This VC shares 3 important factors to consider when investing in AI startups

Photo credit: msaadrasheed / 123RF Stock Photo.

Tak is a star contributor for Tech in Asia and publishes exclusive, high-value content that serves the Asian tech community. Read more from star contributors here.

This post draws insights from the AI & Society panel comprised of Yoav Tzruya, Phil Chen, Manish Kothari, and the author and moderated by Naoki Kamimaeda from Global Brain.

The author is an investor in Sero.ai.

For the non-technical investor, the term artificial intelligence (AI) can be either a celebration or kryptonite. A celebration because adding a few AI startups to their portfolio is a great way to justify to their LPs that their deal flow is current. But it can also be kryptonite in the sense that every startup seems to be using the term du jour for a significant valuation increase.

But the truth, like in most things, is somewhat muddy:

• It certainly is the dawn of AI startups, and increasingly more and more startups will add AI to its repertoire.
• There is a shortage of truly deep technical talent, but that won’t stop the evolution of startups since there are ample open-source libraries in the world.
• Data is certainly key for AI startups, but that does not mean that the Googles, Facebooks, and Baidus (GFBs) are the only sources of these data. This means they won’t be the only players in the AI space.

If you’re interested in investing in the AI space (as a VC or an angel investor), there are really three key principles in investing in startups that employ artificial intelligence or machine learning: whether the startup is a VAS or a HAS, how the startup creates value, and their data-product fit.

VAS vs HAS

First, some definitions: vertical AI startups (VAS) are those that apply AI in one focused vertical industry. Horizontal AI startups (HAS), on the other hand, apply AI across a multitude of different industries. You can read about it in more detail in my previous article.

The general pattern is that HAS are comprised of stronger technical teams (PhD’s will do the trick), will need a longer runway to find product/market fit (let alone revenue), and will likely be acquisition targets for the GFBs. On the other hand, VAS can find revenue models faster, need less technical teams, and satisfy a real industry need.

DeepMind is an example of a HAS and satisfies all three criteria. However, the days of HAS are limited, and some would argue that there really just isn’t that many HAS startups at all.

Categorizing the startups you’re eyeing is the first step to understanding the nature of the beast.

Value creation

Value creation in an AI startup can come from two sources: the product itself (short term) or the startup’s data aggregation (long term). This means that startups can either create value through the use of the product itself or through the amount and uses of the data they collect.

AI startups that care about short-term value creation are great, but those that care about long-term value are even better. This is simply because data aggregation, especially of unique and exclusive data, is inherently more valuable.

Data-product fit

Conclusion