AI-mediated scientific production is increasingly discussed either as a productivity breakthrough or as an epistemic risk. This paper argues that neither frame is sufficient on its own because both under-specify the institutional ecology through which scientific claims are produced, verified, contested, and archived. Building on prior work that reconstructs cognitive rights as habitat rights and introduces the economy of judgment as a three-layer mechanism operating through six diagnostic axes, the article applies that framework to a single documented case: the public GDE emergent-gravity branch and its two companion manuscripts. The claim is intentionally narrow. The paper does not adjudicate the underlying physics and does not offer causal measurement or validated metrics. It asks whether the framework helps explain a structural redistribution in which production costs decrease while verification costs are shifted downstream onto disciplinary communities, archival infrastructures, and other institutions of scientific judgment. The analysis shows that the six axes—time of interpretation, visibility, provenance, capability, contestability, and institutional pluralism—map onto the scientific production chain without ad hoc modification. It also introduces the concept of distributed capability deficit, a condition in which no participant in the production chain can independently certify the output, and links it to the risk of sophisticated wrongness. Four middle-range propositions, three framework-level hard nulls, and a portability agenda are formulated to make the argument falsifiable. The contribution is diagnostic rather than prescriptive: it offers an analytically portable way to study AI-assisted scientific production across domains while keeping self-reference, provenance opacity, and external-validation limits explicit.
Andrea Viliotti (Thu,) studied this question.