Operating one of the world's most heavily used AI services means ordinary engineering decisions arrive with extraordinary blast radius: a serving change touches hundreds of millions of users, a rollout decision made at 2am determines whether an incident is a blip or a headline, and - because the systems are novel - there is often no playbook, no precedent, and no one more senior who knows the domain better than the person on the spot. OpenAI's loop probes for high-stakes technical judgment: the ability to make consequential calls under uncertainty and time pressure with a reasoning process that deserves the stakes, rather than either freezing, escalating everything, or gut-calling and hoping. What interviewers listen for is the anatomy of your decisions, because process is what transfers to novel situations when pattern-matching can't. Strong candidates show the same skeleton across stories: they bound the blast radius before deciding (what's the worst case, is it reversible, who's exposed), they buy information cheaply when time allows (a canary, a shadow run, a five-minute test that discriminates between hypotheses), they distinguish what they know from what they're assuming and say so out loud, they make the call at the moment the option value of waiting runs out - and they own the outcome, including designing the follow-up that catches them if they were wrong. Interviewers also deliberately probe the wrong call: a candidate with no story of a consequential mistake, honestly owned and instructive, either hasn't operated at stakes or can't be trusted to report bad news - both disqualifying at a company whose incidents are public events.