So I got started working on ML in 2015 as well, and it has gotten far easier, but the thing that has gotten easier is the engineering side of things, not the ML

So I got started working on ML in 2015 as well, and it has gotten far easier, but the thing that has gotten easier is the engineering side of things, not the ML bits. And tbh, "take the latest model and tune it on your data" has gotten easier, but not trivial, since the code people release for the latest model is what I would call "research quality" code. Some labs will release production-ready code, but it's pretty rare. Particularly as more labs pick up PyTorch and you realise you can't afford to run that in prod. I think the author is right about basically everything else he says, but I also don't think it's an argument against picking up these skills, since they're going to be foundational to basically everything going forward IMO. So while there won't be a huge premium for ML engineers, it will be a necessary skill set if you want to work on challenging problems. And at some point the curve for adding data flattens out, and it becomes cheaper to hire engineers to think than subject matter experts to label data.