The plan is to have LLM working completely autonomously, in that case, the more resources you have, the better. Perhaps people will use local LLM to ask questions, or coders use them for their personal projects, but that's not where the real money is.
If the companies don't see that kind of value (so LLMs don't become dramatically better in some kind of quantum leap from where they are now), they won't want to pay those costs. Already, most AI projects in corporations tend to fail.
If the efficiency of LLMs gets 10x better, then either corporations will "private cloud" their own AI or start using competitors that aren't carrying those kinds of debt loads from the "gold rush" phase.
Very much unlike with software. Where the goal for long while is to burn as many resources as possible on end user devices.
Your real personal agent which knows you and helps you like "good morning elmer2, your calendar invite for dinner is today, you will need to leave at 18:18 if you want to use your normal public transport route per train. I put an alarm in your phone for you"
Agents to agents
Agentic teams.
Finetuned models for everything like Java/spanish coding model.
Very long term research like multiply hours or days or weeks and plenty of these in parallel.
What they can't do is the rug pull of pricing like Fable did, hoping for profitability while playing the "it's so super smart" card. It's very profitable, but customer will be very happy to leave for cheaper pasture and that's why the recent news about this or that cheaper chinese models make headlines.
Essentially, the rush now is "if I make it a boring profitable company I'm not worth a trillion AND i'm overshadowed that plays the singularity card even if they're bullshitting"
I'm not saying I see them going that way or that I would, but at least THAT would possibly work.
Even better, that "gamble" will have to be rescued by taxpayer money.