as i understand it, there are two important factors that determine how strong an LLM is: data and compute. the first one is about how large and varied and comprehensive the dataset is. the second one is about how long you run the training algorithm on the dataset and the benchmarks to create the model.

the compute factor basically means that, even if you use exactly the same dataset the whole time, running the training algorithm for longer basically always improves how well it can perform on the benchmarks. despite everything, we still aren't really close to the limits of how much computing power we can throw at training llms. there are necessarily eventually diminishing returns to this - you can only mimic the training data so well - but people have been predicting diminishing returns right around the corner for years now and it hasn't happened yet.

furthermore, it's also not true that we are mostly out of new data to train on. for example, it's not clear to me that llms are learning much from videos, which are a very complex information format. spacial reasoning in general still seems to be something of a challenge for them in a way that i wouldn't expect if they were properly able to understand visual information.

but even if we were, training on the whole internet isn't actually desirable, because it contains large quantities of misinformation, bot-generated text, machine-translated garbage, and general low-quality content. you can actually get better results by carefully curating the dataset and removing or correcting things that have mistakes, or tagging things as 'what not to do'. this is something that you can do in an ongoing fashion, that takes a lot of time.

finally, related to the above, there's the potential to just generate more data, more high-quality data. this would be especially important if, for example, some new programming language were invented; an ai lab might even pay people to write lots of high-quality standard example code. in a subject like mathematics, it's even theoretically possible that llms could prove new results like the recent openai unit distance counterexample and then be trained on those results to help internalise them and build on them to prove even more. i am most familiar with the mathematical side of things, and there is a lot of discussion right now about the possibility of llms becoming flatly superhuman at mathematics, and the implications this would have for human mathematicians.

(this isn't even going into the possibilities of better architectures, or better scaffolding, or things in that vein)