No, the reason not to learn your tools is if they're not your tools anymore, and something else is. I don't spend time trying to manually invoke filesystem APIs to create files in my work projects and instead will often just have my LSP do it when I declare a module and the file isn't found. I could memorize the exact sequence of system calls needed, but my employer would prefer I spend time on things that are more worthwhile to them.

I don't disagree that having a deterministic CLI is useful, but I'd argue that's not the question here, because the CLI is what the statistical machine will be using. The question is whether the time spent doing it manually is worthwhile compared to the tokens spent on it. I'd further argue that if you instead learn how to pick the right model for the right job so that you don't use something way more expensive than needed for the task at hand, the token overhead will be extremely minimal for stuff like git operations; knowing that you can use something cheaper than like, Astra or Opus if all you want to do is a quick git cleanup and push is going to save your employer a lot more money in the long run than if you insist on doing every messy rebase by hand.

The point of knowing your tools is to stop escalating to other people every time something doesn't work with them.

Even if an LLM can hold your hand through everything, the disruption I've had because people don't understand what the LLM tool is doing, and ultimately does damage (we've had repos corrupted, systems taken offline, systems brought their knees, numerous data security incidents... it's been a fucking nightmare) is immense.

While using LLMs is a great idea, it has become crucial to put safeguards in place to ensure that their use doesn't deviate from team practices or compromise security.