I'm not sure I agree with that. Many (or most) of the software engineers I know find the heavy reliance on AI coding agents/assistants pretty soul-sucking and uninteresting. I feel the same, and I'm looking for some kind of middle ground. For example, I will only use agents when doing so would not deprive me of learning and discovery.
I've been explaining it like this:
Programming was 1% judgment and 99% effort, where lots of folks could carve out productive careers carrying that effort and receiving that judgment.
Agentic coding has cut that 99% down by at least a couple of orders of magnitude for some work. Well-judged and well-described systems can manifest quickly where effort alone would fail. The 1% is still there, but, by ratio after optimization of the sweaty part, it's at least half of where the value is.
I had an example of this this morning, where Claude Code left to run overnight on an open problem had made an absolute hash of multi-source grounded clustering. I course-corrected it with a rule (I don't like magic number tuning on small datasets) and a specific approach (use clustering with separating anchors/seeds), and it had the system working in 15 minutes (confirmed after a couple of hours of processing). These are the same techniques that we would use with junior engineers.
Along the way, it drafted reports and ran experiments that taught me about some of the limits of SOTA listening/characterization systems that I otherwise would have had to spend time researching.
Just make teaching you an explicit goal of the system, and you'll be able to swivel from opacity to illumination.
Your comment about programming historically being 1% judgement and 99% effort is interesting. I'm not sure I agree with those exact percentages, but nonetheless, I think the more consequential part of your comment is that the 99% is being reduced by a couple of orders of magnitude. I think that's what ought to trouble us as software engineers. Expending effort is often how we learn and grow. This is true in the context of physical activity (e.g., going to the gym to strengthen muscles) as well as in the context of intellectual activity (e.g., struggling through a problem set). If I go to the gym with my forklift, I can lift things, but I'm not likely to get stronger. Similarly, if I have Claude write all my code, I'm probably not learning much.
Experienced folks who know how to describe and articulate through others have a huge opportunity here. I have ultra-quick interns in my laptop, waiting to apply aggressive and slightly presumptuous energy to any and every problem. I also know how to pull them back in and get them to focus that energy (because junior devs were the same).
New folks will sink or swim quickly, but they're less expensive and more plastic on average. They're raised in this. We'll see what that does to quality.
Deeply technical managers, designers, scientists, program managers, and product managers are now in possession of an incredible power, to be able to craft existence proofs to counteract the couched recalcitrance that engineering orgs have held over their judgment for decades. There's a certain intellectual integrity in this, even if nobody can actually read the code at the rate it's being produced.