Take railroads, for instance. Back in the 1800s, too many were built. Many of them (almost all, I think) went bankrupt at one time or another. At that point, the creditors made a rational evaluation: Is this worth keeping, or not? If yes, then let's try to reorganize a business that can actually survive. If not, tear it up and sell the scrap. Some were kept, some were torn up.

But the post-bankruptcy railroads that were kept were able to operate without the burden of the construction costs, because that had been destroyed in the bankruptcy (along with the original owners).

So, AI: I suspect that the training costs (plus hardware costs) dominate the operating costs. If that is so, then a post-bankruptcy AI company could still be a profitable business. It wouldn't be able to grow its hardware very fast, or be able to re-train new models very often, but it could still be an ongoing business. The current owners would still get nothing, though.

Inference is probably okay priced, at least at API prices.

The salaries, training and especially data center build out might be a little crazy right now.

If I use an LLM for programming why would it need to update constantly. As soon as you could run a SOTA class model on let’s say the surely upcoming 1TB RAM MacStudio it is out there and can never be taken back. If that was my only venue to get access I would shell out those 10k in a heartbeat

The task is not so monumental that it could not be provided at a reasonable price or financed through advertising, but as long as major players are willing to operate at a loss, you face little choice but to operate at a loss yourself.