Imagine you want to make a script with the LLM, it generates code, and you run it and it errs out. You paste the error, the model gains a new nugget of feedback. Do this sufficiently many times with many devs, and you got an experience flywheel.

But this applies to all domains. Sometimes users return days later to iterate on a problem after trying out in real life ideas generated by AI, this is how LLMs can collect real world feedback and update. Connect related chat sessions across days, and evaluate prior responses in the context of the followups (hindsight).

There is also a wealth of experience we have that is not documented anywhere. The LLM can gradually rub off our lived experiences by making itself useful as an assistant and being in the room when problems get solved.

But this experience flywheel won't be exponentially fast, it will be a slow grind. I don't think LLMs will continue to improve at the speed of GPT 3.5 to GPT 4. That was a one time event based on availability of internet scale organic text, which is now exhausted. Catching up is easier than innovation.

But we can't deny LLMs have "data gravity" - they have a gravitational pull to collect data and experience from us. We bring data right into AIs mouth it doesn't even have to go out of its way to scrape or collect. Probably why we have free access to top models today.