Personally, language diversity should be the last thing on the list. If we had optimized every software from the get-go for a dozen languages our forward progress would have been dead in the water.
I mean more on a model performance level though. It's been shown that something trained in one language trains the model to be able to output it in any other language it knows. There's quality human data being left on the table otherwise. Besides, translation is one of the few tasks that language models are by far the best at if trained properly, so why not do something you can sell as a main feature?
At least from a distance it seems like training a multilingual state of the art model might well be easier than a monolingual one.