage Equity Financing: Review and Directions." Venture Capital, vol. 24, no. 4, Apr. 2022, pp. 1-34. DOI: 10.1080/13691066.2022.2063092. Torssell, J. "The Most Influential Team Attributes When Predicting Start-Up Success: A Quantitative Study of 25,430 European New Ventures." Master's thesis, KTH Royal Institute of Technology, 2022. DiVA Portal, www.diva-portal.org/smash/get/diva2:1697289/FULLTEXT01.pdf. Warzyńska, D. A., et al. "Dutch Artificial Intelligence Startups: A Case Study Analysis of Twenty-Four Dutch Artificial Intelligence New Ventures Characteristics and Financing." Tilburg University, 2020. Weber, Michael, et al. "AI Startup Business Models." Business & Information Systems Engineering, vol. 64, 2022, pp. 91-109. DOI: 10.1007/s12599-021-00732-w. |
| Předběžná náplň práce |
| This research analyzed temporal patterns in venture capital funding for artificial intelligence startups across pre-2014 and post-2014 periods. Using data from 15,608 AI startups with 45,479 funding events, the study employed a multi-faceted methodological approach combining logistic regression, multiple regression, and survival analysis. The research identified three key transformations in the AI funding landscape: increased investor selectivity (evidenced by a 10% decline in follow-on funding rates), accelerated funding cycles (24% reduction in inter-round intervals), and standardization of the relationship between time-to-first-funding and follow-on success. The study demonstrated how early market validation became increasingly critical as the AI sector matured, with faster initial funding consistently predicting higher follow-on success. The analysis also revealed evolving funding strategies, with larger initial rounds increasingly functioning as substitutes for multiple smaller rounds. These findings provide strategic insights for entrepreneurs navigating AI funding environments, investors refining evaluation criteria, and policymakers considering support mechanisms for early-stage AI ventures with longer development horizons. |
| Předběžná náplň práce v anglickém jazyce |
| This research analyzed temporal patterns in venture capital funding for artificial intelligence startups across pre-2014 and post-2014 periods. Using data from 15,608 AI startups with 45,479 funding events, the study employed a multi-faceted methodological approach combining logistic regression, multiple regression, and survival analysis. The research identified three key transformations in the AI funding landscape: increased investor selectivity (evidenced by a 10% decline in follow-on funding rates), accelerated funding cycles (24% reduction in inter-round intervals), and standardization of the relationship between time-to-first-funding and follow-on success. The study demonstrated how early market validation became increasingly critical as the AI sector matured, with faster initial funding consistently predicting higher follow-on success. The analysis also revealed evolving funding strategies, with larger initial rounds increasingly functioning as substitutes for multiple smaller rounds. These findings provide strategic insights for entrepreneurs navigating AI funding environments, investors refining evaluation criteria, and policymakers considering support mechanisms for early-stage AI ventures with longer development horizons. |