Comment by nbardy
8 hours ago
This is a wildly incorrect and myopic view on the world.
Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models.
There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm working on finetuning models for starting my own companies.
I find the fine tune approach more interesting than straight to RAG and MCP.
End of the day they're all customized data stores and protocols to interact with them. May as well stick to a uniform toolkit with fine-tunes.
Not that other tools aren't useful. But reaching straight for a bunch of infrastructure reliant services is like jumping in with k8s when you're still at a stage where basic mocks in code are sufficient.
I won't roll my own encryption or UI lib but want to stay focused on the incompleteness of the project I have to ship not all the buttons and knobs of some dependency or framework. Same old manage context switch problem.
Both of you are right. There is demand for tailored (fine-tuned) models; almost every enterprise would theoretically benefit from them.
But there are also a lot of prerequisites, namely does the enterprise have its sh*t together on a technical level. Does it have the processes and data pipelines available to train and benefit from these models? Probably not!
Applied ML is at the crown of a tech pyramid whereas most enterprises are still struggling at ground level. Being able to build from be ground is likely a safer skillset than only knowing how to work at the (non-existent) apex.