Since it's not mentioned in the article, the distinction between open source and open weights is important. Open weights models are almost like a 'first shot is free' entry drug. Without at least the original training data your ability to meaningfully upgrade it is so limited that its utility will quickly fall behind the latest versions of continuously developed models. So much that it'll leave you craving for another release, or have you going back to the provider's API. Even simple things like moving the knowledge cutoff forward will noticeably improve the UX, and that's not to speak of more fundamental improvements like reasoning, quantization-aware training and all the goodness that's yet to come.

Sure, we can do research to bring improvements to open weights models, but it's the same thing: it's either open source or it won't benefit the general public nearly as much.