> Presumably OpenAI would need a way of discerning the useful output from the useless output You are the validator, using real world testing. But not in one-of

> Presumably OpenAI would need a way of discerning the useful output from the useless output You are the validator, using real world testing. But not in one-off interactions, when you exchange multiple rounds with the model. After the whole chat session is finished, the model can rank their answers in context, having hindsight. They can just look at the later outcomes and observe which idea was good or bad. Especially bad ideas would elicit a response from the user, trying to iterate on them until solved. Basically the LLM is exploring the world through indirect agency. They act through users, and collect outcomes also through users. More complex projects can even be spread out over many days in many sessions, LLM providers need to comb through the logs to see "idea -> outcome" chains. This whole feedback collection process stops working in one-round interactions, like many we have in the API. But in the chatroom a significant number of interactions continue after the first response, or patterns emerge across multiple sessions, as new one-off interactions relate to past ones. Scale this to 200M users, they have a huge amount of personal experience and interactivity to offer to the model. An experience flywheel that could be spinning once a year, or once a month, or even daily, absorbing new experience from users and serving it back to the users.