Knowledge Base Series About Data-Driven VC
This article opens a series of blog posts about Data-driven VCs. My goal is to share my perspective of a significant change in the VCs market with the audience. In this series, I will try to answer how the VCs market is changing and how VCs start adapting to this change. In the next few weeks I will publish an article about:
Part 0. Knowledge Base Series About Data-Driven VCs — this is an open article describing myself and why I am writing this series
Part 1. How Does AI Disrupt the VC market? — this is the first formal article about how AI is going to disrupt the VC market. The article answers the question:
- What is the problem with traditional VC?
- What is the VC market’s future?
Part 2. Should Traditional VCs Be Scared of the New-Generation AI VCs? The second article of the series will answer:
- Who is a better investor, AI or human?
- What are the most important/interesting AI use cases among VC firms?
Part 3. What Database Should VCs Use?- In this article, I will try to answer a bothering question of most of the investors:
- What database is the best for generating quality deal-flow?
- What are alternative sources of identifying startups?
Part 4. How Does AI Support VCs and Help Adding Value For Portfolio Startups?- This topic can be attractive to VCs and startups. AI can help:
- Generate better investment opportunities and make better investment selection.
- Startups can be supported by AI to recruit the best candidates and sale their product to clients.
Part 5. Build or Buy an AI Tool for VCs? — This article will answer the most critical question for all Managing Partners :
- Should I build or buy an AI tool for VC?
- What are the interesting tools on the market?
Part 6. What Are Some of The Hurdles In Using AI In The Investment World? — This article will mention some current AI initiatives for VCs and answer the question:
- What are the biggest challenges in using data-driven VC in the VC world?
Part 7. How To Integrate AI With Traditional VC Processes? — The last article of the series will try to answer the following questions:
- How to implement the AI into traditional VC processes?
- What non-technical obstacles may prevent us from being properly implemented?
Who am I?
My name is Krzysiek Pilat but you can call me Chris, and I am a data scientist with experience in algorithm trading, VC and startups. I am always passionate about investing, startups and technology. A few years ago, when I worked in VCs I was responsible for the automation investment process and build a startup scoring system. I wrote my MSc thesis about “How to optimise the decision-making process in VCs investment selection?”. My experience in various industries such as Algorithm Trading, VC and Data Science gives me a unique perspective for fundamental changes on the market.
Why do I think AI will change the VCs market?
AI disrupts many various industries, from the obvious example of driverless cars to something more subtle like music production or food supply. I believe it will be the same for the venture capital market in the long term. I will try to do my best to explain what such change means for VCs and their portfolio companies. It is essential to mention that there are few approaches to AI, from the database-driven system (using data mining algorithms) to a more guru-led system (using deep learning-based techniques).
What is the definition of AI?
“AI is a computer system” capable of carrying out tasks that generally require human intelligence such as visual perception, speech recognition, decision-making, and translation between languages.
This series is non-tech. If you are interested in a technical discussion about data-driven VCs, let me know on Twitter, LinkedIn.
Best
Krzysiek
