Feels like AI is entering its “infrastructure matters” phase

A year ago, most discussions were about which model was smartest. Now it increasingly feels like the bigger differentiators are becoming: * latency * orchestration * context handling * reliability * inference economics * developer workflow * deployment flexibility The interesting shift is that model quality is improving across the board fast enough that “best benchmark” doesn’t automatically translate into “best real-world experience” anymore. We’re seeing more teams optimize around: * workload routing * hybrid local/cloud setups * smaller specialized models * faster iteration cycles * predictable scaling costs In a weird way, AI feels like it’s maturing into a systems/infrastructure problem almost as much as a model problem. Curious if others are seeing the same shift or if frontier model capability still dominates most decisions for your workflows.