GoKawiilEveryone is watching for the next frontier model.
The more interesting layer may be the one in front of it.
Routers are becoming the control plane for AI inference. Their first role was practical: route the right request to the right model. That already matters because production AI is no longer a one-model world.
A router can cut cost by deciding when a request deserves a frontier model and when an open-source or local model is enough. It can make safety policy executable by sending sensitive domains to stricter models, stricter filters, or stronger review paths. It can coordinate cloud and edge, keeping private or low-latency intent local while escalating harder work to the cloud.
Those are important jobs.
But the next router job is more interesting:
A router can make the model better.
Not by changing weights. Not by asking every application to build a bespoke agent graph. By turning one model API call into a bounded collaboration inside the serving layer.
Figure 1: The router is moving from model selection to capability construction.
This is why Sakana Fugu landed so loudly: it made a commercial product out of a simple but powerful idea, that a "model" can be a surface, and behind that surface can be a team. The research around this idea, including the Fugu technical report and coordination papers such as Conductor and Trinity, gives useful language for thinking about orchestration.
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