VC Notes (Part 1 of ?)

Random quick notes about VC that I've come across this week. Someday I'll go back and aggregate them.

As a VC (or an investor writ-large)

• Finding the ripcord in a deal - If a deal has a bunch of yellow flags but no damning red flag: you need to be able to find the "ripcord" in the deal, the one thing you can point to (in front of the CIO, in front of the investment committee) that's makes the deal most unattractive in the clearest, most explainable way. E.g. "not a good fit for our portfolio for XYZ reasons" or "untenable valuation" may be better than "well it's a collection of yellow flags"

For evaluating VCs

• Ability to squeak into closed deals = green flag. Shows that you add true value to the start-up, and they're willing to let you in for good reasons. 
• High-value VC qualities: gravitas, technical expertise, ability to sell next round of capital. Succinct way of putting what can make a VC firm great. From Ed Grefenstette, Venture Market Update - Capital Allocators with Ted Seides
• (Also authenticity, ability to admit mistakes, the trite "intellectual honesty")
• Massive venture firms' portfolio construction - from podcast above. Huge venture firms (like a16z) have to invest a little in hundreds of companies at early stage to buy the "option" for future rounds. Solo GPs are investing in the earliest stages and looking to build alongside founders (and respond to late night texts) -- they're more invested in the company's success. Good to think about both from allocator and start-up angle.

Portfolio Construction

• Mixing huge and emerging VCs - from the same podcast above. One strategy for venture allocation is to get one or two marquee names to build stability in the venture book (for ~30% of portfolio), then a handful of contrarian, orthogonal emerging managers / solo GPs to give diversification. Gives more credence why it's hard to be in the middle right now

Tooling Updates (and Exploring Antigravity!)

Antigravity continues to impress me. I've been excited by how much I've been able to get done, that I've continued to push the system to make updates. The updates I've made:

• A chorus of LLMs: one way to "fix" the deficiencies of LLMs is to query multiple LLM providers (e.g. OpenAI, Gemini, Anthropic), then have another LLM summarize all the answers. The thinking goes: one of them might be hallucinate, but the chances of all of them hallucinating in the same wrong direction is low. (It's similar to how blockchain consensus works!) I created a tool that queries a few LLMs and then uses another to summarize them all -- you can play with this tool with any question you might have.
• PDF Parser: I ultimately want to tackle the problem of processing and summarizing a bunch of files sitting in a folder. One use case: "read" through a term sheet and pull out all the key terms. (I would love to build up to "find me the bad/anomalous parts of the contract" but for now "get me the terms" would suffice!) I built this out, using OpenRouter to read the PDFs (using mistral-ocr, which costs $2/1000 pages). I then added in an LLM call that says, "if this looks like a term sheet, then pull out XYZ fields." It's just one use case for now, but it seems to work fairly well. (For testing, I used the Buffer term sheet found here.)
• Competitors and public comps: I applied the "chorus of LLMs" to help find me competitors and public comps. My initial thought was to build this agent like I would run this: run a Google query, read the pages, then make sense of it. I tried using LLMs by themselves, hoping that throwing 5 different LLMs would provide good enough coverage across the internet. It's worked fairly well!

Lots of other doodads added on -- ability to download the report, etc. And, not too bad for a few hours of work!

So far, the app is a ChatGPT (plus other LLMs) wrapper, but the value this app is adding above ChatGPT alone:

• The chorus architecture adds a legitimate benefit (and one that would be hard to do without this app!)
• The agent adds a lot of extra context to the LLM query -- e.g. "don't hallucinate, return sources, etc." This is built into the series of LLM queries automatically. (In other words, great "prompt engineering"!)
• Configurability -- I can tweak this to deliver the information to me however I want it! With the death of SaaS, this is the dream -- being able to build tools exactly as you want them!

Where this app will hit walls:

• Analyzing tables/graphs -- say we upload a board deck. It will struggle with tables, figures, and graphs! (This is a known weakness of LLMs -- as they say, a picture is worth a thousand words.) There may be some tricks with tables and graphs, and that'll get me more into the weeds of LLM/AI capabilities. (A similar need: trying to figure out if a term sheet has been signed or not, which can be important if we have multiple drafts in a folder!)
• Processing very long documents -- dealing with very long documents (e.g. a 10-K) is challenging. This is what things like RAG were built for -- but that adds a-whole-nother dimension of complexity.
• Finding good data -- this is a forever challenge! For example, I typically get my private market data from Pitchbook now, but I couldn't get my ChatGPT API to integrate with Pitchbook. Crunchbase (via the chorus of LLMs) seemed to get pretty good information, but having good, reliable data will continue to be a need throughout the AI era.

Public Market Comps Tool

Motivation:

• There's all this talk about SaaS being dead because even the least tech-savvy can build anything with AI!
• Coding-proficient people (like me) need to learn how to integrate AI effectively -- and ideally be able to build live, working products quickly
• Somehow, even with all of the AI and software in the world, I can't seem to find tools that work exactly like I want them to!

Product:

• One important step of VC is public market comps, which helps give a good estimate of what the company could be worth in X years (e.g. using a simple EV/revenue multiple)
• We have an AlphaSense license, but it (a) somehow isn't able to pull this financial data well (it struggled to get US-based public market caps) and (b) AlphaSense really, really, really wants to lock you into their product (which I hate!) by making all links point to an alphasense.com URL

Instead of trying to build this from scratch (again), I threw it to Google Antigravity. My prompt:

i'd like your help creating a new project that finds public market comps for an input company, industry, or sub-industry. i think this should include a step for market scanning/filtering, and a step for pulling metrics like rev, EV/rev, market cap, company name, country/exchange, brief description. this should be built modularly. also use OpenRouter for LLM calls.

also build a simple streamlit frontend for this

A couple notes on this process:

• Being "good at AI" is slowly meaning "being able to clearly state everything you want, exactly how you want it." My prompt above probably gets a solid B-
• Being conversant in software development, however, is a huge plus! Knowing what APIs or frameworks are good (and why) is a very human, consider-the-tradeoffs skill, at least for now. For example, I like OpenRouter because it ensures the whole app is LLM-agnostic. I also like Streamlit because it is in Python and so simple (meaning: I don't have to go learn another language and framework). 
• This is also helpful to know how this "agent" works. Right now, it's a two-step process: (1) "scan" the market with an LLM prompt, then (2) pull the market data from a Yahoo Finance API (yfinance). 
• Should this be insufficient, a few ideas that I could use: (a) upgrade the single LLM call to aggregate the results across multiple, (b) use a market scanning API in addition (or instead of) the LLM call, (c) replace yfinance with a Bloomberg API, etc. etc. etc.
• I had to upgrade to Google AI Pro ($20/month) for Antigravity to finish the app (it ran out of credits!). How many credits the Pro upgrade comes with still remains a mystery. 

I created the app in about 1.5 hours, while watching the US/Slovakia men's hockey game. (Antigravity did most of the work.) It is imperfect and not to my exacting expectations, but (a) I recognize this is my fault in not being crystal clear, (b) it works well enough, and (c) it is completed. Easier to edit than write anew. 

Anyways, here's the link: https://market-c-fsjognovanewadxgz7o72g.streamlit.app/. If you happen to use this, don't incinerate my money by using too many tokens. (Fortunately, it looks like right now, about 100 runs of this would cost less than 10 cents.)

The new ESG: attention addiction?

Classic ESG focuses on companies who are polluting the environment (think industrials, coal plants, etc.), with the idea of either (a) filtering out heavy polluters or (b) investing in renewables. It feels like decades of environmental pollution in the US -- and smog-filled cities, birth defects, etc. -- have slowly shifted the nationwide consensus to "minimize environmental harm." 

A long aside: Never mind a whole separate conversation of whether it's better to simply divest from the biggest polluters or engage with them; Yale professor Kelly Shue argues if we actually care about reducing absolute emissions, it's counterproductive to choke off funding to these "brown" firms. It reminds me of the wider political discourse -- is it better to cut every Trump voter out of your life or try to actually engage with a few of them? The former feels better short-term (to the individual), but the latter is better for the long-term health of the nation.

Some of the biggest public companies of the 1980s -- Exxon, GE, Chevron, DuPont, GM, Ford, Philip Morris, etc. -- were in oil and industrials. It took a few decades for the political and investing appetite to catch up to these companies. We now have a whole industry -- frameworks, consultants, you name it -- around mitigating environmental "risk" in the portfolio.

The giants of the 2010/2020s weigh heavily tech -- Apple, Microsoft, Google/Youtube, Facebook, Amazon. We already know there's a huge problem with social media addiction, and Australia has recently proposed banning social media for kids under 16. But ... I haven't seen or read much at all about "anti-virality" ESG investing. The "environmental detritus" of social media addiction is hard to see and hard to measure. But yet all the largest social media sites (be it TikTok, Instagram, Reddit, Facebook, or any others) are incentivized to keep you addicted to the platform, because more average time spent on the app means more ads served, which means more revenue. 

In my view, the problem is not just in the addiction itself but in the example it sets to start-ups. If all the biggest players are competing on keeping your attention, how can a new start-up break in without trying to make something flashier, more attractive, more addictive? Does social media become a race to the bottom? And more importantly, if you are an investor who cares about investing responsibly (i.e. more than just what's in an ESG framework), can you feel good about investing in these types of start-ups or the VCs who back them?

Ten years from now, I think we will look back and realize that a lot of this tech -- which are often great financial investments! -- is socially harmful, and I hope we will have the language, metrics, and frameworks to justify divestment or substantial engagement. I think the question of whether future investors should not invest in start-ups because of their harmful effects on society (social media addiction, but also prediction aka gambling markets, etc.) will always be a contentious discussion, but I think recognizing it as the "new ESG frontier" will push us to ask the right questions and collect the right metrics, especially as tech plays an increasingly outsized impact on society as a whole.