I. Investing in AI

Investing in AI is not an easy job: AI technologies can be 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. This represents a big barrier for investors to efficiently allocate their capital.

Generalist investors find alternative ways to discern investable companies from the media hyped ones. Instead of looking at the code or the algorithms, they identify proxies for AI technologies, such as:

i) Impossible problems: if a problem was not addressable before, but machine learning and recent AI developments suddenly enable a new solution.

ii) Data effect: 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: a major 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.

 

II. So, who are the smartest guys with money?

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.

 

III. List of AI Investors

Here is a list of over 80 of investors who either explicitly focus on AI or have made several notable AI investments:

 

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What VCs Look For In: 
Artificial Intelligence Investments

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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. However, I believe this is a temporary list because in five years everyone will be investing in AI.

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.

This article by guest contributor Francesco Corea was originally published on Medium. Contributor opinions are their own and do not reflect those of TOPBOTS.