Outerport (YC S24) released technology enabling hot-swapping of AI model weights in production environments without redeployment or service interruption. The platform reached 93 points on Hacker News, indicating sustained operator interest. Model weight swapping removes deployment friction from the experimentation cycle. Teams can now iterate on quantization strategies, fine-tuning approaches, or candidate model versions against live traffic without coordinating infrastructure restarts. This compresses the feedback loop between hypothesis and validated result—critical for systems where model performance directly impacts revenue or user experience. For operators, this shifts economics around A/B testing infrastructure. Previously, testing model variants required either parallel deployments (capital overhead) or scheduled downtime windows (operational coordination cost). Hot-swapping collapses both constraints. The workflow changes from "stage update → coordinate deployment window → monitor rollout" to "load weights → measure → swap." This becomes particularly valuable in latency-sensitive or high-availability contexts where any downtime compounds costs. Second-order effect: teams may run more frequent, smaller experiments rather than batched quarterly updates, accelerating the pace at which model selection becomes empirical rather than theoretical.