How Does AI Disrupt The VC Market?
The number of startups grows dynamically year by year, which means that the volume of data generated by startups increases. Most VCs still use Excel Spreadsheet — indeed, it was a useful tool, but a few decades ago for the basic analysis of small data sets. Currently, data generated by startups on the Internet is overwhelming to analyze by traditional VCs.
Some VCs see this significant change in the VCs’ market. Investors, such as Signal Fire, InReach Ventures or EQT Ventures, realize that they don’t only need business persons on the investment team on the investment team,but also technical people who focus on identifying unknown data sources, data mining and building machine learning models. They believe that data will make a difference in the next few years, helping them detect the ultimate opportunity. I like what Charles Darwin said:
“It is not the strongest of the species that survives, not the most intelligent that survives. It is the one that is most adaptable to change.”
What is the problem with traditional VC?
The problem with traditional VC is that they make a lot of decisions based on “gut feeling”, with a lack of data. Average VC analyzes 1000–1500 projects per year, which is only a drop in the ocean of all startup projects. This approach seems to be ineffective because only 2 to 3 deals in 10 are satisfying, which means that VC does not lose money. Still, there is no guarantee that such an investment will be profitable. VCs can work hard by:
- attending events,
- monitoring database,
- exploring the network,
- attending many meetings with founders,
- analyzing hundreds of applications and a pitch deck.
However, it does not mean that they work smart. VCs may miss many startups under their radar because most of the daily work they do manually is time-consuming. It happens because they do not use tools, which can help monitor and analyze all startups on the market. They have established their own set of rules to make investment decisions based on investor beliefs but not on facts that emerge from the data.
What is the future of the VCs market?
VCs, like many other industries, become highly dependent on data to make informed decisions. AI changes high-volume processes that were handled manually by enabling its automation. The top-tier VCs are already using software to identify opportunities on the market. Therefore, if you want to succeed in the VC market, start looking for various data sources. Crunchbase, PitchBook, CBInsight are correct to start, but remember that most of the investors use these databases and many startups never appear there. Data-driven VCs identify alternative database sources. For example, they monitor the talent movement on platforms like LinkedIn. Also, investors may look for signals such as the transition of the best developer to a startup or monitor newly created ventures. Why is LinkedIn the perfect data source? Because it is the first and obvious place to show off a new venture by entrepreneurs. Also, we have interesting data on LinkedIn, such as experience or education. We can even check whether the founders and the team worked or studied together.
Data-driven VCs do not go to the events with the “hope” of meeting a new investment opportunity. Because they use software to monitor all of the interesting startups on the market and outreach them actively, they do not need to have luck. Data-driven VCs know that they will see most of the prominent startups on the market first. Keep in mind that this is a recent use case, and VCs are still learning how to use AI to invest better. We can expect an improvement in the future, which means better spotting of good deals and avoiding the bad ones. Even though it is very unlikely that AI will not help us find the next unicorn, it will help investors not lose much money and focus on the best projects.
Moreover, Data-driven VCs use AI to add value to investment startups and help them faster and more effectively identify clients and new employees by using their networks using all portfolio startups and VCs team linkedin connections. For me, this is amazing. How much value you can generate by using a network of many people and using graph analysis.
But using AI in VCs is not easy, and it’s not cheap for sure. Only the largest funds can afford to build technical teams that focus on R&D. This is a large investment that is not within most funds because they cannot afford it. Therefore, VCs decide to use external tools. Soon there will be an article where I will try to answer the question “Build or buy AI tool for VC?” This is a strategic question for VCs who plan to use AI for their investments.
Traditional VC vs Data-driven VC
Clubhouse
On 29.01.2019 on ClubHouse there was a great room with Sarah Guemouri, Francesco Corea, PhD, Eva-Valérie Gfrerer, Cédric Waldburger , Andre Retterath and Henrik Landgren spoke about “Data-driven VC: How we use AI to find the best founders”. I hope shortly we can meet again and talk about the future of AI in the VCs world. Stay tuned!
Conclusion
The data-driven VC industry is a natural extension of the informational age. The most significant advantage of new generation VC firms lies in their ability to use AI and machine learning to conduct many investments in a fraction of time and with greater efficiency than ever before. It seems that the more old-school you are, the more quickly you will be disrupted, and it is already happening. However, even though the world is eaten by software, we need to keep in mind that AI is not a crystal bowl. It will not help you find the next unicorn or become a successful investor if you do not know how to invest. Moreover, the implementation of AI in your daily business life will be expensive and time-consuming. You can fail!
We need to remember that investor’s intuition and experience are still valuable in the investment process, and we cannot rely on AI only.
Even though AI cannot replace VCs’ decision-making process, it can make their lives and decisions more efficient. VCs may expect the best possible results when the experienced investor and AI work together. AI may help investors make the best possible investment decision by collecting data, analyzing, and giving a recommendation. It can act as a ‘fiduciary’ in assisting the VCs investment portfolio and advise on making the next investment. But in the end, the investor makes the final decision. The takeaway from this article is that if you want to become a great investor, start by making smart decisions based on facts and data, not just intuition or gut feelings. Use the AI to support you, not replace you.
In the following article, I will answer the question, Should Traditional VCs be scared of new-generation AI VCs? If you want read all series the best place to start is Knowledge Base Series About Data-Driven VC
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