Linguistics was a leader of the cognitive revolution, through extensive mathematization and theoretical development that directly confronted the behaviorist and empiricist traditions dominant in psychology (Tomalin, 2006). Interestingly, the linguistics community has fractured amidst this push, with many researchers feeling that modern theory developments do not serve the interests of the field (Dockum & Green, 2023). At the same time, the advancement in computing hardware and accessible implementations of probabilistic models have advanced the engineering of language or Natural Language Processing (NLP, Min et al., 2021). Where previous work in the field was able to satisfy both engineering and scientific aims (Steedman, 2008), current engineering approaches have become extremely focused. This has led to debate about the importance of NLP to the field of linguistics and cognitive science more broadly (Mitchell & Krakauer, 2023; Rawski & Heinz, 2019), with some proponents claiming that the new technology reflects a paradigm shift for the field (Baroni, 2022; Wei et al., 2022), while others claim it is overhyped (Shanahan, 2022) or inconsequential (Veres, 2021) or even comes at the expense of scientific insight (Marcus, 2022; Rawski & Baumont, 2023). A problem-centric view would consider this controversy to be a communal problem resulting from a rapid expansion of problem space, due to technological development and a rising adjacency to new external problems. Indeed, a tension between pure and applied research exists in many fields with scientific and engineering components. In many cases, the result is the emergence of distinct research communities doing work that draws from one another (e.g., Linzen & Baroni, 2021; Valvoda et al., 2022). Further, one might imagine that linguistics might develop theories that contribute to the solution of societal problems which have rapidly emerged as a result of NLP technology (Bender et al., 2021).