I suspect it will still fall on humans (with machine assistance?) to move the field forward and innovate, but in terms of training an LLM on genuinely new concepts, they tend to be pretty nimble on that front (in my experience).

Especially with the massive context windows modern LLMs have. The core idea that the GPT-3 paper introduced was (summarizing):

  A sufficiently large language model can perform new tasks it has never seen using only a few examples provided at inference time, without any gradient updates or fine-tuning.