AI Needs Safety Infrastructure, Not Just Guardrails, as It Enters the Real World

Writer Jinav Shah argues that AI systems are rapidly moving beyond answering questions to taking real-world actions such as booking appointments, executing code, and approving transactions. Unlike traditional software bugs that can be traced to specific lines of code, AI errors are distributed across billions of numerical parameters with no clear point of origin. Shah compares this challenge to how past technologies — cars, electricity, medicine — required dedicated safety infrastructure before scaling into everyday life. He highlights a core technical problem called superposition, where a single word is represented by hundreds of overlapping numbers encoding multiple meanings simultaneously, making errors nearly impossible to isolate. His central argument is that the AI field urgently needs interpretability and diagnostic tools — not to slow progress, but to deploy AI responsibly in high-stakes situations.