in fact I am betting opposite. frontier models are getting not THAT much better anymore at all, for common business needs at least. but the OSS models keep closing the gap. which means if trajectories hold there will be a near future moment probably where the big provider costs suddenly drop shaerply once the first viable local models consistently can take over tasks normally on reasonable hardware. Right now probably frontier providers rush for as much money as they possible can before LLMs become a true commodity for the 80% usecases outside of deep expert areas they will have an edge over as specialist juggernauts (iE a cybersecurity premium model).
So its all a house of cards now, and the moment the bubble bursts is when local open inference has closed the gap. looks like chinese and smaller players already go hard into this direction.
Local open inference can address hardware scarcity by repurposing the existing hardware that users need anyway for their other purposes. But since that hardware is a lot weaker than a proper datacenter setup, it will mostly be useful for running non-time-critical inference as a batch task.
Many users will also seek to go local as insurance against rug pulls from the proprietary models side (We're not quite sure if the third-party inference market will grow enough to provide robust competition), but ultimately if you want to make good utilization of your hardware as a single user you'll also be pushed towards mostly running long batch tasks, not realtime chat (except tiny models) or human-assisted coding.
So its all a house of cards now, and the moment the bubble bursts is when local open inference has closed the gap. looks like chinese and smaller players already go hard into this direction.