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.

The curse (and benefit) of the Epic culture

 Working at a place for 8 years -- through your 20s -- really shapes how you view the world, how you interact with people. I think about this more and more the further I get away from my time at Epic (I left in Jan 2023). There's some quirks I've noticed about myself as I venture into the outside world, double-edged swords. A few of the things I've been thinking about are below.

Speaking with certainty / humbleness

We're trained to speak with our hospital customers with certainty and knowledge; better for someone to trust that everything you're saying is accurate, even if it means half your answers are "I'll get back to you." In healthcare, this works amazingly well -- it's a cornerstone of building long-lasting trust. Works less well in the real world / the investing world, where you can't possibly know everything and are rewarded for having an opinion.

It leads to a natural culture of humbleness (especially on the TS team) -- you're generally aware of your limits, and you constantly have to reach out to other people for help and expertise. 

Low/no sales

Our long-term technical support (TS) team has almost no sales that we need to do -- no upsells, no selling new products. If we ever do get to that conversation (say, of adding on a new module), we kick the demo and contracting to our implementation team. I believe it's an excellent model of support: we could just focus on fixing problems as best we could, and never had to worry about billing or budgets or upsells. 

I realize now it's a weakness I have now -- that sales muscle isn't there (for better and for worse!). For example: in the investment memos I've presented, I've focused on the facts of the investment, treating it like a puzzle to solve for us to decide on. Others do much better at "selling" their companies -- again for better and for worse. 

Replaceability

Part of the Epic culture -- for better or for worse -- is that everyone is replaceable. My cynical take: the genesis culture of this is that turnover is/was high, so you need to ensure that if someone leaves, you can replace them. This works well when you have to travel to a customer site or go on vacation -- you can have real back-ups to replace you. A lot of energy thus goes into ensuring that other people can easily know what you're working on, into educating others on niche areas of the software, on building redundancy. In some investment firms (and in some governance structures), this replaceability -- a focus on process, on sharing -- is not a focus. 

Deference

At Epic, we supported the hospital IT's team who supported end users (doctors, pharmacists, etc.) Thus, as Epic staff, my goal was always to make the end users trust the hospital IT team -- and ideally, never know that I existed (unless I came onsite). I would go out of my way to ensure that the hospital IT team looked like the heroes instead of me -- good for them, good for me. Same with newer team members: the quicker customers trusted the newbie, the quicker I could roll off; feeding the newbie answers was a win-win strategy. However good this may be for the org, the "leading from behind" strategy is not visible enough, especially when switching careers. It's a hard skill to unlearn.

"Build it yourself" mentality

Epic famously does not acquire; any tool you wanted, you had to build yourself. I feel the same way now -- I'd rather build a tool that works just how I want it than try to find a pre-existing software that does 80% and locks me in. A blessing and a curse.

High product-building capability

I've spent over 200 days onsite, which taught me how to think about designing a product to address real customer needs -- noticing small pain points, asking questions to understand larger workflows, figuring out which issues were root causes and which were a symptom of another larger problem. This is unanimously a good thing -- I like to think of it as the original Forward Deployment Engineer popularized by Palantir -- but it is devilishly difficult to put on a resume. Talking with another ex-Palantir engineer, it's a rare, subtle skill, but one that is very hard to boast about or verify (save being an ex-Palantir FDE).