To train GPT, all of the training data (the internet of text, scanned books, etc) had already existed, even before the GPT project began. Arguably, the compute required (for GPT-3) also already existed, even before GPT-2.
The GPT project really just came down to investing in all of the pieces to take the ideas from a 2017 research paper to the next level. Nobody knew if X thousand GPUs, plus all of the internet's text, plus neural network transformers, would work out. But somebody took a risk in putting together the existing pieces, and proved that it can.
There's no analogy here to humanoid robotics. Not only is the data required for neural network operated humanoids close to non-existent (at the scale needed), but the nature of the data itself is enormously more complicated that taking a list of tokens in a vocabulary, and outputting 1 more token from the same vocabulary.
That being said, I still applaud the ambition of the Figure team. While I think it's clear they are presenting incredibly cherry-picked examples, they aren't trying to mislead consumers with a product for sale (because... they can't). Instead, they are productizing important research to investors, who would otherwise waste money on less important and less ambitious projects. So overall I find projects of this nature to be a net positive for technical innovation.