(^me working on my clickbait titles)
Claude (and ChatGPT) has gotten pretty amazing at conducting long-form tasks. But it still lacks a long-term, shared-across-the-entire-team memory, or what we used to call a "database." For example, I am doing research on X industry, asking Claude 10 questions in succession. Some of the links I click through and read (and don't like -- too low quality); some of content I disagree with and mentally ignore; some of it is repetitive or off-topic.
Currently, there is no way to share the distillation of your thoughts. (Also - distilling your thoughts to just the essence is hard!) Claude (and other LLMs) don't make this easy. You can share a whole chat, but that is a huge pain to read through. You can Claude to give you a summary, but it sometimes doesn't capture things correctly. The only way -- at least, in my opinion -- is to transcribe the highlights by hand, and/or summarize it in your own words (i.e. old-fashioned reading and writing). I still subscribe to the belief that "writing is thinking," so the time taken to write is time well spent.
In investment-land, institutional memory takes the form of memos (or investment decks). However, few places take the time to write up all the info on deals they did *not* participate in -- the effort is too high. The big opportunity, then, is lowering the activation energy it takes to turn research (i.e. reading an article, cruising through Google, skimming LinkedIn, interrogating Claude) into saved, institutional, and trustworthy memory. I think the workflow model that will work best long-term is human-as-curator (and AI as secretary and first-pass analyst). The big challenge is designing a workflow and data structure that fits, and the institutional buy-in to think this way.
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