The AI landscape is undergoing a significant transformation. The primary focus has shifted from model intelligence to more nuanced aspects such as latency, orchestration, context handling, reliability, inference economics, developer workflow, deployment flexibility, and more.

This change is driven by the rapid improvement in model quality. As a result, having the best benchmark no longer guarantees the best real-world experience. Teams are now optimizing their approaches by focusing on workload routing, hybrid local/cloud setups, smaller specialized models, faster iteration cycles, and predictable scaling costs.

AI is maturing into a problem that is as much about systems and infrastructure as it is about the models themselves. This raises an interesting question: are others in the field observing the same shift, or does the capability of frontier models still dominate most workflow decisions?

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