It hasn't produced any fruit.
And especially from a retrospective perspective, the importance of that can't be understated.
I'm not enough of a mathematician to be able to do this myself, but intuitively I expect that there must be some way to measure the chaoticness of a program-type mathematical system, the degree to which small perturbations in the input produce large changes in the output. We are familiar with this in practice in the programming world in the difference between languages like J or a fluffier language like Java. Within the domain of legal programs, perturbations in the original symbol stream are more likely to have larger effects on programs in J than in Java. My personal feeling, after watching people sort of screw around with CAs for the last many years (only recently unsubscribed from /r/cellular_automata) is that CA are simply too chaotic to be useful for any non-trivial purpose; the task of establishing correlation between a real physical outcome and a CA is too difficult, and then even if you do, the task of understanding the CA itself is still itself quite large! It just isn't a useful way of modeling the world. Or, if you prefer, the problem isn't that CAs are too simple to model things with, the problem is that in general they are too complex. Either a CA is degenerately simple or impossibly chaotic and there just isn't enough in between. The same characteristics that makes it fun to watch a CA explode from a very small seed into a complex diagram that is still somehow obviously structured in strangely complex ways makes it impossible to actually make them do anything you want to do.
Go look at the Turing machine implemented in Life. Look at the sheer size of the thing. It's a bit of a cheat because it is simulating something else that we already have models for, but... could you imagine trying to understand the behavior of Turing Machines through the lens of that Life model?
Another interesting thing to look at is the way people sometimes try to incorporate CAs into video games. It turns out they are very, very hard to tame into anything useful, without a lot of work spent constraining them down to something tractable. Or, in other words, by stripping out all their power just so they might do something slightly predictable.
Turing machines are a much better model of computation than CAs are of very many other real processes, and in practice, Turing machines are still virtually useless, useful only for the theoretical pleasingness of the UTM and the resulting mathematical theorems, but not something we use on a day-to-day basis. Lambda calculus is way more useful, and even more useful than that is just the adhoc models we tend to use day-by-day that may lack nice mathematical properties but actually resemble the machines we work with. CAs have an even larger gap between their sheer mathematical perversity on the one hand, and any useful application on the other.