In recent years, it's become increasingly clear that Artificial Intelligence (AI) startups can scale to become $1 billion-plus companies. When it comes to innovation at the early-stages, there is a pressing need to differentiate between hype and actual potential for scale and impact. Today, many startups claim to be innovating through the use of AI. Whilst some succeed, others fail to deliver upon their promise. How does one go about cutting through the noise and identifying the AI startups that have the most potential for scale?
AI is hot, I mean really hot. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. As a founder of an emerging AI company in the enterprise space, I've been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment? In this post, I'll share some of my thoughts and experiences around the whole concept of AI-First, the "last mile" problems of AI that many companies ignore, the overhype issue that's facing our industry today (especially as larger players enter the game), and my predictions for when we'll reach mass AI adoption. A few years ago, I wrote about the key tenants of building Predictive-First applications, something that's synonymous to the idea of AI-First, which Google is pushing.
It's hard to believe that a mere 50 years ago we still relied on humans to manage telephone switchboards. What once required an army of people to operate at scale was quickly replaced by computers powered by microprocessors. Nowadays, most of us can't imagine a world where humans are required to make a telephone call. Data science is in the midst of a similar revolution. While the mathematical tools to make predictions from data have existed for centuries, and the algorithmic ones for several decades, all have required humans to manage the data inputs and interpret/iterate on the outputs.
For the past decade, New York has worked to overtake the Bay Area as the US's #1 startup hub. New York startups have doubled their relative share of dollar volume invested into new tech companies nationwide since 2006 (source: Mattermark), and are now potentially staring down the opportunity to carve out a larger slice of that pie. Recent breakthroughs in Artificial Intelligence will enable tasks that were once thought impossible for a computer to accomplish. In the way that mobile made Uber (via GPS chips) and Snapchat (via front-facing cameras) possible, AI is believed to be the technology that will set off the next wave of massively successful start ups. The most interesting thing to see over the next few years is how New York's startup community will weather the hype surrounding artificial intelligence.
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.