The key question is how good that understanding is. For example, a model would likely have a good understanding of various named colours and hex values (e.g. from the HTML specs, X11 specs, and various colour comparison websites) such that it could reasonably correlate that to a CSS entry. It's not clear if/how well a model would identify that given an image, though it should be easy to generate a dataset of image to colour name and/or hex code for training and evaluation.

What's more interesting is whether these frontier models are at their core transformer models, whether they use residual streams to facilitate learning, and whether they are using some other as yet unpublished architecture that gives them an edge.

How do you train an LLM to create a world that only exists in an artist's head?

I think spending a day with just the lighting systems alone would alleviate us of any misunderstandings here. Getting lighting to work right isn't something you can solve by duct taping a vision model to the contraption.

Most of AI is being used to generate procedural content. It is impressive on the first video or first image, and it might look useful on the surface, but it gets grating quite fast.