Also, an out-of-date model which doesn't know about last year's world events, hit songs and new JS libraries is a depreciating asset even before you consider low-cost competitors catching up. So you'd presumably have to do some training just to keep the model up to date at the current quality level (unless you completely give up and just sweat the assets). And on the other side of that coin: over the next few years, do the latest, biggest models continue to generate user-perceived real-world improvements sufficient to keep users wanting the latest and greatest?

> If a major model provider were to just halt progress on developing new and improved models, the open weight alternatives would catch up in a couple years.

That's why it would be bad strategy.

And apparently the most efficient training and inference thanks to their TPUs, IIUC?