Data-driven VCs

II. Building or buying it?

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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:

  • Aingel.ai: they have recently filed a patent for a machine learning system that scores startups and founders and also matches the companies to the most suited investor;
  • Capital Pilot: another service that facilitates the match between companies and investors;
  • Crunchdex: a new company that is focusing its attention on identifying the fastest growing startups;
  • Kognetics: they have a proprietary framework to identify interesting deals and offer extra insights on trends, markets, and competition;
  • Preseries: this is another fully automated solution to discover and evaluate startups, which it also has a voice interface (through Alexa);
  • Radicle: their proprietary software can be used to detect novel interesting sectors, and I believe they have something to say also on new ways to evaluate startups;
  • Rocket DAO: a decentralized crowdfunding and startup evaluation platform, they help companies and investors to better match (still in beta);
  • Valsys: they provide professionals with the tools they need to make data-driven decisions in valuation and estimation processes. They focus however more on a later stage.

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.

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