Right now, there is so much misunderstanding about what ML is, what resources it needs, and how it works that the corporate environment is very stressful.
ML jobs are well paid, but they are NOT fun. No one understands ML devops & the infra needs to enable tight experimentation loops. Existing observability and telemetry systems are wildly bad for model training, reproducibility or any form of online or semi-online learning. As an ML engineer you’ll have to take on huge workloads of devops, infra, tooling, data munging. I’ve seen more than a few brilliant ML engineers burnout and quit because of this.
As ML becomes better understood as a boring technology, and decisions around ML projects, team structure and especially ops support start to get more standardized, I think this will get better.
The pivot you mention means a thinning out of the headcount on the pure ML research side. But it also means opening up more positions in ML engineering, infra & devops.
If people choose their specialization appropriately and remain open to being less on the research side of this, then I think there will continue to be lots of opportunities for high-paying jobs, and people will know their required responsibilities more unambiguously and probably will be happier, rather than dredging through the endless series of bait and switch jobs that exist today, promising a focus on ML research but typically forcing you more into ML devops & data platform management.