Who is Best Positioned to Invest in Artificial Intelligence? A Descriptive Analysis

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry.

We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the Advisors and Scientist-in-Residence are becoming extremely important nowadays. Those roles would also help in setting the right expectations level, and figuring out what is possible and what is not.

AI investors are also slightly different from other investors: they should have a deep capital base (it is still not clear what approach will pay off), and a higher than usual 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.

When it comes to AI hardware or robotic applications then, few extra points are advised investors do not have to suffer from the sunk cost fallacy bias, and technical milestones should be clear a priori to track real progress.

All these characteristics are motivated by a series of AI-specific problems: first, as above-mentioned the technical complexity makes often AI startups black boxes. Secondly, it is quite hard to show proof of concepts. Some narrow AI prototype might be easier to be built, but in general, the difficulty of creating GAI-resembling software and the opaque benefit-costs analysis make hard to attract initial funding and in my opinion, this is where the governments should intervene in.

The concern then about what kind of milestone is deemed investable (revenues, open source communities, etc.) is tangible, and I would suggest considering investable only those companies showing some degree of technical innovation, either actual (MVPs) or potential (academic publications), or with data virtuous cycle (a mixture of unique datasets and users).

On the hardware side instead, other considerations have to be added: they are way more expensive than AI software developments, and victim of higher obsolescence and replacement costs. Hence, the tradeoff cost/reliability/speed/full control adds a further layer of complexity in the investing game. In particular, it is interesting to notice that if we would be able to work in the robotics space at much lower costs, this would shift completely our risk aversion perception, and it would encourage investors to risk more given the lower cost.

Having identified all these characteristics, we can try to draw a rough profile of companies that might represent (ex-ante) good investment opportunities: an early sign of good potential investment is definitely the technical expertise of the founders/CEOs. You should prove to have the right mix of technical understanding, technology exposure, access to a wider network, and vision leadership in order to convince brilliant researcher to work for your AI company.

The second point of interest is the diverse and multidisciplinary team: it does not sound impressing having all the co-founders or research team to come from the same school or previous research lab, but rather quite the opposite. Finally, startups that are people-centric are ex-ante more likely to succeed. The ability to create and supporting a developer community, as well as making products that are designed to be easily understandable have more probability to be adopted without frictions.

It is not a coincidence indeed that all the features so far highlighted were observable in early-stage success such as DeepMind. However, as we already emphasized earlier when discussing new business models, DeepMind has not only innovated from a strategic point of view, but it also stressed out the major points of interest for any AI startup.

First, always aim to a general-purpose intelligence: the value DeepMind is proving to own is the ability to apply their general research in the same way to medical problems or energetic issue.

Second, do not be afraid of public exposure to failure: challenging Lee Sedol on a live worldwide recording was risky, but the brand reward and resonance obtained from winning vastly overcame the effects from a (potential) public failure.

For the full article including descriptive analysis and list of investors/accelerators, check here.