Summary: Getting an AI startup to scale for an IPO is currently elusive. Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy. While AI is most certainly destined to be the next great general purpose technology, on a par with the steam engine, the automobile, and electrification, there just aren't any examples of new AI-first companies that look like they'll grow that big. OK, in the 80s it took a long time for the'computer age' to show up in the financial statistics and maybe we're at the same place. Still, a bunch of people, especially VCs are wondering how to grow an AI company all the way to IPO.

In 2016 and 2017 I spoke with dozens of venture capitalists, many of whom have a specific and overt focus on artificial intelligence technologies. I wanted to know what made an AI company worth investing in, and what business models were generally the most appealing for investment. It took me almost a year of interviews to come to that conclusion. VCs all want to invest in business models with a defendable "moat". Companies that can acquire more data and more users in a positive feedback loop have the chance to blast beyond the competition and become nearly unassailable.

Summary: Now that we've detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it's time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup. In our last several articles we've taken a tour of the four major strategies for creating a successful AI-first company. So which one is best? Since we're going to offer a side-by-side comparison you may want to refer first to the foundation articles on the four strategies: There is wide agreement that controlling a unique data set is the most effective way to create a defensible moat.