On the one hand I understand this fairly deeply.

I started doing "ML" ~ 20 years ago building classifiers people would laugh at today and even at the time barely impressed people when they were 95% correct.

I moved into NLP and built NERs that missed 2-10% of named entities per document routinely. Best of breed approaches and models rarely fared better.

Learned the cornerstones in school for ML; linear regression, ANNs, traditional RL, image classifiers, A* bots, etc, most of which got baked into transformers later on.

Then the transformers went from interesting novelty to useful. I couldn't build a useful one locally, but the toys versions were still fun to play with.

Then the novelty LLM went from useful to generally applicable. Then they became a silver bullet.

I still can't build one locally. I can distill or build or fine tune if you give me some rented GPUs. But to call this ML is very much a stretch.

I still use the traditional ML a lot, but mostly for evals and analysis.

I get being naturally bummed by this but I can't justify feeling anything but vaguely nostalgic about it. Someone with a $20 subscription can mog anything I can build with the skills I picked up.

If someone hands you a silver bullet you'd be a fool to decline it and spend your time hand casting a crude piece of brass. If the difference between 95% and 99% means you know how to aim or oil the gun, that's the world we live in.

Building a good RAG pipeline or prompt optimization or LLM consensus is dumb stuff that produces a better result than anything I could do from my 2010 ML/AI textbooks. I don't lack the knowledge or capacity to compete, I lack the compute.

That's the job now for 99% of companies.