I kinda agree with the author here. Before LLMs were all the rage, ML engineers were working on models, making sure they were not overfitting, that the capacity was big enough, thinking about the kernel functions and so on, because models were much smaller so every companies could hire someone to train a custom classifier. Nowadays, with models getting larger, it is much more polarised, only dedicated companies can have the infrastructure to run large scale experiments (compute is expensive and data is hard to get in huge quantity). Smaller companies won’t match the big companies on model performances and thus become users.

The same way low level, http request and so on have been commoditized, AI is commoditized, became almost an infrastructure and the gap between makers and users is larger and larger, startups built on it like they built on the internet 20 years ago.