Could you explain your mental model of the situation a bit more?

Let's take something like GML 5.2 that is open source - anyone can run it on NVIDIA or AMD chips. People are currently making money by running GLM 5.2 Nvidia and AMD chips and selling inference on aggregators like openRouter.

So the cost to serve is like cost of Chip or financing for Chip + power and rackspace somewhere. We basically know that each year you will be able to server exponentially more inference per $ on Nvidia and AMD hardware as it gets to newer generations, so why would you expect the cost of inference on open models to also increase?

Like let's say not through a Chinese subsidized provider but like BaseTen https://www.baseten.co/pricing/ why would that get more expensive year over year?

No, they don't, they can also be defaulted on/written off. Open weights are there to stay, plus research, plus the GPUs, just the power needs to be paid.

There's a limit to price hikes due to competition, and open weights can't collude so closed weight companies don't have that much wiggle room to raise prices unless they are much better than open weights.