Either there's a massive reduction of inference and training costs due to new discoveries and then those big hardware hoarders have no moat anymore or nothing new is discovered and they won't be able to scale forward.

Either way it doesn't sound good for them.

Control and consistency, as well as support for specialized workflows is more important, even if it comes at the expense of model quality.

1. AI succeeds and this success is defined by they will be able to raise prices really high

2. AI fails and receives a bailout

But if there are alternatives I can run locally, they can't raise prices as high because at some point I'd rather run stuff locally even if not state of the art. Like personally, USD 200 a month is already too expensive if you ask me.

And the prices can only go up, right?

For the long-term value of a horribly overpriced and debt ridden corporation it matters quite a lot if the catch up of open source to adequate is less than a decade in instead of several decades in some uses and a few more in others.

I'm not sure what entirely to do about it, but the fact that some providers still release open models is hopeful to me.