Except that the theoretical model is not affected by any of the things you mentioned. The existence of caches, branch prediction, instruction reordering, parallel execution, etc. does not chance as problem sizes change. If your inputs become large enough, you will reach the upper limit of your CPU's ability to speed up execution with those features. In the limit, the asymptotics still matter, and experience has shown that the limit is not at all far-fetched in most cases.
What makes matrix multiplication an exceptional case is that the constant factor on the best known algorithm is so large that we do not know how to build a computer with enough memory to store a problem large enough to overcome that constant. That is not the case with this analysis of linked lists; all one can say is that the data sets chosen in that particular article (possibly representative of the most common data sets) are not big enough. One can certainly store a large enough data set to overcome the advantages arrays have, and so the only real question is, "Is it possible that the inputs will be so large?"
Maybe the answer is truly, "No, that is unlikely." I am skeptical, though, as there are not many cases where such statements can be made. Even software written for embedded systems that target specific products is likely to be repurposed for new systems with different inputs. Even researchers, who write software for the particular datasets sitting on their hard drives, often re-use their code in future work. There are "border" cases, like integer multiplication, but typically libraries will just select the best algorithm for a given input size (e.g. for multiplication, you'll probably only see FFT methods applied above a particular threshold). Perhaps linked lists are now a "border" case, but all that would mean is that we need to use abstract "sequence" operations that dynamically choose particular implementations to use as their sizes change.