The AI’s impact on the VC investment market

Will AI disrupt VC’s market?

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In front of us, economics is changing thanks to the global use of AI. Regardless we like it or now, the most repetitive processes are or will be automated soon. Last year, while working in Venture Capital, I observed many weaknesses in the Venture Capital market. This prompted me to see a significant opportunity for AI to make this business be more efficient.

Statistics is brutal — . Why is that?

When I was working in Data Ventures, I identified two biggest challenges for this industry: access to the pipeline projects and the right choice of projects worth investing in. First of all, we didn’t know where to find the startups, and even if we found and analyzed hundreds of potential ones, we invested only in few of them. Therefore, since only 1 or 2 in 10 projects bring a positive return on investment, I believe that these challenges are crucial to focus on. This article opens a discussion on how using AI can make VC as a data-driven organization and how using both investors’ experience and data improves the decision-making process.

Where the good projects are?

Unless you have a very famous brand like YCombinater, you have to actively be looking for projects for your data pipeline. Thus, how does VC’s look for a project for the pipeline? Most of them:

• attend brand events
• use the network
• work on PR to be very active on the internet
• screen social media profiles, such as LinkedIn, Twitter, Facebook, etc.
• search databases, such as CrunchBase, AngelList, F6S, etc.

Working in VC is like a research job: every day looking for the new opportunity to invest in. Searching may seem easy, however, in reality, is difficult. For example, if everyone already has heard about the great project, the valuation of that company can be extremely high, because many funds want to invest in it. This might not be the place for you. The art is to gain knowledge about the project before anyone on the market hears about it. The most incredible projects don’t go to brand events or ask investors for funding. Instead, they focus on the client and solution they can offer. However, being the first “who knows” is challenging and often impossible.

The second question that VC funds are trying to answer is

How to choose the best projects for the investment portfolio?

Most of VC meet more than 1,000 projects during a year, whereas only 1% of them are finally invested in. On my experience, 60% of the projects are rejected in the first touch with a fund. The touch means short call or analysis of pitch deck or the application. This process is highly time-consuming and ineffective. Furthermore, the rejected projects do not generate further turn over for the fund, what means that most of Venture Capital investments fail.

The common rule of thumb is that of 10 start-ups, only three or four fail completely. Another three or four return the original investment, and one or two produce substantial returns. The National Venture Capital Association estimates that 25% to 30% of venture-backed businesses fail.

Source: The Venture Capital Secret — 75% of Startups Fail

What means, that only 1 or 2 of 10 investments are “the proper decision”. It is highly ineffective!

Which VC’s are using Data Science approach?

More and more VC funds are using a data-driven approach. As I focus on the data science market, I found some of them. These are:

• AI Fund: this found is launched by Andrew Ng, the co-founder of Coursera and creator of one of the famous courses about deep learning. Fund has raised $175 million and will be sequentially initiating new businesses that use AI to improve human life;
• Correlation Ventures: is a new breed of venture capital firm, leveraging world-class analytics to decide on whether to invest or not in 2 weeks, plus other 2 for extra due diligence. They build one of the biggest databases which collect investment data from over 20 years from the American investment market;
• EQT Ventures: uses AI system called Mother Brain, which collects data from more than 20 online sources, like the startup database, online traffic measurement site Comscore and social media like LinkedIn, Facebook or Twitter. The critical point is to find the investment before anyone else notices it;
• Hone Capital: the Palo Alto-based US arm of CSC Group, fund developed model is based on data from such databases as AngelList, which is designed to evaluate the potential of Startup quickly;
• WR Hambrecht Ventures: Thomas Thurston is the key man behind WRH (and Growth Science, its sister tech company) who is advocating for the use of data science to guide growth investments;
• SignalFire: the firm run by Chris Farmer does not only use analytics to pick the right companies but also to help them grow by providing market intelligence and talent matching services;
• Venture/science: a quant-driven VC led by Matt Oguz, it uses AI and decision theory to compute the risk associated with different attributes such as team completeness, vision, etc.;
• 645 Ventures: Series A investor, it follows a metrics-driven approach to Growth Seed investing in a bunch of different sectors;
• InReach Ventures: led by Roberto Bonanzinga, InReach had quickly built a name as the software powered house able to scout early-stage European startups even before others VCs realized they need funding (see what happened with Oberlo, for example). Apparently, it took them two years and £5 m in investment to build their proprietary software;

If you are interested more in researching funds using AI, I recommend you this article: Artificial Intelligence and Venture Capital

In conclusion

AI is not a crystal ball, it rather helps investors make better decisions due to using the existing data. In other words, using AI can increase the probability of success. Can you imagine how much VC’s performance would grow if the probability of success was higher in only one more company of abovementioned 10? I believe, that using both data and investors experience is crucial in order to make a better decision, what generates a competitive advantage.

If you are interested, you can watch a great speech by Thomas Thurston, a founder of Growth Science (link), who explains how VC’s are using AI.

This is my first article, and I will appreciate any feedback. If you want to contact me, you can find me on LinkedIn