I've been writing quite a few memos in the past few weeks, poring through data rooms with tens or hundreds of documents. I think I'm in a unique spot because (a) I love poring through data rooms (perhaps spending too much time on it), (b) it feels like many parts of the process are slower than they should be, and (c) I know that some of the pieces could be done by LLMs.
Some concrete examples from VC: company background, IP, founder background, cap table, and past funding rounds are all pretty time-consuming, as well as a little tedious/rote to write, low on the time-to-value scale. (I'm more often than not annoyed that I have to dig through legal docs to get the investors, amounts, terms, etc. from prior rounds -- can't AI do that yet!?)
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I've spent more time than the average person thinking, "If I were to write a program to do my job, how would I have it do my work?" (This pursuit of abstraction -- from real world to code -- is what software development is all about.) It turns out, when I'm writing an investment memo, I have a generally set rubric of questions I'm trying to answer. The largest, most institutional players (like NEPC, $1.7T AUA) have frameworks and checklists to make sure that you're hitting all the boxes. For me, these frameworks feel a bit boring, but it's hard to touch on exactly why; perhaps in my imagination, investment research is a creative endeavor that can't be done by checklist alone. Or perhaps, it's that nobody is excited about process -- which in this case can mean a 200-line checklist.
But ... this framework approach approximates how humans think. I have a list of sections that I need to fill in, and each successive slide deck or Excel or article that I read adds some piece of evidence to one or many of these sections. To make it less abstract: let's say I'm researching low earth orbit satellites, and my goal is to write an industry report. My sub-sections might include: market segmentation, market size, growth drivers, demand drivers, regulatory/policy, and market risks. I might read a few McKinsey market reports that fill in some sections, then a policy report, talk with an expert, then review a few pitch decks for LEO start-ups. With each new document, I gather information for each bucket, then after I've reviewed everything, I synthesize it together, weighing the most trusted resources more highly. The process never happens this clearly, but I believe it's replicable steps that most people follow.
This same general framework -- read documents, extract relevant data, organize data into framework, resolve data conflicts, and synthesize a final analysis -- are what most knowledge work seem to follow. Breaking it down this way gives us a chance of replicating the steps with AI.
Why are LLMs not good at this out of the box? I'd argue that LLMs are general-purpose tools, built so that you can ask any question you want. Tools like Google's NotebookLM are excellent at this -- feed it documents, and ask it anything. Investment research -- and most knowledge work -- fundamentally has an end goal or question, and the framework/process that you use to get to the answer is "tribal knowledge," typically not well-documented and slightly different firm-to-firm. This "tribal knowledge" is what the LLM lacks out-of-the-box, and what I think can be codified for success.
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The knowledge extraction process then becomes:
1. Read documents,
2. Extract relevant data points,
3. Organize data into framework,
4. Resolve data conflicts, and
5. Synthesize a final analysis.
Each step has its own technical challenges. For example, finding relevant documents can be difficult, as is reading them (reading slide decks, especially graphs and charts, is not easy for LLMs today). I believe the most valuable piece, though, is creating a knowledge framework, documenting that "tribal knowledge." The more we use AI, the more we'll realize that for most companies, value will still come from the boring things like process, documentation, and frameworks.
