Find the concept of vector encoding beautiful. In its fullness, aren't embeddings the "thought" which can be expressed in many different words/sentences? Better the model, crisper the thought.
Think infra cos on top of LLMs can go either of two ways - make LLMs smarter/efficient; make LLMs more useful. The former is going to get harder to make a business case out of - eg LLMs just incorporate CoT reasoning or handling parsing. Way more excited about applications.
Find the whole "chatGPT wrapper" debate somewhat dumb. As it has been since the beginning of technology, what wins is likely the ability to uniquely solve customer needs at scale, and becoming the go-to for those needs.
Surprised by how little use case specific data one needs to transform LMs from good to great! Bloomberg GPT used just 0.7% of their prop. data in training set. How much of a moat is legacy data really or incumbents in the age of AI?
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