To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's

To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's value with machine learning?" For example, I don't theoretically need Netflix/Spotify/YouTube to have a great recommendation system. All I need them to do is stream media to my device. However, their recommendation systems add immense value to their product for me. A similar example would be navigation services (Uber/Google Map's ETA prediction, for example). I don't necessarily need those apps to give me predictions about traffic patterns—their core functionality is just giving me directions—but it makes the product much more valuable to me. For those businesses, this increase in value leads to an increase in usage and therefore revenue—it's not simply an abstract "nice to have." I think oftentimes the argument about the value of ML comes down to a binary decision of whether or not ML can 100% replace humans in a given domain, and that this is a flawed model. ML is capable of improving most products, in my opinion, and we're already seeing it happen.