Content

The field of AI entrepreneurship is complex and rapidly evolving, presenting significant challenges for start-ups in anticipating and preparing for rare and high-impact events, known as Black Swan events. These events, as popularized by Nassim Taleb, refer to unforeseen events that have a major impact on the system in which they occur. The impact of Black Swan events on AI start-ups can be profound and threatens their performance and viability, making it a pressing issue for entrepreneurs and investors.

This thesis aims to address this challenge by exploring the impact of Black Swan events on AI start-ups, identifying the key factors that influence this impact, and evaluating the strategies that AI start-ups can adopt to mitigate the risk posed by these events.

By providing valuable insights into the impact of Black Swan events on AI start-ups, this study will contribute to the understanding of complex systems and their challenges, and offer guidance for entrepreneurs entering the field of AI entrepreneurship. The results of this research will provide a roadmap for AI start-ups to turn the risks posed by Black Swan events into opportunities and increase their resilience, making a valuable contribution to the ongoing development of the field of AI entrepreneurship.

Main Research Question:

How can AI start-ups effectively anticipate and prepare for Black Swan events in the AI industry?

Sub-Research questions:

What is the impact of Black Swan events on AI start-ups, and what are the key factors that influence this impact?

What strategies and approaches can AI start-ups adopt to anticipate and prepare for Black Swan events?

Research Goal

The goal of this thesis is to gain a deeper understanding of the challenges faced by AI start-ups in navigating the unpredictable and complex landscape of Black Swan events. By exploring the impact of Black Swan events on AI start-ups and the key factors that influence this impact, this thesis will provide valuable insights and strategies for AI start-ups seeking to mitigate the risks associated with these types of events.

Possible Methodologies

- Systematic Literature Review

- Qualitative research methods: in-depth interviews, focus groups, case studies to gain a comprehensive understanding of the impact of Black Swan events on AI start-ups and the strategies they adopt to mitigate the risk.

- Quantitative research methods: surveys and statistical analysis to validate and quantify the results obtained from the qualitative research.

- Mixed methods approach, combining both qualitative and quantitative research methods

Other research methods can be used or in mix to validate and strengthen the findings of the study.

If you are interested in writing a thesis on the above topic, please contact me with an exposé, curriculum vitae and a short mail with the name of your desired topic.