What I find interesting, though, is how the "feature" isn't really exciting anymore, or because it creates SVGs that aren't "perfect" that we maybe feel underwhelmed sometimes (at least I do).
However thinking about it...if someone asked me to manually create an SVG, or hell even draw a quick doodle on a bit of paper of the same, I'd still probably be much slower than an LLM and potentially end up sketching less accurate anatomy than the machine.
I think the general "organic task" stuff has been mostly sorted out, but in personal and professional experiences using AI to try to _do_ something, I've found less so recently problems with hallucinations and moreso problems with attention.
For example GPT5.6 still has issues where if I provide it with a list of documents and then ask it to raise questions from that information. Then provide it with additional documents that answer some of those questions and ask it to summarise which outstanding questions there are again, it still asks questions that have become irrelevant with the additional documents - but when this is pointed out it knows exactly what to do and produces the correct list of outstanding questions.
I'm sure frontier models are doing all sorts of crazy stuff with attention already, but it seems to me like we almost need some hierarchical attention mechanism like KVL (with Level added) so that it's aware not only of semantic connections between tokens in the context but also of where there are gaps, missing links to assist the model in becoming aware of its own attention span (I guess).