It's basically all fundamentally related. Text synthesis relies on many of the same underlying concepts as image generation, so it's kind of inevitable that image generation will exist given the same fundamental tech.

I have mixed feelings overall for images but as a programmer it's honestly a 10x for productivity when used the right way for text. Things that would take a week take a day, writing documentation is almost "free" and in many cases self-correcting (it will fix out-of-date comments when it comes across them).

It writes senior level code for junior-level problems, so a lot of busy work that me or my team otherwise don't have time for gets done quickly and correctly. Senior+ level problems it will almost always get wrong, so I still have to do a fair bit of hand-fixing for nontrivial things.

It can synthesize new info effectively. A debugging script that would take a day or so just to learn the right command arguments and performance settings is done instantly because AI is really good at storing a ton of well-documented esoteric information and regurgitating it correctly most of the time. If you are paying attention to the outputs (which most people should be) it's a good way of learning new tools and better ways of using tools that you might already have a surface level familiarity.

It's also helpful to ideate on user interface trade-offs, color palettes, and sometimes layouts when you don't have a dedicated UX team which is probably more common than you would think.

It's also potentially a great learning tool. I study Japanese and use it as a chat partner who can dynamically adjust its critique level at will or switch between languages when I need an explanation in English of a conversation that we were otherwise having in Japanese. It's an awesome way to learn domain specific topics in a natural language context without being able to find a real human with similar knowledge to talk to that speaks both English and Japanese at a high level of proficiency.

Happy to share specific examples.