Some systems look scalable right up until they meet real production traffic. The tests pass. Dashboards are green. The architecture diagram looks clean. The team feels good about it. Then traffic grows, usage patterns shift, and the system starts failing in ways that were never obvious in staging. I have seen this in very different environments: public-sector infrastructure supporting criminal justice workflows across 87 counties, and enterprise AI infrastructure where even tiny per-request costs multiplied across high-volume evaluation pipelines. In both cases, the system did not fail because someone forgot to add servers. It failed because an assumption that was harmless at small volume became expensive at large volume. Most of them were assumptions nobody had written down anywhere. This is the first post in a five-part series on scale assumptions: the design decisions that look harmless early and become painful later. The assumptions that fool you The first trap is linear thinking.…