MSFT and Apple are taking the same approach.

The frontier model space costs 1000x as much to develop as the small language models, and is only 1.5 years ahead.

Factually, the frontier models have not paid for themselves. So, if you're MSFT and Apple, you don't need to run in a race where even the winner loses massively.

You can try to train models 1.5 years behind that are highly likely to be profitable, given your market position.

The average person is lagging behind what AI is capable of by 3+ years anyway...

So you can save 1000x on training and 10x on inference and just use SOTA small models.

Why spend $5B training a model that's for sure not going to make $5B (after inference costs) when you can spend $5M building one that WILL make far more than that after inference costs?