"Best methods" is doing a lot of heavy lifting here. "Best" is a very multidimensional thing, with different priorities leading to different "bests." Someone will inevitably prioritize reliability/accuracy/fidelity/interpretability, and that's probably going to be a significant segment of the sciences. Maybe it's like how engineers just need an approximation that's predictive enough to build with, but scientists still want to understand the underlying phenomena. There will be an analogy to how some people just want an opaque model that works on a restricted domain for their purposes, but others will be interested in clearer models or unrestricted/less restricted domain models.
It could lead to a very interesting ecosystem of roles.
Even if you just limit the discussion to using the best model of X to design a better Y, limited to the model's domain of validity, that might translate the usage problem to finding argmax_X of valueFunction of modelPrediction of design of X. In some sense a good predictive model is enough to solve this with brute force, but this still leaves room for tons of fascinating foundational work. Maybe you start to find that the (wow so small) errors in modelPrediction are correlated with valueFunction, so the most accurate predictions don't make it the best for argmax (aka optimization might exploit model errors rather than optimizing the real thing). Or maybe brute force just isn't computationally feasible, so you need to understand something deeper about the problem to simplify the optimization to make it cheap.
It could lead to a very interesting ecosystem of roles.
Even if you just limit the discussion to using the best model of X to design a better Y, limited to the model's domain of validity, that might translate the usage problem to finding argmax_X of valueFunction of modelPrediction of design of X. In some sense a good predictive model is enough to solve this with brute force, but this still leaves room for tons of fascinating foundational work. Maybe you start to find that the (wow so small) errors in modelPrediction are correlated with valueFunction, so the most accurate predictions don't make it the best for argmax (aka optimization might exploit model errors rather than optimizing the real thing). Or maybe brute force just isn't computationally feasible, so you need to understand something deeper about the problem to simplify the optimization to make it cheap.