I did my PhD on trying to use ML for EDA (de novo design/topology generation, because deepmind was doing placement and I was not gonna compete with them as a single EE grad who self taught ML/optimization theory during the PhD).

In my opinion, part of the problem i that training data is scarce (real world designs are literally called "IP" in the industry after all...), but more than that, circuit design is basically program synthesis, which means it's _hard_. Even if you try to be clever, dealing with graphs and designing discrete objects involves many APX-hard/APX-complete problems, which is _FUN_ on the one had, but also means it's tricky to just scale through, if the object you are trying to do is a design that can cost millions if there's a bug...