Well I don't want to keep jumping between different AI models as the things progress and companies do one up on each other, I wonder if most people would shift to aggregated wrappers that create a pipeline and allow easily switch-ability between models like LangChain does, but targeted to consumers instead.

designer alchemy | everything is experimental |
a decade+ of consumer products | farcaster godview: /intelligent | self exploration: /tanishq

100%

I'd extend this even further. perhaps the knowledge base is really static/global & outcomes are just recombinant of non-inferential elements, so wrappers also bargain for lower cost by publicly aggregating as many instances of recombination, not internalizing into the models they chain together for their service.

𝖒𝖆𝖓𝖉𝖆𝖙𝖚𝖒 𝖆𝖉 𝖆𝖘𝖙𝖗𝖆

I assume closed models pivot from data like common crawl imminently, too much blowback from creatives & small & open LLMs already ate that moat, unlike synthetic.

imho the "polymath generalist" vibe w/ service like GPT is a red herring. the stochastic element just needs to reflect, plan, & reason from external data.

That makes even more sense! It won’t work in closed models tho

But at the same time, aggregation on multiple open models might just work?