People want to be able to write either python or javascript (i.e the 2 most widely used languages) , and have a compiler with an language model (doesn't have to be large) on the back end that spits out the optimal assembly code, or IR code for LLVM.
Its already possible to do this with the LLMs direct from the source code, (although converting to C usually yields better results than direct to assembly) but these models are overkill and slow for real compilation work. The actual compiler just need to have a specifically trained model that reads in bytecode (or output of the lexer) and does the conversion, which should be much smaller in size due to having a way smaller token space.
Not only do you get super easy adoption with not having to learn a new language, you also get the advantage of all the libraries in pypi/npm that exist that can be easily converted to optimal native code.
If you manage to get this working, and make it modular, the widespread use of it will inevitably result in community copying this for other languages. Then you can just write in any language you want, and have it all be fast in the end.
And, with transfer learning, the compiler will only get better. For example, it will start to recognize things like parallel processing stuff that it can offload to the GPU or use AVX instructions. It can also automatically make things memory safe without the user having to manually specify it.