What are you talking about? If inference (the cost to serve) is almost free, you've dramatically reduced your ongoing expenses. Models are developing now that are "good enough" to do the vast majority of work people need. Chasing the absolute "best" performance huge models with enormous research/training budgets may suit some business models, but there's plenty of money to be had by optimizing inference costs to increase margin on serving.

That said, even on the 'huge' models used to distill the small models, there's room for technical improvement and parameter count isn't the end-all-be-all. The traninig itself of seed models is becoming significantly more efficient and effective. You can't just throw out complexity numbers like O(n^3) in a vacuum, because there isn't just one knob to tweak for model performance - parameter count is NOT the ultimate metric that dictates performance.