Scaffolding, tool use, and fine-tuning can multiply a base model's effective capabilities far beyond what was evaluated at training time. This means the evaluated artifact isn't the deployed artifact. How should governance account for post-training enhancements, and are current evals systematically underestimating deployed risk?