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.
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