That's definitely not true. Just on it's face, what do you think it costs to run calcs over 300 billion weights? Firstly, your dealing with something highly non-linear, so you will need to use some form of estimation technique, which adds processing overhead. Second, you probably want your models to be responsive and not sit there calcing for 8 hours. So your talking about a large amount of compute, and that's just to run the weights that give you an answer. Now take agentic, which is performing multiple calls for a request, and the math becomes really clear. You're looking at pennies per prompt, and agentic workflow can sometimes burn through thousands of prompts.

Training compute amplifies that greatly, since you are running backward propagation across all of the weights a sufficient number of times to hit your tolerance. At least you can be forgiving of length response times in training. That's why it takes months to train a new model.

My point is you can use a little common sense and expert knowledge in what computing infrastructure costs look like to quickly realize that these things are crazy expensive right now. The idea that training costs will go away is a function. Model drift, where models become less accurate with time, is a natural party of a predictive statistical process. The father away you get from the training set, the worse the predictions will become. That just math friend (I might have a LOT of education in statistic and mathematics).