The model's arent there yet. I am confused how people cannot extrapolate into the future and understand that the models will improve
As a counterexample: The maximum speed of travel for the average person for millenia used to be as fast as they could run, then it was as fast as the fastest horse can run, and then within a century it has accelerated to almost the speed of sound – at which it has plateaued.
Looking purely at the decades of acceleration, you might have very well concluded from the data that we'd be making significant headway towards getting within double-digit percentages of the speed of light at this point.
a) trained on crappy data, including questionable RLHF feedback. b) trained with questionable embedding layers. c) trained with questionable loss functions d) trained with questionable optimizers e) trained at questionable precision (somewhat related to d) f) are very big which stops fast iteration around all the above.
It's kinda like semiconductors. You don't have to think of it as a curve - just ask people who are really close to them and they'll have a laundry list of stupid stuff which is currently done and will likely be improved upon over time.
But when talking about future growth potential, I don't think you can get around making assumptions about the shape of the growth function.
Even if someone could point to a function and say "10x better" by 2030 - what does that even mean in the context of an LLM for example?