I think it likely that instead of replacing existing methods, we will see a fusion. Or rather, many different kinds of fusions - depending on the exact needs of the problems at hand (or in science, the current boundary of knowledge). If nothing else then to provide appropriate/desirable level of explainability, correctness etc. Hypothetically the combination will also have better predictive performance and be more data efficient - but it remains to be seen how well this plays out in practice. The field of "physics informed machine learning" is all about this.