I mean, for me, the unexpected part of 3 is what got me into programming in general. The first time you type a mysterious incantation into an editor and a few more mysterious incantations into the console and the console prints "Hello, world" like it was supposed to, it's unexpected because it's hard to believe that any of this mysterious incantation stuff actually works at all.
As you get better at programming you have to take on harder problems to create the surprise of something working, because you gain confidence, and as you gain confidence, you start expecting your code to work. It's only when you've compiled the thing 6 times with small corrections and gotten segfaults each time and the 7th time you finally find the place you weren't updating the pointer and you correct it, but this is the 7th error you've corrected without the segfault going away, so you don't really expect it to fix the problem, but then you run it and it's fixed!
And then you get a job and the reality is that most of the jobs you're just writing CRUD apps and for a little while you can get some surprise out of learning the frameworks, but eventually you actually get really, really knowledgeable about the Postrgres/Django/React stack and nothing surprises you any more, but because nothing surprises you any more, you're really effective and you start being able to bill the big bucks but only for work on that stack because it takes time to struggle enough to get surprised, and the time that takes means your time is worth less to your clients. Money ruins everything. And if you don't do anything non-billable, it's easy to forget what programming felt like when you didn't know how your tools all worked inside and out. Not everyone takes this path but it's certainly the easiest path to take.
I think for a lot of folks who have been doing this for a long time, the reason ML is so exciting is it's getting them back out of their comfort zone, and into a space where they can experience surprise again.
But that surprise has always been available if you continue to find areas of programming that push you out of your comfort zone. For me it's been writing compilers/interpreters for programming languages. Crafting Interpreters was awesome: for the first time I benchmarked a program written in my language against a Python program, and my program was faster: I never expected I'd be able to do that! More recently, I wrote a generational GC. It's... way too memory-intensive to be used in my language which uses one-GC-per-thread for potentially millions of threads, but it certainly was a surprise when that worked.
Personally, I'm keeping track of ML enough to know broad strokes of things but I'm not getting my hands dirty with code until there are some giants to stand on the shoulders of. Those may already exist but it's not clear who they are yet. And I've got very little interest in plugging together opaque API components; I know how to make an API call. I want to write the model code and train it myself.