I don't know where you are in your career, me I am on the backend. But all the time I was working the constant churn of new tools/languages/frameworks and so on, the race to keep up with the vendors just wore me out. And despite all that, building software honestly never changed much.

I have been working with both Codex and Claude, and you are right, you can't trust them. My best practice I have found is constantly play one off against the other. Doing that I seem to get decent, albeit often frustrating results.

Yes, the actual building of the code is either over, or soon to be over. The part that I always considered the "art." I often found code to be beautiful, and enjoyed reading, and writing, elegant code all the time I was working with it.

But the point of code is to produce a result. And it's the result that people pay for. As you mentioned with the evolution of development in your original post, the process and tools might have changed, but the craftsmanship in operation with those using them did not.

You make a fair point that this abstraction is different — prior layers were engineered and traceable, and an LLM output isn't. But I'd argue that makes the human in the loop more important, not less. When the abstraction was deterministic, you could eventually lean on it fully. When it isn't, you can never fully step away. That actually protects the craft.

Until AI becomes a "first mover" god forbid, where there is no human in the chain from inception to product, there will always be a person like you who knows where the traps are, knows what to look out for, and knows how to break a problem down to figure out how to solve it. After all, as I have always said, that is all programming really is, the rest is just syntax.

This is the argument for actually learning, so you don’t ship half-baked code, because the AI isn’t good enough. The people who are telling you it is likely have a financial interest in pushing the narrative that AI will do it all.

> LLMs, on the other hand, are non-deterministic. Therefore, we cannot treat their outputs as just another layer of abstraction.

This is another problem. A lot of code is written so exactly the same thing happens every time. If AI is going in and changing the logic in subtle ways that aren’t noticed right away when updates are made, because no one understands the code anymore-that’s a problem. In hobby or toy apps, no one cares, but in production code and critical systems, it matters.

That didn’t stop me from getting a phd.

If you think it’s all there’s to programming that llm spits out, then the problem is in you somewhere, not in llms.