When databases came by, I bet people felt the same way about them. Now they're a common tool used in most systems, and most devs and engineers will have at leas

When databases came by, I bet people felt the same way about them. Now they're a common tool used in most systems, and most devs and engineers will have at least a basic understanding. My hypothesis is that ML will follow a similar path. It seems like an exotic skill now, but there's already a mass of undergrads familiar with it from their education. We'll still need experienced practitioners to lead projects and architect systems (like DB admins and architects do!). But in some 10 to 20 years, everyone will use ML where appropriate, get some value from it, and it will have lost its hype. There will be some uniquely new capabilities that ML enables, just like DBs enabled storing state at scale, efficiently and cheaply.