Why not both? This is what currently happens. And science on a distant location drops some knowledge, that can help us on earth.

The important part in our live is, to gather information and share it. So we can generate insights and come up with solutions. So it's important to research an many tasks, not only on a single branch.

As far I expierenced it, it gets really boring at some point, because some predictions feeling kinda useless. Or you need a f-load of parameters to produce an "okay" result. And at some point, you wish you could debug a trained model.

Most bosses or companies don't know, what ML is really doing. They believe we can put some random data inside the model and after that, the output gonna be a gamechanger.

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I'm doing a lot of automation and optimizing processes. My first boss wanted, to implement "some AI" in our process. I told him, it makes no sense to train a model, if our process is already stright forward and deterministic.

He wanted it anways, so I added a portion sarcasm to it: "Maybe in a private Blockchain with Kubernetes?"
He was all in.

So I guitted this job after a few months.

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But here's my opinion/suggestion:

If you love working as ML-engineer -> go for it. It's still hypetech and companies throwing money at it. (Till the next "AI" winter)

But learn some programming skills for being a developer at one day -> developers are higly needed! And this won't change over the next decades.

I'm waiting now, till sponsors of the research saying things like: "and now put that into a blockchain!"