Sure there will be corrective behaviour in the market, and the better product with more outreach, better experience will win over suboptimal products with overlapping offerings, but does that mean that the current generative AI momentum is hollow or there is a sticky use case behind the promises? And if so, in your opinion, how overstated is the Total Addressable Market compared to what’s claimed by an aggregate of startups across the VC space?
I would like to know the cost of enabling this type of selective specialization of the models.
If it’s particularly not intensive, I wouldn’t be surprised if model architecture moves towards self-specialization or topic-selection with some effective function calling e.g. model used for a while -> automatically call model specializer after a few queries on the topic -> now use the newly returned specialized LLM
I wonder at what magnitude this could improve model efficacy
Iterative leaps of open-source models becoming better are huge examples that companies competing on LLM model layer have an ephemeral moat.
Serious question: assuming this is true, if an incumbent-challenger like OpenAI wants to win, how do they effectively compete against current services such as Meta and Google product offerings which can be AI enhanced in a snap?
“Needs tool usage” and “found the answer” blocks in your infra, how are these decisions made?
Looking at the demo, it takes a little time to return results, from the search, vector storage and vector db retrieval, which step takes the most time?