You are looking at things like the failure of full self driving due to massive long tail complexity, and extrapolating that to LLMs. The difference is that full self driving isn't viable unless it's near perfect, whereas LLMs and text to image models are very useful even when imperfect. In any field there is a sigmoidal progress curve where things seem to move slowly at first when getting set up, accelerate quickly once a framework is in place, then start to run out of low hanging fruit and have to start working hard for incremental progress, until the field is basically mined out. Given the rate that we're seeing new stuff come out related to LLMs and image/video models, I think it's safe to say we're still in the low hanging fruit stage. We might not achieve better than human performance or AGI across a variety of fields right away, but we'll build a lot of very powerful tools that will accelerate our technological progress in the near term, and those goals are closer than many would like to admit.
