The problem with the tractor analogy is that there is not much that can go wrong in digging with a machine versus by hand. However, AI, like people, makes non-obvious mistakes that require a high level of skill to catch.

For instance, what we're seeing with pure vibe-coding is that even the best models make small mistakes, usually in the architecture but often in even lower-level abstractions, that compound over time resulting in unmanageable complexity. Senior engineers are able to spot and fix these issues early based on intuition, but that intuition has come from a lot of hands-on experience.

Using AI for a given skill is becoming a separate skill in itself, for sure. But this still requires having an understanding of the original skill itself.

I think this requirement won't change until we have super-intelligence, where AI is consistently much better than humans, i.e. beyond AGI. Because even at human-level performance, the only human that can verify an expert's work is another expert.

And the tractor operator with weak muscles is up the creek without a paddle in any situation that comes up that the tractor can't handle. They are also toast if the tractor's own advancements in automation renders less and less relevant.

Our only value in an LLM dominated world is the ability to handle the situations that the LLMs can't. Those who let their skills and critical thinking atrophy (or never built them in the first place), well, they're simply not going to make it. Shepherding LLMs is not a difficult skill to acquire and isn't going to be enough to let us maintain our value.

>You can build muscles by digging a field by hand but most people commercially use tractors.

Have you met a farmer? Most farmers are strong as all hell.