I think this is because the kinds of problems that arise in system design are logical and symbolic in nature and the current crop of "AI" has no symbolic reasoning capabilities. All the current hype is about pattern matching. Very good pattern matching but just pattern matching nonetheless. Whereas when constructing a compiler or a JIT it's more like what mathematicians do by setting down some axioms and exploring the resulting theoretical landscape. None of the current hype is about theorem proving or the kinds of inductive constructions that crop up in the process of proving theorems or designing compilers and JITs.
It would actually be very straightforward to do so if the costs of testing solutions weren't so high. CPU architecture and JIT code can both be represented as unstructured (non-tabular) data. I even recall a circuit having been optimized by a genetic algorithm a while ago in an experiment. I also recall using LSTM to generate valid code from IIRC examples in Linux. Superiptimizatiom is also a relevant topic.
We just need better simulation tools or more resources.
But I don't put genetic algorithms in the same bucket. Genetic algorithms are a different breed of optimization algorithm compared to neural nets and gradient descent which is what the modern crop of AI is basically all about.
For an example of the kind of logical problem optimizers solve you can take a look at: https://github.com/google/souper.
So I don't see how you can take the current neural nets and get them to design a more efficient CPU architecture or a better JIT.