I didn't mean to imply that theorists don't produce valuable work, or that theory does not necessarily lead to practice. The distinction I intended is that some people prefer to work off of an underlying model, while others are content so long as they get the results they desire.

For example, contrast symbolic processing with artificial neural nets. Symbolic processing has a very solid philosophical basis and it can be used to solve many meaningful problems. Some problems, however, are so complex or nuanced that there are insufficient computing resources to implement a solution based on symbolic processing. Artificial neural networks can be used to address those complex problems, yet we lack the theory (at this time) to really understand the full limits or capabilities of complex artificial neural networks.

My reference to Wolfram was about his proposal to cast everything as automata. Even though that does not seem practical for all or even most problems, it provides a certain comfort, much like lambda calculus provides. Beyond the comfort of a solid philosophical grounding, automata also give us a way to approach many problems (such as simulation) in a principled manner.

Put another way, some people like to understand first, and use that understanding to discover results. Other people want the results first, and then seek to understand based on the results they found.