> Training LLMs on detailed interaction data from AI-powered IDEs could become a powerful flywheel leading to the automation of practical coding.

I agree. But this is a more general flywheel effect. OpenAI has 500M users generating trillions of interactive tokens per month. Those chat sessions are sequences of interaction, where downstream context can be used to judge prior responses. Basically, in hindsight, you check "has this LLM response been good or bad?", and generate a score. You can expand the window to multiple related chats. So you can leverage extended context and hindsight for judging response quality. Using that data you can finetune a RLHF model, and with it finetune the base model.

But it's not just hindsight analysis. Sometimes users test or implement projects in the real world, and the LLM gets to see idea validation. Other times they elicit tacit experience from humans. That is what I think forms an experience flywheel. LLM being together with humans during problem solving, internalizing approaches, learning from outcomes.

Besides problem solving assistance LLMs are used for counselling/keeping company/therapeutic role. People chat with LLMs to understand and clarify their goals. These are generative teleological models. They are also used by 90% of students if I am to believe a random article.

So the triad of uses for LLMs are: professional problem solving, goal setting/therapy, and learning. All three benefit from the flywheel effect of interacting with millions of people.