They dont need to scale down anything. AGI is a red herring.

Even Deepseek at its absurd prices is a very healthy business. Regarding their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation. Because of this, all of them are spending aggressively on compute. Apart from that, user acquisition and data labelling are the major costs that are preventing net profitability right now. High quality data labelling is said to not have a cost advantage in china etc as well and they pay global market prices for this. I can confirm this is true in india too the model companies I know pay global market rates for high quality data.

> Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?

By limiting the number of tokens you use per month? per week, per hour? And by limiting the inference time compute dedicated to each turn in each session.

> need to recoup

the world economy has shown itself capable of handling decade-scale recouping easily

The main obstacle today in the inference business is the high variability in usefulness/token. This does not need to be solved, but rather only quantified. Innovation is needed to be able to reasonably bound this variance for a reasonable subset of tasks. And we are making progress on this. Naturally though, tasks on the frontier of current capabilities have very high variance. The last couple of years has followed the pattern where tasks no longer on the frontier have reduced variance, but I am not claiming this will continue to be the case generally as the frontier improves.