Who Wins the AI Value Chain?

There are two types of AI threats. The first is fun to think about: In this threat, a super-potent digital intelligence escapes the intended bounds of its mandate and then wipes out humanity in a robot-overlord, glowing-red-eyed-Arnold-Schwarzenegger doomsday scenario. On second thought, it is not fun to think about per se, but it’s at least a more interesting thought exercise than the second threat. 

The second threat is an AI that goes right up to the edge of Skynet, but falls short of being an artificial general intelligence. Instead, AI becomes a new type of ultra-powerful software that has the potential to upend existing market dynamics. Said simply, the second threat is one where an ultra-small set of people make lots of money. The interesting thing about this scenario is contemplating who wins and who loses. Will today’s tech goliaths be able to leverage their gigantic balance sheets to extend their reign? Or will the future be seized by a new generation of little guys with hyper-intelligent slingshots? The question of which companies win the AI battle is one that needs answering. 

Before making this piece, I found myself waiting for someone to publish a map of the AI value chain, but no one was doing it to my satisfaction. This article is my attempt to make that map. Rather than follow the traditional version of a market map where I list every company ad nauseam and attempt to impress you with sheer volume, I will instead discuss archetypes of organizations and then forecast scenarios where these archetypes will end up. Less comprehensive, more intellectually potent (hopefully). 

The Rules Of Winning

To start, no one actually agrees on what AI even is! Search on Investopedia or Google and you’ll get some definitions like “when a machine can mimic the intelligence of a human.” I would love this definition if I happened to love entirely useless sentences. These words are so shifty, so dependent on context, that even the word AI feels futile. So much truth gets lost in the process of translating the Google definition (replete with lofty hand-waving) to pragmatic business use cases. Part of the definitional difficulty comes from AI’s very nature.