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. 

VC Notes: Part ?

 Recently got looped into a conversation about a start-up in a little bit of trouble. Another fund (the lead) was spending a lot of time with it. Question came up: has the fund had any big exits yet? (Do they need this one to do well?)

Never thought much about this -- that a fund really really would like a few big early exits (because that helps raise the following funds, and a variety of other reasons). Incentives can get misaligned with start-up founders (or the success of the company). Weird behavioral quirk you don't think of if you're not at the table.

AI: a race to turn probabilistic into determinstic

A newer way that I've thought about the phase of AI and LLMs that we're in: it's a race to turn probabilistic/stochastic ("random") processes into deterministic ("not random") ones. 

• Easy example: adding two numbers together should use a calculator, not an LLM. Bad use case of AI.
• The company tracking suite I'm building out is built using the help of AI, but ultimately it's to build a strong database in the format I want, so that I can store companies, market maps, data, thoughts, etc. for the long-term. In my opinion, a good use of AI: turning a noisy, messy process (AI-driven coding) into something reliable (a deterministic database)
• LLM/agentic loops are all trying to build processes to reduce the error rate to close-enough-to-zero (e.g. double-checking/validation agents) or putting humans in the loop to bring it to "zero." The challenge is that you can reduce the probability of error close to zero, but can never quite hit it. I see this in the "document processing" realm -- we hope that the frontier models can read the PDF with 100% accuracy ... but for high stakes scenarios, would we trust it?

The art of investing == the art of cooking?

 If you only used recipes for cooking, do you really know how to cook? I say this as someone who seldom follows a recipe closely (which is why I don't bake), but does following recipes make you a good cook? I'd argue no -- it's all the smaller things: getting the pan to the right temperature, knowing when to turn the heat back down, sensing when to put keep things cooking for a few extra minutes, knowing what to do if things get a tiny bit burnt, adjust to different pans and stoves, etc. Yes, you can cook food by only following a recipe, but good cooks are able to both (a) fill in the gaps where the recipe is not 100% prescriptive and (b) adapt if things don't go 100% according to plan. 

I keep thinking about this through the lens of investment frameworks. On one hand, investors (and more generally, most people?) do a bad job of formalizing their thinking into easy-to-digest checklists. Investment frameworks push people to standardize tribal knowledge. (And, if you're a GP looking to raise from institutional money, you need to have some well-defined process, and a framework -- even if light -- accomplishes this.) On the other hand, a purely framework-driven investor would likely miss things. 

Whether frameworks alone can drive investment decisions is a core question in AI-driven investment diligence. As an investor, I believe most investors would say "no, there's some human element to it." It's hard to say exactly what they'd miss -- just in the same way you can't teach all the norms of cooking all at once. Sure, you could try to list everything out, but (a) doing that is very hard, (b) it creates an unruly-sized checklist that nobody would follow, and (c) new things always come up. Frustrating to me as a newer investor, but very understandable as someone who cooks a lot. Exceling at the earlier stages require a mix of framework, good coaches, and reps.

How I'm thinking about VC now

I think a lot about where a VC firm like CT Innovations plays in the larger venture ecosystem, as well as what types of deals are in our "sweet spot." The way I think about it now (which is sure to evolve over time) is that we are an agglomeration of many VC strategies all lumped under one umbrella. I'd like to think that most venture firms have one or two "founder archetypes" in its sweet spot -- but given our mandate of investing in nearly anything that comes out of the state, we have no singular archetype. The way I've come to think about it is a sort of mental accounting: many deals fit our portfolio, but some because with an economic/catalytic tilt and others because they are ones larger players (a16z, etc.) would be interested in. 

From what I've seen thus far, some of the archetypes I've seen and diligenced:

• Industry specialist, great founder, great business, "small" TAM -- Right now, I think this is our sweet spot -- a great founder (often repeat founder) building great businesses in a $1-5B market, too small for other venture investors to get excited (i.e. no "homerun," unicorn upside) but a potential for a great return. 
• High-growth potential unicorns -- Connecticut simply gets fewer of these high-growth, "hype" companies where valuations step up 2-5x between rounds. These are the a16z/Sequoia/Thrive archetypes -- super high growth potential, high valuations compared to revenue, "true" venture deals that can go 30x or 0x. 
• Catalytic capital -- These deals have an "impact" angle to them. By no means are these deals meant to be concessionary, though. CT has a host of amazing talent -- Yale/UConn professors across science and tech, as well as pharma and insurance expertise -- that have the potential to become great companies. We can be one of the first checks in on these deals. 
• Economic development -- Less often, we make deals in part for local economic development. 

At a higher level, the way I'm thinking about it now on the tech side is we invest in (a) good, solid venture-able businesses, (b) a few moonshots, and (c) a few true pre-seed companies. A really fun mix of companies -- albeit a bit disjointed for a "typical" VC firm -- whose strategy is driven by the natural restrictions of CT Innovation's mandate.