Comment by ZainRiz
12 hours ago
I'd respectfully push back on the framing here.
If you look at value as purely the LLM output, then there's a valid argument that the best frontier models will always be better than fine tuned specialists. (I'm not convinced personally, but it's a defensible claim)
But that misses two dimensions: 1. The cost of acquiring that output 2. What is actually "good enough" for that specialist domain
Not every output needs to be the best to produce value.
And as specialist models increase in cost, their cost/value proposition goes down.
At some point, there's a threshold where cheaper, fine tuned models are "good enough" at the task and also substantially cheaper than the expert models.
That's where fine tuning helps.
Personally, I became a believer in fine tuning after fine tuning a 1B Qwen model as a second pass over my local voice transcription app, achieving excellent accuracy at ~zero token cost and waaaay lower latency than if I'd invoked my Claude subscription under the hood.
At some point, the idea of cost/benefit analysis in the software business turned into the benefit analysis. The amount of money going into the frontier LLM model game is fantastically ridiculous. Being much better than the free resources doesn’t even touch how much better they will have to be to justify the expense of creating them, let alone continually maintaining these services.