Many angel investors and VCs believe AI/ML is a paradigm shift. Some of us believe the magnitude of its impact will be as massive as the internet. Ultimately every business will adopt AI/ML.
Most of the startup pitches I have heard over the last 12 months mention AI/ML. Of course, a lot of it is just fluff - entrepreneurs using hot buzzwords. How do I separate the wheat from the chaff?
Looking back, a rough framework begins to emerge:
Innovation and Efficiency
True innovation means using AI to make possible something that was impossible. ThirdWatch.ai uses AI to prevent eCommerce fraud by evaluating over 200 parameters in realtime. Doing this manually is just not possible.
Efficiency means a significant improvement in costs or productivity. Newsbytes uses machine learning to produce news stories algorithmically. Just 6 editors are able to publish 80 stories daily. That is the number of stories published in a typical newspaper with 100+ staff!
AI/ML intensity
By intensity I mean how critical is AI to the value proposition. ThirdWatch is an AI focused application where AI is at the core of the offering. The team is obsessed about the efficacy of their algorithms because customers depend on their risk score to accept/ reject transactions. TrustCheckr enhances user data by stitching together aspects of the user’s social profiles and using AI to build a trust score (i.e. is the user a fake or a real person). Websites and Apps using TrustCheckr value both the enhanced data as well as the trust score. Newsbytes core value is the quality and format of its stories. Both are more human driven than algorithmic.
In all the 3 of my investments I mention above, AI/ML significantly changes the business case. But the differences in the use of AI has a large impact on what I looked for in the founders, which business metrics I track and the VC funds who will be potential Series A investors.