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Unsupervised Investments (I): A Guide to AI Investors

A list of 85 funds investing in Artificial Intelligence and Machine Learning

Image Credit: faithie/Shutterstock

I. Investing in AI

Investing in AI is not an easy job: AI technologies are black boxes and unless you are able to dig into lines of code they may be inscrutable. Simply looking at proof of concepts might not be enough to really understand the underlying stack behind specific applications, and this represents a big barrier for investors to efficiently allocate their capitals.

Generalist investors found then alternative ways to discern investable companies from the pile of tech-driven companies out there. Instead of looking at the code or the algorithms, they identified proxies for AI technologies, a sort of must-have list to help them cutting out media phenomena from interesting ventures:

i) Impossible problems: if a problem was not addressable before, it is really likely that a machine learning algorithm is behind the proposed solution of that problem;

ii) Data effect: it is common knowledge that neural nets require a lot of data to be trained, and if the startup has a way to create a virtuous data cycle (data network effect) or has access to proprietary data, this is sometimes enough to be deemed as investable;

iii) Team and Patents: the biggest barrier to entry AI/ML is talents and IP. Therefore, if a team is composed of scientists/researchers and has patents (obtained or pending), it would already be a good candidate for an investment even without any revenues. This is driven by top tech companies acquiring smaller startups simply for their ‘brain power’ rather than their actual numbers.

Image Credit: Aniwhite/Shutterstock.

II. So, who are the smart guys with the wallet?

AI specialists are luckily not that naive, but they are able to go much deeper and look behind the veil. As I already pointed out in previous articles, AI investors have different characteristics from more general investors:

i) Deep Capital Base: they usually should have a deep(er) capital base (it is not clear yet what AI approach will pay off);

ii) Higher Risk tolerance: investing in AI is a marathon, and it might take ten years or more to see a real return (if any). The investment so provided should allow companies to survive many potential “AI winters” (business cycles), and pursue a higher degree of R&D even to the detriment of shorter term profits. An additional key element of this equation is the regulatory environment, which is still missing and needs to be monitored to act promptly accordingly. Of course, in saying that, I only refer to the right hand of my AI Classification Matrix, because for narrow AI companies the risk tolerance may indeed be lower;

iii) First-Hand Coding/Engineering Experience: venture capitalists use the help of ‘venture partners’ or ‘scientists in residence’, but AI specialized investors are able to dig into codes and architecture by themselves.

Marc Andreessen, Bill Maris and John Doerr (left to right). Image Credit: http://www.bizjournals.com/sanfrancisco/news/2013/04/10/john-doerr-marc-andreessen-and-bill.html.

III. List of AI Investors

I then compiled a list as extensive as possible of every investor I read or bumped into over the past months. It looks like there are at least 85 of them:

Image Credit: Looker_Studio/Shutterstock

A final remark: there is a fund which does not make AI investments, but it is an AI investor. It is called The AI VC, and it claims to be entirely powered by an artificial intelligence engine. THE AI VC has ‘Unicorn Identification Capabilities’ (or at least it claims to have them) and it was created by anonymous founders. Well, even if the reality is that it is simply another RocketAI, I thought it was at least worthy to mention it because the concept is intriguing, regardless of the real value of the fund.

Furthermore, it is useful to notice that AI is sometimes seen as an asset class. Indeed, Smith & Williamson has just launched a fund to “offer investors pure exposure to a concentrated portfolio of companies that derive most, or all, of their revenues and growth from AI”. Well, let’s see what will happen!


IV. Other Works

This is my personal list. I have performed an extensive research work, but I still might be missing someone or misleading some deals or investment strategies (please let me know if this is the case!). However, I believe this is a temporary list because in five years everyone will be investing in AI. I will try to keep this list as updated as possible in the meantime, so check it out from time to time to see if anyone new is on the list.

Furthermore, many more interesting articles and researches exist about this topic. I would highly recommend you to have a look at the incredible works CB Insights and Anand Sanwal have done in the past 6–9 months about investing in AI (here and here some of the articles on VC, here for CVC, and here for AI investors with a healthcare focus). Tracxn is also a good source for major investors in AI startups.

On the other side, I would suggest to also check the European machine intelligence landscape by the Project Juno team (Libby Kinsey, Sebastian Spiegler, Laure Andrieux), in addition to the famous MI infographic made by Shivon Zillis, (see above Bloomberg Beta).

*Note: the initial list published in Feb. 2017 was made by 77 funds. Thanks to David Kelnar, Nathan Benaich, Alex Flamant, Andreas Thorstensson, Mike Collett, for the post-publication comments.