Users are reporting a perceived decline in Claude's performance, but data suggests the issue lies in the scaffolding rather than the model itself. Factors like cache TTL and adaptive thinking effort flips are likely causing the instability. This discussion highlights why enterprise leaders must distinguish between core model degradation and infrastructure configuration when auditing AI reliability.
Today's focus is the critical distinction between model intelligence and the orchestration layers that deliver it.