AI to write investment memos?

 The more I've spent time with AI tools and investment memos, the more I believe that AI should never be the primary author of investment memos. Some thoughts I've had in my head:

• Having "AI write the first draft" turns the role of the investor from primary author to editor. For anyone who's edited papers or done code review, these are two drastically different roles. An analyst's job is to take a bunch of noise/data and capture the essence of them in as few words as possible. An editor's job is to gut check another person's work -- tone, diction, facts, etc. -- but seldom to digest the ideas completely. Editing is very hard work (arguably harder than being a primary author!), AND being an editor dilutes your brain with another person's key ideas. Your ideas become responses, not original ideas.
• Through this lens: one counterpoint I've heard is "have AI write the first draft then just remove what you don't need." Just as in editing, removing AI text is actually really hard! All of the stuff is usually valid, but how can you remove something that is useful but not important? I've seen this in memos recently -- 50+ pages, but missing some fundamentals.

KPIs for state-backed venture in tech

If you are a place-based venture firm, how do you measure success? I think this is actually one of the trickiest conundrums out there. As the saying goes (and I may be butchering it), you are what you measure. 

The challenge in tech is that (a) tech workers are well-paid but generally used to living wherever they want, (b) tech companies generally are more human capital light than other industries, and (c) many tech companies fail. So: if you measure by "how many jobs did we create/retain in the state": there is an incentive not to back the high-risk, high-reward moonshots (because if they fail, you lose jobs!), but instead to back good businesses with capped upsides. (There is also some incentive to fund start-ups that maybe shouldn't be funded!) If you measure by "how many start-ups did we create," you run into similar issues -- you can have many start-ups, but most/all fizzle out. Perhaps you measure by leverage -- how many external VC dollars went into our companies -- but then that incentivizes competing in the latest stage companies. 

Ultimately, the question is "what does a tech start-up ecosystem look like, and how do you know it's doing well." This factors in a lot more than just VC money alone can provide -- e.g. the city itself has to be a place people want to move to -- and so the KPIs are challenging. 

The human as the tastemaker

 In my view of AI: (a) the AI is able to collect a near infinite amount of data on almost any topic imaginable, but (b) the human is still the filter that determines what is relevant. Yes, AI can and will get better and better at this, but "taste" will always be a human characteristic. 

My current excitement is around making the market map building process better. Some of this is rote (e.g. finding all the M&A transactions, finding all the companies in the market), but some of it requires a little finesse (e.g. which companies actually get included in the set? how is the market segmented? who are the incumbents?) A lot of this can be automated by LLMs ... but I do think there's still a surprising amount of judgement involved. 

One example: M&A analysts get paid big bucks to determine what companies should go into a comp set. Another example from the software world: a lot of coding is getting automated, but things like database architecture require a deeper level of thinking that AI can't do alone. This is because building a database (and many other human tasks) have no perfect solution and requires weighing trade-offs -- so they inherently cannot be completely AI'ed away. It's a little like saying that AI can, with 100% certainty, pick what you should have for dinner tonight or pick what your child's name should be. (That being said, some decisions that might've felt consequential a few years ago might now just be automated/abstracted away e.g. by using an all-in-one provider like Supabase.) 

Overall, though, it's becoming clearer that all the AI efforts now will eventually lead to a larger emphasis on humans as the tastemakers. They are the ones who will make sense of all the inputs, who will give a lens on how to interpret it all, and who people will ultimately trust. 

"Assistive AI": or, lowering the barrier to being better organized

There are many, many great use cases for AI (and many mediocre ones), but I'm coming to realize that one of the BIG use cases of AI is a technology that lowers the barrier to doing what you should've been doing already. 

In my case, it's getting all the stuff I read or see into nicely organized notes. For example, today when I was cruising on LinkedIn, I came across this post which included the image below:

In a dream world, I would have a file on my computer that was organized, and I would transcribe this info into it. The benefits would be immense: (a) I could recall specific pieces of knowledge across time, and (b) I could easily share this info with someone else without giving them a deluge of links to read. But, of course, few people are this anal in their treatment of data. The challenge is that you can read this one piece of info, but you may never actually need it, or you might just need the gestalt of it in your day-to-day work. Or if you need the same info, you can likely just ask Google or ChatGPT. 

Here is the test of what I've built this far: can it handle it? Can I just give it a prompt and have it find the companies, create them, and load the transactions?

It struggled to find a company's website, so it prompted me for it (great job, agent!), and successfully uploaded it:

Hot damn, it worked -- it uploaded all of these transactions to the "Quantum" market map that I created:

(Note: I've spent a lot of time building all these capabilities and building up the agent to get to this point -- and have hit a lot of bugs -- so exciting to see this work first time.)

This feels so small but so significant at the same time. If I can upload this info via chat, it'll be (relatively) easy to set it up via a WhatsApp chat, or routed to an email. Imagine -- I can just forward this LinkedIn link to an email, and let agents handle the rest of the information organization. Very small, but information -- and organized information -- compounds. 

If you give a mouse a cookie ... (CRM progress)

I read a recent post about how Draper's investment team used to run on 8 spreadsheets but now runs on a patchwork of tools stitched together. (For example: one Airtable page per company.) I'm sure this solution functionally works well, but it's made me realize that I think like an "enterprise engineer" -- almost the complete opposite of a start-up engineer. At Epic, I've spent nearly a decade thinking about "if we add this single line of code in the wrong way..." or "if we make this data structure look like XYZ," then it's not optimal long-term. In other words, the enterprise engineer's belief is that there are many nearsighted, wrong ways to solve a problem, but only a handful of good long-term solutions. This is in stark contrast to the "move fast and break things" ethos of Silicon Valley of yore, and to the "just vibe code it" ethos of today. 

I say this in part to justify my slow movement on building my CRM tool. The tool blossomed into much more -- I'm adding in market maps along with companies (public and private), investors, etc. One thing I am spending the most time with is the data structure, or how all the different entities link to one another. To make this more concrete: yes, you can have one Airtable page per company. But then how do you link an investor to it, especially when you consider that an investor can invest multiple times in the same start-up, out of different funds? Getting this data structure right is a pain in the ass, especially if you consider that the data is sparse (if you have a cap table, you have all the info, but most of the time you just have snippets of info -- and the data structure should work for either!)

Along the way, I also realized that a good tool should allow you to centralize and share preliminary information easily, something that investment offices I don't believe do well today. For example, if I review a data room or do market research, who knows what I've searched for? (Even I forget what I found, unless I take the time to write it out!) The only way to know is to ask me, and hope that I remember or have the time (and that I remember correctly). I think this is a hidden crux of a great tool -- make organizing my knowledge easy, and thus make sharing it easy.

So this has been the "if you give a mouse a cookie" scenario for my CRM tool. It's slowly blossoming into a more all-encompassing "system of record" tool that has all the info. The other thing I've always been interested in -- and have shaped up the "marketing" of it in my head: the tool should allow you to layer public info (e.g. public articles) with private info (e.g. private phone calls, confidential pitch decks, etc.) to create a tool that can TRULY manage a mosaic of information. This requires tighter auditing and control of what data came from where -- but if successful, can be a really cool tool that mirrors how people actually think. But! the hard part is the data structure -- and knowing that the hard part is the data structure! -- something enterprise-thinking me is stuck thinking about. So, hence the continued slow roll.

Claude has a memory problem

 (^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.

Source / Pick / Win

 A little bit of introspection on being good at source / pick / win ... 

1. Sourcing is both "becoming commoditized" ... but also isn't. It *is* easier than ever to build AI tools to help list out all the companies in the known universe. It is *significantly* harder to get founders to respond to cold emails, without some sort of brand name or reputation (or in the worst case, a desperate need for cash). 

Nothing will beat having boots on the ground, spending quality time face-to-face with a founder, building rapport and telling your story. I've found a few start-ups that (a) I would've otherwise never found and (b) who otherwise wouldn't have responded to my emails. 

Sourcing is a learned skill, an extremely process-oriented step, and a very human process. It's one that I've been slow to uptake, but getting better at.

2. Picking is the due diligence. AI will be able to help accelerate diligence, but I believe good investment taste will be hard to find. The apprenticeship part of the business, one I continue to cut my teeth on (writing memos by hand, assisted with AI searches).

3. Winning is the very human part of it that will forever be human. Who do you want to be invested in you long-term? Who brings value outside of capital? Who do you trust? Who do you like? It seems crazy that you would take money from person A than person B just because you like being around person A and find person B annoying ... but that is a very real part of the process. 

This is the piece that spending 8+ years at Epic learning to build trust come in handy.