In one section, Church describes the evolution of genetic research over the last several decades - how scientists went from doing everything manually to using machines and how much more productive they became. He ends with:
So in summary, the descent of man (the devolution of research persons) went like this: (1) DIY. (2) Buy parts. (3) Buy kits. (4) Buy machines. (5) Buy services. (6) Buy interpretation.This immediately struck me, not only because of its resemblance to the evolution of the computer and many other technologies, but also to a very important aspect of startups: how to make money by offering customers value.
Generally speaking, your profits will be proportional to the value you offer your customers and you'll pull ahead of your competitors by offering more value. At the same time the commoditizing nature of technology means that what's valuable and profitable today will become ordinary and cheap tomorrow. The challenge for startups then is to move up this value chain, disrupt competitors stuck on the lower rungs of the ladder and reap the profits.
Here at Kytephone, we make an app to turn an ordinary Android into a kids phone with parental controls. This lets us give parents peace of mind by offering them a service that lets them locate their child, see who they've been talking to, which apps they've been using etc. While parents certainly appreciate our service, what they really want is someone to tell them specific, important information - did my child get to school safely? Are they being harassed by someone? Are they spending too much time on Facebook? In other words, parents want an interpretation of their child's data to assuage their fears and worries.
Our challenge then is to give parents timely, important information about their children without them having to do anything. And bonus points for not asking for things back, like "Where does your child go to school?" While it is very hard for computers answer such questions, we are surely heading that way. We can see this not only in the Machine Learning boom, but also high-profile effort like Google Now or IBM Watson.
