This post suggest to main rules:
Rule 1: Never mix workloads
Rule 2: Apply guardrails
Applying those two rules help to decompose a monolith into smaller components to get the benefits of monolith and avoid the problem with microservices.
I think that many people could be interested in sharing cost if they can obtain a LLaMA based finetuned model better than GPT4 in their preferred language. So there is an opportunity for someone creating a startup just for that.
Spoiler: the fragment "As an AI language model" reveals that the comment about the product or the book is from a LLM and not a person in a context in which it is supposed a person is giving a review to a product.
An hypothesis is that in the latent space there is something like a branching factor that can be used in context learning to select the main tree for others layers. So a LLM is able to have the knowledge of many specialized smallers LLM and select the appropriate one by way of giving values to the branching factor. The branching factor could be some combination of attention layers operating in the latent space of previous layers.
A model of publishing in which the authors of related work are compensated (citations, appearing as coauthors,...) would allow new approaches and ideas to be disseminated easily. The main factor here is about novelty and possible applications of the new approaches.
Applying those two rules help to decompose a monolith into smaller components to get the benefits of monolith and avoid the problem with microservices.