However, starting with LLMs directly generating machine code and moving to ML directed optimization passes, you're moving the whole stadium with a crawler-transporter, not only the goalposts.
Helping compiler in a well defined way, and working on IR or driving it repeatedly to optimize the output is one thing, generating machine code from a training set is other. Let's not mix them.
"Compiler 2.0: Building the Next Generation of Compilers with Machine Learning"
https://eecs.berkeley.edu/research/colloquium/compiler-2-0-b...
There are other researchers if you interest yourself on the matter.
Then the whole iPaaS agentic coding environments like Workato and Boomi, that even if they might generate 3GL as intermediate step, you don't get to see it.
https://github.com/zwang4/awesome-machine-learning-in-compil...
Using small, purpose built ML "things" to make things better makes sense. We use them everywhere. I never argued against it, as well.