The article examines the pitfalls of standardization within a mid-sized SaaS company, where a small development team struggles with balancing fairness and throughput in data processing services, exacerbated by increased load from a new customer. The team's attempt to standardize queuing systems using a shared Kafka-backed library fails to resolve the issue and instead complicates matters, mirroring historical failures of centralized, top-down solutions as explored in James C. Scott's "Seeing Like a State." The text argues that such approaches often overlook local, tacit knowledge and the nuanced conditions on the ground, drawing parallels with the flawed practice of scientific forestry, where attempts to impose uniformity led to ecological and economic failures. The author suggests that embedding with teams to address specific problems may yield better results than pursuing a broad, generalized solution, as top-down standardization efforts risk neglecting the implicit understandings of those closest to the problem.