Yes. Fine tuning is an optimization. Like all optimizations, it’s downstream of figuring out your problem, implementing a naive solution, scaling the naive solution, and getting frustrated by SWAP-C. THEN you start poking at what optimizations are possible, choosing an approach, implementing the optimization, and redeploying.

This cycle happens when you have a well defined use case with high volume. It is the far opposite end of the spectrum from the general purpose intelligence on tap that frontier AI models purport to deliver.

I think focused fine tunes and big general models will coexist. Ideally with smart routing and caching to use small, specialized and local options when appropriate.