GCs create a paradigm in which you still craft logic on your own. It is simply an abstraction, one you could even think of it as a pluggable library construct like Arc<T> in Rust. It doesn't write or transform code at the layer programmer writes code. I think GC is closer to the paradigm that stack local variables will go out of scope when you return from a function than a transformer that cleans up the misunderstandings about lifetime.
If someone said I crafted this unique approach with this special kind of neural network, and it works on your AST or llvm IR, and we don't prompt it, it just optimizes your code to follow these good practices we have engrained into this network, I'd be less concerned by it. But we are trying to take LLMs trained on anything from Shakespeare to YouTube comments and prompting it to fix memory leaks and deadlocks.
If someone said I crafted this unique approach with this special kind of neural network, and it works on your AST or llvm IR, and we don't prompt it, it just optimizes your code to follow these good practices we have engrained into this network, I'd be less concerned by it. But we are trying to take LLMs trained on anything from Shakespeare to YouTube comments and prompting it to fix memory leaks and deadlocks.