(1) is absolutely not true if you actually use these models on a regular basis and include Google in here too. The difference in reliability beyond basic tasks is night and day. Their reward function is just so much better, and there are many nuanced reasons for this.
(2) is probably true but with caveats. Top-tier models will never run on desktop machines, but companies should (and do) host their own models. The future is open-weight though, that much is for sure.
(3) This is so ignorant that others have already responded to it. Look outside of your own bubble, please.
> Top-tier models will never run on desktop machines
Sorry, but you don't know that
I mean it's not hard to understand that if good model can run on consumer hardware, even better models can run in data centers
If we get to the point where a local model can reliably do the coding for a good majority of cases, then the economic landscape changes significantly. And we are not that far from having big open weight models that can do that, which is a first step
Larger, yes, absolutely. Better? Right now it seems that bigger is better, but if we are thinking about long term future, it's not obvious that there isn't a point of diminishing returns with regards to size. I can also imagine a breakthrough, where models become much smaller, with the same or better capabilities as the current, very large ones.
You are always going to get the same scaling laws in model size regardless of what else you do, so the same degree of improvement seen now relative to the smaller models will be achievable in the future. Yes, small models may be on par with previous generation large models, but the same is true for processors and you don't see supercomputers going away. It's the same principle.