In statistics and machine learning this problem is called concept drift and it's an open research problem. Indeed all the models I listed are already out-of-date with the present state of the world; unless they are fine-tuned on new data.
The regime for the above networks is called "pre-training". The idea here is that rather than training _directly_ on some challenging, specific task, you instead train a more generic task on a _lot_ of data. This gives you a "backbone model" that winds up being very strong on more specific tasks as well. In many cases, the ability to (cheaply) curate or create enough accurate data for specific sub-tasks might not even be possible. It's easier to scrape 400 million captioned images from the internet than it is to find/create millions of visual Q&A prompts/images.
CLIP is a great example of this. While it was trained explicitly "just" to compare images and captions and to output a score of the cosine similarity between the features of the two - I have seen it approach effective state of the art on text-to-image generation tasks, image-captioning, Q&A, etc.
This style of training needs to be updated after it is trained, typically because of the way these datasets are curated. Automatic curation via pretrained models is one option. Another option is to give the model direct access to the internet which is starting to be explored. Pre-training helps a lot with general distribution shift; but it's _probably_ not going to be able predict memes before they happen anytime soon.
Reinforcement learning, on the other hand, requires an agent to learn in real and simulated environments. This obviously lends itself to re-training on-the-fly (and indeed, self-play and on-the-fly retraining are used heavily in practice).
https://youtu.be/kopoLzvh5jY
I have not yet fully grokked reinforcement learning but it is incredibly exciting research and is the correct direction towards making effective use of machine learning in robotics. Note that reinforcement learning and pre-training are not mutually exclusive and may be used in tandem.