Data-driven VCs

Who is using AI to be a better (and smarter) investor

I. A data-driven what??

The ones of you who know me are very well aware that if there is something which has sort of obsessed me for the last few years, this is definitely how to use analytics and AI to improve the venture industry.

While I tended to focus on scouting and evaluation, I learned that AI can be also used to spot general trends, identify market gaps, improve VCs portfolio management, better match co-investors, and deals, gather intelligence on competitors’ landscape, identifying potential acquirers, and improve pricing models.

I have been thinking about those issues for a while now (and stay tuned because I will post over the next few months my latest research in this field), and did already write on the importance of using AI in VC, summarized some academic research on this topic, and generally wrote about AI investors and accelerators.

I am clearly not the first one who is arguing for a data-driven approach to investing, and I thought it would have made sense to write a short post on other same-minded investors that I got to know or heard of.

Knowing exactly what they do is quite cumbersome without having inside information, so I am simply reporting public knowledge as well as what I read and heard on venture funds using AI to some extent (in alphabetic order):

From the original list of about 10–15 funds made a year ago, we now have more than 25 funds that are using AI in different ways. Even though this increase may look like a drop in the ocean of the venture industry, it seems to me something extremely relevant that may change, in fact, the way we think about investing.

II. Building or buying it?

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Image Credit: ihazdotca/Shutterstock

Is this the whole story? Not quite.

Even though I started this post (and my search) focusing on venture funds that use AI in different ways, I eventually discovered that VCs are not the only players in this niche industry. In fact, there exist several startups and tools that I think are worth mentioning for the sake of completeness because they are trying to democratize VC investors’ skills:

This is likely only a partial list, but it conveys and bolsters the point mentioned above: having an AI-driven investment engine is becoming a trend, and we should expect more of those solutions in the future.

It is also interesting to notice that there are more funds pouring money into these engines development than companies selling those systems as a service. In other words, VCs seem to prefer building over buying when it comes to intelligent software for their own internal use. Intuitively, this is paramount to create a moat and a competitive advantage with respect to other investors, but it is also true that this could segment the market and polarize it: in fact, while bigger funds may have the resources to invest into building their own platforms, this may not be true for smaller funds, and this could also result in wrong signaling to LPs and potential deals (e.g., if you buy a software rather than building, you may be considered to be a second-class investor).

Furthermore, a last comment. We listed so far funds and mainly software companies that are offering different types of AI services. These are not the only two options though. There are intermediate alternatives such as the one provided by Clearbanc and 20-Min Term Sheet, where they use algorithms to review the startup’s marketing and revenue data and decide whether to grant a loan in about 20 minutes. Similar capital-as-a-service offers are provided by other companies such as BlueVine, Lighter Capital, Corl, always with an automated process that speeds up the investment decisions.

III. Conclusions

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Image Credit: archy13/Shutterstock

I am pretty optimistic about data-driven VCs being the future, although I had a finger in the pie for long enough to understand that it is not trivial as people believe (or tell). This is why I am spending a lot of time thinking and working on how to push it further, and I will release very soon a couple of new interesting posts on practical research I have conducted on the topic.

I do not believe the future of the industry is to be fully automated and VC is and always be a people business. On the other side though, it sounds astonishing that algorithmic thinking has not permeated so far the way investors work on a daily basis.

Finally, I spent time researching and talking to many of those people, but it is also very likely that I might have misunderstood something or missed someone out there who are working on similar approaches. If so, please feel free to reach out!

This article originally appeared on Forbes

Written by

Research Lead @Balderton. Formerly @Anthemis @UCLA. All opinions are my own.

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