People talk about these models like they are "catching up", they don't see that they are just trailers hooked up to a truck, pulling them along.
They usually lagged for large sets of users: Linux was not as advanced as Solaris, PostgreSQL lacked important features contained in Oracle. The practical effect of this is that it puts the proprietary implementation on a treadmill of improvement where there are two likely outcomes: 1) the rate of improvement slows enough to let the OSS catch up or 2) improvement continues, but smaller subsets of people need the further improvements so the OSS becomes "good enough." (This is similar to how most people now do not pay attention to CPU speeds because they got "fast enough" for most people well over a decade ago.)
Proxmox became good and reliable enough as an open-source alternative for server management. Especially for the Linux enthusiasts out there.
That's going to be pretty hard for OpenAI to figure out and even if they figure it out and they stop me there will be thousands of other companies willing to do that arbitrage. (Just for the record, I'm not doing this, but I'm sure people are.)
They would need to be very restrictive about who is allowed to use the API and not and that would kill their growth because because then customers would just go to Google or another provider that is less restrictive.
Number two I'm not sure if random samples collected over even a moderately large number of users does make a great base of training examples for distillation. I would expect they need some more focused samples over very specific areas to achieve good results.
The economics probably work out though, collecting, cleaning and preparing original datasets is very cumbersome.
What we do know for sure is that the SOTA providers are distilling their own models, I remember reading about this at least for Gemini (Flash is distilled) and Meta.