There's a few things here that I think are important. First of all, completely agree with the point of this post and I completely expect that to become the outcome of GPT-6 or 7 let's say. Human expert level at everything would be the absolute best.

However, I think it may not be super difficult to achieve superintelligence using LLMs as a base. There's two unknowns here and I'm not exactly sure how they will mesh together:

1. Multi-modality. If we GPT-7 also has video and audio as modalities, and is, say, trained on every YouTube video, movie, and tv show ever made, that alone could potentially lead to superintelligence, because there's a ton of information encoded in that data that ISN'T just human. Predict the next frame in a video for instance would presumably have a way, way higher ceiling than predicting the next token in human written text.
2. Reinforcement learning. Eventually, these models may be able to take actions (imagine a multi-modal model with something like GPT-5/6/7 and Adept's model which can control a desktop environment) which can learn from trial and error based on its own evaluations. That would allow it to grow past human performance very quickly. Machine learning models that exceed human performance almost always use reinforcement learning. The only reason why we don't do that for base models is that the search space is enormous to use an RL policy from scratch, but if we build a model like GPT-n as a baseline, and then use RL to finetune it, we could get some amazing results. We've already seen this from RLHF, but obviously that's limited by human ability in the same way. But there's nothing stopping us from having other reward functions which are used to finetune the model and don't involve humans at all. For instance, I would bet you that if we used reinforcement learning to finetune GPT-4 on playing chess or Go (converting the game state to text, etc), it would probably work achieve superhuman performance on both of those tasks.