There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…
I think a lot of this is based on preconceptions. A lot of apps were made with Electron, because it was common wisdom that native is 'too hard'. Now with LLMs, people write native apps in Rust, and I'd like to think some of them found that there isn't such a huge jump in difficulty they assumed there would be.
That's the same case finding companies that will actually pay for hand made LLM instead of using something from big providers will be hard because most companies won't be able to afford it.
Yeah if you find a company that will do that stuff directly, good for you, but you will have to be very lucky and you will have to compete with other people who followed PG advice.
So I would rather learn all there is about properly using LLMs and integrating them with existing systems, that will most likely by useful for 90% of companies out there.
Building business niche harnesses is in my opinion much better direction. Knowing what will work best in specific cases is it FTS or vector search, optimisation of usage, getting best results while using cheaper models, knowing how to use tools to run models on the servers, and all the tooling around that like various MCP or just tooling that will be provided to models.
That is what I am currently busy with and I already have customers for that knowledge.