It's a different skillset and way of thinking. Engineers tend to think vertically deep on technical problems. With AI, you have to think horizontally broad and vertically up on the architectural problem. The trick is to be comfortable relegating the details to AI.

One concrete example of this realization was when I was researching how to optimize my claude code environment with agents, skills, etc. I read a lot of technical documents on how these supplemental plugins work and how to create them. After an hour of reading through all this, I realized I could just ask Claude to optimize the environment for me given the project context. So I did, and it was able to point out plugins, skills, agents that I can install or create. I gave it permission to create them and it all worked out.

This was a case of where I should not think more technically deeper, but at a more "meta" level to define the project enough for Claude to figure out how to optimize the environment. Whether that gave real gains is another question of course. But I have anecdotally observed faster results and less token usage due context caching and slightly more tools-directed prompts.