According to @karpathy, out-of-the-box PyTorch models continue training even when the underlying infrastructure experiences failures, highlighting both the robustness and potential risks in AI deployment scenarios (source: @karpathy on Twitter, 2024-06-29). This behavior allows AI teams to maintain progress during transient infra issues but may conceal deeper failures that could compromise model accuracy or data integrity, especially in large-scale, production-level machine learning pipelines. Enterprises using PyTorch in mission-critical AI applications should implement advanced monitoring and failure-handling mechanisms to ensure model reliability and minimize business risks.