If they've already built themselves a loyal customer base (which is usually the point of fighting a price war) and the customers are happy with the technology they have, then if funding is tight and turning a profit is more important why wouldn't they pivot to optimizing inference by stopping further training, freezing the model versions, burning the weights into silicon and building better caching strategies and improving harnesses and tools that lower their cost and increase their margin?

If all they do is hike prices then they'll lose customers to competitors who don't or who find a way to serve a similar model cheaper.

The demand isn't going to go away purely through higher prices. Once people know something is possible they will demand it whether supply is constrained or not. That's a huge bounty for anyone who can figure out how to service that demand.

There is no evidence that labs are losing money on inference subscriptions. The labs have massive fixed costs, but as long as inference spend is higher than the datacenters they use for inference cost all they need to do to become profitable is scale up. Right now software engineers are basically the only ones actually paying for inference, the labs just need to create coding assistants for everything that are good enough that every white collar worker in the country(world?) is paying a $1000/yr subscription. Certainly theres a lot of risk, will models become commoditized and everyone switches to open models? can they actually get non software engineers to pay for inference in mass? But its not like theres no path