Yeah i remember that Ed article ! I don't think the technical aspects are relevant to the newer generation of models, but yeah of course any attempt to compress inference costs can have side effects : either response quality will degrade for using dumber models, or you'll have re-inference costs when the dumb model shits its pants. In fact the re-inference can become super costly as dumber models tend to get lost in reasoning loops more easily.

I think a lot of it is still done by hand, and there is also synthetic data distilled from larger models of course.

The switch you mention (from 4th gen to 5th gen GPT) is when they introduced the model router, which created a lot of friction. Basically this will try to answer your question with as cheap a model as possible, so most of the time you won't be using flagship 5.2 but a 5.2-mini or 5.2-tiny which are seriously dumber. This is done to save money of course, and the only way to guarantee pure 5.2 usage is to go through the API where you pay for every token.

There's also a ton of affect and personal bias. Humans are notoriously bad at evaluating others intelligence, and this is especially true of chatbots which try to mimic specific personalities that may or may not mesh well with your own. For example, OpenAI's signature "salesman & bootlicker" personality is grating to me and i consistently think it's stupider than it is. I've even done a bit of double blind evaluation on various cognitive tasks to confirm my impression but the data really didn't agree with me. It's smart, roughly as smart as other models of its generation, but it's just fucking insufferable. It's like i see Sam Altman's shit eating grin each time i read a word from ChatGPT, that's why i stopped using it. That's a property of me, the human, not GPT, the machine.