I’m not sure it will go this direction. Using a generic (near) frontier model is often cheap enough that you need to talk really big volumes before it pays off to fine tune.
My example: we were doing single digit millions of automated call summaries a few years back at a major bank with GPT-4o. Smaller model gave more rejected summaries (compliance not happy), so we briefly looked at fine tuning a smaller model and basically concluded that even at that scale the effort of data collection, management, fine tuning, hosting the model, etc didn’t have a sufficient business case vs picking up other projects.
My example: we were doing single digit millions of automated call summaries a few years back at a major bank with GPT-4o. Smaller model gave more rejected summaries (compliance not happy), so we briefly looked at fine tuning a smaller model and basically concluded that even at that scale the effort of data collection, management, fine tuning, hosting the model, etc didn’t have a sufficient business case vs picking up other projects.