Vertical vs horizontal: 2 ways to build an AI startup

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AI companies tend to be one of these two varieties:

• Very science-driven, who are typically academics working on one fundamental problem but serving many industries, say general NLP
• Solving problems for a very specific type of customer and belonging to one specific industry

For brevity, the first one can be labeled as horizontal AI startups or HAS, and the other vertical AI startups or VAS.

HAS typically evolved from funded research programs, academia, military, or corporations who perform fundamental science innovation. VAS can be started with less heavy research, thus fewer funds, and rather focused on intelligently applying technology to existing problems. Because of current open-source technologies, the opportunity to develop VAS has increased at a rate previously unseen.

Building a VAS

The first rule of starting a VAS is to focus not on building a startup but on solving a problem for a customer or user by applying AI. There may be a slight terminology difference here, but the ramifications are drastically different. Starting a VAS to build a startup is creating technology for its own sake, but building a VAS to solve a problem is getting rid of a customer pain using technology. We see many examples of founders being in love with the technology when the orientation should be on the customer.

Here’s another reason why starting a VAS is important: By constraining who the customer is, a startup constrains the data in which they need to train their algorithm. Identifying who the customer is, what data is important, and the quality of that data, a startup trains its algorithm most efficiently.

One more important reason why a VAS is an earlier approach than HAS is because of the competitive advantages it affords. HAS plays into the hands of large corporates like Google, Facebook, Amazon, Baidu, and the like, and they have infinitely more human and financial capital to succeed in the long run. VAS, on the other hand, affords a vertical approach that big corporates likely won’t join in because in their eyes the space is too niche. Assuming that the VAS startup moves quickly, they should build up a very good sustainable advantage and data moat that produces a business that is not only sustainable but also valuable.

Building a HAS

Can AI startups be purely technology-driven as opposed to customer-driven? The answer is yes, but it does require an incredibly strong technical team. Examples of this include Deepmind and Sentient.ai, where the founding and core team members were strong academics or deep AI practitioners with extensive experience. And even then, raising capital for those startups was not an easy path because of the lack of a well-defined path to revenue.

However, if a startup decides to go HAS, there are two conditions to differentiate: one is to focus on the democratization of the base technology and the other is to create a strong community around consumers of that technology. An example of this is Digital Ocean, which was competing with AWS and Softlayer at the time. But because of its strong community roots and easy user interface, it was able to gain traction and build a strong business.

The upside of HAS startups is not to be understated. Every groundbreaking technology comes to a tipping point when it flows out of the hands of its creators and gatekeepers for the community. By enabling community involvement and innovation, the base technology becomes more widely used and, in turn, more widely adopted. A good example of this is VisiCalc, a spreadsheet application for the Apple II which many argue was one of the prime reasons for the massive adoption of desktop computers. A similar logic can apply to HAS startups.

Thanks Nathan Benaich, Tom Tran, and Rahul Vishwakarma for the comments.