Yes you understand the thought! The thought isn't use ChatGPT to code existing programming languages but to take advantage of LLMs ability to traverse complicated relationships that result in logically composed behaviours.
A load balancer, access control list, pairwise zip, fork/join, map, reduce, interleaving, LIFO, FIFO, dispatcher, scheduling, binpacking, control loops, transformation, coordination, orchestration are all behaviours that could be useful in designing an architecture. An LLM probably knows the arguments they take and how to apply them to another scenario at an abstract level. They can match up the variables in each behaviour with what they map to.
For example, I don't need to write code that glues things together if the LLM knows where a load balancer should fit on a dispatcher
Today, that process is easier than ever but still very manual.
It sounds like what you're suggesting is that we try to develop tools that look to an LLM to figure out which "functions" to call, and to write them if they don't already exist. In this way, a corpus of tokenized behaviors could be composited by an LLM to produce an on-the-fly program based in intent rather than syntax.
Is that a fair summary?