OpenAI’s data infrastructure was experiencing mysterious crashes due to an 18-year-old bug in the GNU libunwind library and silent hardware corruption on an Azure host. The team used population-level analysis to debug the issue, treating it like an epidemiology problem to identify and fix the seemingly inexplicable crashes. By building a high-quality data set about the entire population of crashes, they were able to uncover the two unrelated bugs and resolve the issue. Why it matters: this story highlights the importance of rigorous debugging and the potential for hidden bugs in widely used open-source libraries to cause significant problems in AI systems.