Hot take: A lot of AI “wins” are demo-grade and don’t survive real users, real latency, and messy edge cases at scale.
Real authority comes from production-grade fixes for messy human data — monitoring, evals, guardrails, and boring reliability work.
What’s your biggest AI deployment nightmare? Ours turned into a full one-month rollback: tracing regressions in prod, combing through code and prompt versions, and auditing logs to figure out what changed, when, and why.
It was fun to untangle, but we'd rather never do that again lol.