And yet, most of what people end up doing ends up being effectively OS and application scripting. Most ML projects are really just setting up a pipeline and tel

And yet, most of what people end up doing ends up being effectively OS and application scripting. Most ML projects are really just setting up a pipeline and telling the computer to go and run it. Cloud deployments are "take this yaml and transform it some other yaml". In as much as I don't want to use Fortran to parse a yaml file, I don't really want to write an OS (or a database) in Python. Even something like django is mostly deferring off tasks to faster systems, and is really about being a DSL-as-programming-language while still being able to call out to other things (e.g. ML code).