As an new AI image generators are not just replicating existing pictures but generating wonderful new ones, language models are not just replicating average of what they seen but are capable of generating novel ideas.
I think that the reason image ones have caught on better in some ways is because they don't need to be accurate. We're not asking them to understand anything, just produce images based on text prompts (which is amazing stuff all by itself).
Image generation didn't catch on because of lack of accuracy, but because of how GOOD the results are. It's made artwork immediately accessible to the masses without the huge learning curve. That's where these AI's are going to shine very very quickly.
Show them enough pictures labelled "Van Gogh" and they get an idea of what "Van Gogh" looks like. They dp am awesome job of that.
The problem with the text ones is that people think that showing them words mean that they make a model of the thing the words are describing, rather than of how those words go together.
It's clear the art AI's have a model of "van gogh's" style, and apply it to create very unique forms of art. The neural model weight's aren't storing compressed images of van gogh, but relationships and mathematical models about a concept.
I believe a colossal truth is that the most efficient way to learn how the words go together is to at least make some approximate model of what the words are describing. And our optimization algorithms and model architectures are good enough to find theae solutions.