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.

Indeed. ML won't be the be-all end-all of software engineering, but it'll be a core skill to have significant knowledge about. Even if you're not developing new models, knowing the best practices for using them or extending them is going to be invaluable.

I'm in a similar position. I did a bunch of courses on coursera, udemy, pluralsight, fast.ai -- because I like ML stuff, however, I'm pretty sure this will be just a hobby.

I've come to the conclusion that AI/ML is a winner takes all market, I really don't think we'll see millions of ML/AI "developers" like we've seen C++/php/.NET/Java in the past.

For the next 10 years I'll focus on kuberbetes/some big data/some ML, but even though I regret it, I think the only viable option is to get into management.

Nah. It's a specialized skill that most programmers won't need to know. Is it useful? Yes, but only if you're a specialist, just like any other niche in computer science. Nobody needs an amateur machine learning engineer, just like nobody needs a hobby compilers enthusiast on their team, or weekend warrior cryptanalysis abilities.

Be an expert or go home.

Learn as much about ML as you're interested in but don't worry about becoming a dinosaur at all.