Epistemic Infrastructures of Science in AI Era Should Rebalance Costs of Generation and Verification

AI systems can now cheaply generate plausible scientific artifacts such as papers, reviews, and surveys. This has led to \emph{epistemic pollution} in our scientific systems, where unreliable but plausible-looking artifacts accumulate faster than the system can filter them out. The problem is structural: the epistemic infrastructure of science was calibrated to a world where producing a plausible artifact required substantial expertise, labor, and time, so generation cost itself served as a rough filter; AI weakens that filter without lowering verification cost. We argue that \textbf{AI-era science should rebalance the costs of generation and verification through a redesign of the epistemic infrastructure}. The current paper-centered system makes verification expensive: papers compress long-context scientific logic into prose, forcing reviewers, human or AI, to reconstruct underlying argument structure before they can evaluate it. To address this challenge, we propose \textbf{blueprints} as preliminary epistemic infrastructure: structured, decomposed research artifacts that represent claims, evidence, assumptions, and definitions as typed graph components. Blueprints trade an upfront generation cost for cheaper, more local, more distributed verification downstream. We have instantiated the proposal in a proof-of-concept prototype.