I think it's better to think of GPT-3 not as a model but a dataset that you can interact with. Just to give an example - recently I needed to get static word e

I think it's better to think of GPT-3 not as a model but a dataset that you can interact with. Just to give an example - recently I needed to get static word embeddings for related keywords. If you use glove or fasttext, the closest words for "hot" would include "cold", because these embeddings capture the context these words appear in and not their semantic meaning. To train static embeddings that better captures semantic meaning, you'd need a dataset that would group words together like "hot" and "warm", "cold" and "cool" etc. exhaustively across most words in the dictionary. So I generated this dataset with GPT-3 and the resulting vectors are pretty good. More generally you can do this for any task where data is hard to come by or require human curation.