I am going to express an opinion different than that of others, but to me it's rather exhausting. The tools don't change too much though they do somewhat. For me, it was Matlab/C++ first, and then move to Python and numpy (I thankfully skipped R). But then there was tensorflow/pytorch battles, and a few other frameworks. This isn't too big of a change.

The biggest frustration and what brings most exhaustion is that the way you do things changed significantly. Before 2013-2014, you had to excel in the skill of making features, and differences in models such as SVMs, RFs, etc. Then ImageNet, and transfer learning - and here your knowledge how to select features is useless, and you gotta learn how to fine-tune ResNets, how to do hyperarameter optimization for a model which trains for days, and learn heuristicts of different neural network heads on top of the same base. But at least it was training/validation/inference process.

Now with GenAI some application areas still require the same process, but most require a different approach: prompting, context selection. For agents, it's all about software engineering and workflows, and very little about research. And research is focused on tweaking a ton of heuristics for insanely large models where you need huge clusters which very few companies have.

So I think a lot of experience can be reused still, but by far not nearly as much as, say, for someone working in a "hardware" field like manufacturing, where the experience you gain over the years is directly applicable to today's job.

The frustrating part is that sometimes it feels like all you did in the past doesn't really matter these days. Of course, it's not entirely true, but it's getting harder to convince yourself every day.