It's one thing to train on Common Crawl in 2023, but what about when you have to shell out millions of dollars just for access to data sets to train on in the future? Same thing with human reinforcement. The customers for both are willing to pay much more than a crowdfunding campaign would.
Training is expensive now, but data sets can be expensive in the future.
If the dataset contains text from 2022, things that happen in 2023 and later won't be in it. The model will only get you so far, and new data, events, concepts, discoveries, etc will be absent.
If we trained models on all text generated up until 1900, for example, you could get it to produce some impressive results if just generating text is the goal. If the goal is to build something that imitates a more general AI, it wouldn't "know" about antibiotic treatments for common illnesses, modern vaccines, either World War, powered flight, transistors, computers, etc. It would only be useful for so much.
Or alternately someone could make a major advance in distributed training and we could all contribute cycles in a distributed effort like Folding@Home. As it stands training requires far too much bandwidth for synchronization and moving model data around. Some approach to sharding training would have to be discovered. It’s an open problem area.
Neural networks are very parallelizable and training is stochastic so my intuition is that it should be possible. Even if it were less efficient than synchronous training you could make up for that by harnessing 100X the compute from a huge crowd.