[RaffaelCH]

Regarding your first point, if the cost of not doing it while you wait exceeds the cost of training/maintaining the model, then this approach still makes sense. This really depends on how fast you expect cheap (and open-source) models to improve. So it might only be a temporary strategy, but still worthwhile. Also, I expect that specialized models will always be better (cheaper or better outcomes) than a general model, similar to how specialized HW like GPUs is still used even though CPUs have improved a lot too.

Maintaining in the way of ongoing training is relatively cheap, considering that the initial training run is only 500$ (in this example). Creating new examples to account for drift in the training data is more expensive, but these examples can then be reused when training a new model. And to some degree you need them anyways, to evaluate the models and prompt changes you make.

Overall, as you pointed out, this won't always make sense (either due to the cost of creating examples and training, or simply the lack of available data). But they point this out themselves in the diagram towards the bottom of the page: it only makes sense for frequent and verifiable tasks. This might result in this only being a sensible approach for very large companies (they talk about millions of decisions), but it might make sense for them.