To your first point - as you increase the number of parameters in a model you (quickly) begin to suffer from the curse of dimensionality. In a crude sense, the data requirement for a similar level of confidence is exponential in the number of parameters, so it can be difficult to use a high-dimensional model to understand a problem, even with a billion (or trillion...) observations. The best one can hope for is that, if there is a simple relationship hidden in the data, your high-dimensional model captures that relationship in a way that is amenable to extraction - I presume this is what you are saying in your third paragraph.
To your second point - I agree. I too do not reject that there is utility in constructing models that make no effort to match the form of the underlying reality. However, the fact remains that in such cases it is very difficult to use your model to gain deeper understanding, and as such these models simply aren't useful for a lot of science in their current form, precisely because they don't tell you anything about reality. Now if someone were to devise a way of extracting "intelligent", (meaning, sensible given existing understanding) simplified relationships from high-dimensional models, that might be a different matter...
To your second point - I agree. I too do not reject that there is utility in constructing models that make no effort to match the form of the underlying reality. However, the fact remains that in such cases it is very difficult to use your model to gain deeper understanding, and as such these models simply aren't useful for a lot of science in their current form, precisely because they don't tell you anything about reality. Now if someone were to devise a way of extracting "intelligent", (meaning, sensible given existing understanding) simplified relationships from high-dimensional models, that might be a different matter...