I do agree with this. I was able to get exactly what I needed even out of GPT3.5. If you put enough parameters and examples, along with a real solid system prompt and (if you can) proper temperature and topK/topP, there's no reason they can't basically function like "smart typing assistants". The issue is that its a sliding scale of ambiguity to chaos. The more ambiguity, the more the LLM fills that in. And it can be very difficult and time consuming to know when you're being ambiguous or not (you don't know what you don't know, OR, you can't track what you aren't tracking).

Depending on the task, it can sometimes be just as arduous to produce enough guidance and guardrails to get the LLM to output exactly what you need that you can trust without issue or extensive review than it is to write it yourself and use the LLM just for ad-hoc generation. It's a constant balance and an endless amount of micro-decisions, honestly, but it's pretty essential to stay engaged and not YOLO with agents the way so many are. Most of my interactions with models these days are done in pseudo-code.

True innovation is doing something new that has never been seen before. Even if you are doing it in a stable language that's just a stable poetry form (rhyme and meter and formatting), the real magic is the content of the poetry, and the real beauty of code is when the poetry reads well to both audiences, the compiler and the next maintainer, on multiple levels, the literal and the metaphorical.

LLMs can't reach the metaphorical. LLMs don't know what true beauty is. I will grant you they have gotten great at the literal and the poetry forms. But it is the beauty that elevates things to my quality bar, and makes a difference between "legacy code" and "innovation" to me.

Yes, and? Something can be both scare and inadequate to a given task. FAANG L5s cost a pretty penny but I wouldn't trust a random one to prove a crypto library correct.