The demo trap Every AI product looks impressive in a demo. Low latency, clean responses, happy path all the way. You ship it feeling confident. Then real users show up. And everything starts falling apart in ways you never saw in development. Latency spikes that don't reproduce locally. Costs that triple overnight without any obvious cause. Responses that were consistent in testing but became wildly unpredictable under load. Your "simple" pipeline that was three API calls in the prototype is now fourteen moving parts, and you're not entirely sure what half of them do under pressure. This is the moment most teams realize that building an AI product isn't really about the model. It's about everything around it. Why production AI is a distributed systems problem In development, you're working with clean data, low concurrency, and forgiving conditions. Production is the opposite.…