You usually do not feel the architecture breaking all at once. You feel it first in the way latency stops being local and starts becoming systemic. A single slow dependency
You know the moment. The bug report says “intermittent timeout,” the team adds a retry, the graph goes green, and everyone moves on. Two weeks later, a different service starts
If you’ve ever been paged at 3:17 AM for something that “resolved itself,” you already understand the problem. On-call rotations are supposed to be a safety net for production systems.
You’ve seen this movie before. A new technology drops, early adopters flood X and LinkedIn with “this changes everything,” and six months later, half the companies quietly abandon their experiments
You’ve probably seen this movie before. A team buys a shiny new platform, maybe a data lake, maybe a workflow engine, maybe “AI.” It solves an immediate pain. It demos
You can spend weeks tuning query plans, resizing pods, and shaving milliseconds off RPC handlers, then still miss your latency target because the real problem is not inside any one
You usually notice the problem before you name it. One team wants a paved road for Spring Boot on Kubernetes. Another wants Node.js on serverless. A data team shows up
Every latency regression looks small in isolation. A few milliseconds in auth. Another network hop in feature flag evaluation. A cache miss that falls through to a chatty backend. None
You’ve seen this movie before. A new tool promises 10x performance, cleaner abstractions, and fewer outages. The demo looks great. The GitHub repo is trending. Someone on your team is