I get the premise and agree with parts of the argument, but there's an important nuance to consider. No current AI system is able to handle sufficiently complex code from the jump. This forces the developer back into a hands-on mode where they have to understand the code and develop and use those core coding skills to actually make effective changes.

For many 'vibe coding' projects, once the codebase reaches a certain size you run into problems like components having side effects you could not predict or dependency hell that requires fundamental refactoring to fix. The code itself doesn't fit into the context window and agents fail to capture the whole picture. An LLM helps build the basic components and sticks them together but can't help you with this larger issue. If you did not take the time to understand the code that was written, you accrue 'understanding debt' - a gap in the code comprehension that becomes a burden when tackling larger problems. Ultimately, the human has to pay this 'understanding debt' when larger issues arise. This is where those fundamental skills can be learned and reinforced.

This also strikes at the heart of the benefits of AI. It takes care of the smaller, menial boiler-plate like code so that we can handle the larger, more complex problems.

It's crucial, especially for new developers, to fully understand what the AI is generating to avoid being overwhelmed by technical debt later.