I strongly believe this premise in the article is correct - we will see a lot of tiny, hyper specialized models for individual tasks, and perhaps that will converge with an orchestration layer for a generalized intelligence that controls these specialized tiny models, that will be quite capable. I don't foresee AGI arising out training bigger LLMs (Though investors won't realise that for a while yet). It's actually h…
General purpose models are always more robust and generally better than smaller narrower models. My bet is that compute will catch up and any “small” model will still be generally capable, just smaller than sota, rather than intentionally narrow. The exception would be for very well defined tasks where the data distribution never varies, but these are rare and don’t really need “AI” anyway when they do exist.
"Can run fully on local hardware without an internet connection and doesn't come with a monthly subscription" makes local models "better" in a way a LLM can never match.