Earlier quoted context omitted.
Just because many more humans spent many more years and many more $$$ building GPT-3 for your convenience.
Right, but GPT-3 can be used generally . That's the difference. It scales because you don't need to build an entirely new model for each different use case. You just change the prelude and use it for something new.
But you have to understand, the use cases you mention are shallow and limited. The heart of GPT, the fine-tuning, is gone. And it looks like even OpenAI gave up on letting users fine-tune, because it means they essentially do build an entirely new, expensive model for each use case.
I wanted to make an HN Simulator, the way that https://www.reddit.com/r/SubSimulatorGPT2/ works. But that's far beyond the capabilities of metalearning (the idea that you describe).