Even the largest LLM has had less "total information" than most humans take in through all of their senses over their lifetime. A single day for a baby is taking in a continuous stream of among other things high quality video and audio and does a large amount of processing on that. Much of that for very young babies is unsupervised learning (clustering), where baby learns that object A and object B are different despite knowing nothing else about their properties.

Humans can learn using every ML learning paradigm in ever modality: unsupervised, self-supervised, semi-supervised, supervised, active, reinforcement based, and anything else I might be missing. Current LLMs are stuck with "self-supervised" with the occasional reinforced (RLHF) or supervised (DPO) cherry on top at the end. non multi-modal LLMs operate with one modality. We are hardly scratching the surface on what's possible with multi-modal LLMs today. We are hardly scratching the surface for training data for these models.

The overwhelming majority of todays LLMs are vastly undertrained and exhibit behavior of undertrained systems.

The claim from the OP about scale not giving us further emergent properties flies in the face of all of what we know about this field. Expect further significant gains despite nay-sayers claiming it's impossible.