The Verification Bottleneck: Managing Trust in Post-AGI Science

AI systems can now compress decade-long research programs into days. The result is a structural asymmetry: discovery scales faster than verification. Current AI achieves only 21\% recall detecting errors in manuscripts; it generates plausible claims far better than it verifies them. This ``verification bottleneck'' has been flagged as a concern, but we lack frameworks for managing it. We contribute three operationalized mechanisms: (1) epistemic triage combining prediction markets, statistical thresholds, and anomaly detection to prioritize what gets verified; (2) verification cascades, a hierarchical architecture assigning epistemic status based on verification depth; and (3) an extension of provisional knowledge to AI-generated claims, with explicit conditions for status transitions. Drawing on social epistemology and the sociology of scientific knowledge, we examine how these frameworks address scalable oversight, trust, and human roles in machine-accelerated science. Without such frameworks, science risks epistemic pollution: a state where valid and invalid claims become indistinguishable.