The “AI experts” I meet fall into two camps: machine learning engineers who
have never run a business, and business people who use AI constantly without understanding
how it works. That gap will not resolve quickly, and waiting twenty years for today's ML
graduates to acquire domain expertise is not a plan.
So: real competency, now. Not the latest social media post about the top ten ChatGPT tricks;
what an LLM actually does, what
persists between conversations and what doesn't, how tool use works, what RAG and
embeddings are for, token economics, evaluation. The glossed-over nuts and bolts that make AI
useful rather than magically impressive.
Karpathy describes LLMs as an emerging operating system — software 3.0 — which gave me
language for something I had already been feeling. The Claude projects I build are not
workflows, they are programs, and I would like to share them the way developers share
code.
I am not closing the expert gap. I am documenting what I am actually doing, in enough
detail that it might be useful to someone else. Including the parts that failed.