n this particular moment in AI, one does not need to show why the object of interest justifies researcher attention because the social world is now built to make its significance appear self-evident. Important authorial work–both justification and historicization–gets cut short as a result. This short-cutting also means we have already lost the first site of struggle: we are limited in how much we can question why it matters at all. We are not in a position to conclude that it matters, when and if that is the case.

Moving out from the muddled middle, and exploring both edges, can help clarify why or whether a particular object is worthy of its researcher. When it is not, it might still be worth our time as vocal adversaries. When we ask, “does this need my analysis or my advocacy?” we avoid unwittingly channelling others' agendas. We honor our own expertise when we question what constitutes a deserving matter of STS attention. While few of us can avoid the traps of funding requirements, some researchers do have the capacity to cultivate stronger technodiversity, and to sit with curiosities that feel somehow peripheral or irrelevant or otherwise not in keeping with one’s professional self-narrative. Scholars who live in democracies–not just as individuals but as collectivities and associations–could also use the approaching burst of the AI bubble as a frame to draw policymakers and grant makers towards what is worth knowing more about, and not just what is worth resisting.