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).

VC Notes (Part 3)

"In investing, you're rewarded for having a point of view." 

Heard this recently, and it's a mindset that's been hard for me (and other STEM majors?) to adopt. In my prior roles, I was rewarded for being knowledgeable, not saying wrong things, and couching my uncertainty. In investing, the best speak with knowledge and conviction, but it seems the next best thing is to speak with conviction but not necessarily knowledge. To sound impressive -- or to have a view, even if ill-informed -- can take you further. Investors don't like to hear "I haven't done my research on that topic"; it seems they'd sometimes rather force you to glom onto a position. Obviously, there's a lot more nuance than that, but I'm slowly learning how to thread the needle of speaking like an investor. 

Gut investing

I wrote about this a little previously, but it also feels like there's some flavor of machismo in some corners of venture where people "trust their gut" and increasingly "learn to trust it more." I've heard it at least a few times, and I think it's something that uniquely exists in venture as something that people are proud of? You never hear a fundamental equities investor talk about their gut as the sole driver of decisions. Anyways, I hear it a lot, I agree with "gut" as a data point, but I think it behooves everyone to tease apart what "gut" means (founder charisma? founder anti-charisma? etc.)

What it takes to build a novel software (e.g. computer science research) is drastically different than what it takes to distribute a novel software

Perhaps it's embarrassing it's taken this long to fully comprehend, but the cool stuff that computer scientists are doing seldom translates to a successful software company, especially in age of AI. Cool algorithms or cool technology usually don't sell; "dumb" software with great distribution are what matter. Most of the MAG7 today are fundamentally "dumb" software with great distribution (save Google perhaps). I've become increasingly cynical about the software technology itself being any sort of differentiator; it's the people and sales channels that make a tech product pop.

Same goes deep tech, say in climate. Great lab work (i.e. science research) needs to be coupled with even better engineering to have any chance of survival. Sometimes it's not the best core science, but the one that can scale up better that wins. 

Syndicate vs. the more modern sole lead

Historically, VC investors looked for syndicates of other investors to share risk. The largest VC firms now don't need -- or want! -- syndicates; there's too much money that needs to be deployed. Instead, it feels like it's sometimes better to elbow others out of rounds. Almost has a PE flavor to it. 

Authentic differentiation

This is probably more through the lens of an allocator, looking at VC funds. (We recently had a day where we saw a few of our portfolio VC managers.) The VC funds that resonate the most are the ones where the point of differentiation feels authentic -- ties back to the person's past career, past predilections, or a difference in the way the GP thinks that manifests itself as strategy. Hard to describe without naming names, but something I think about more and more as I build my "brand."

Progress on CRM

I wrote a few weeks ago that I had started to build a CRM tool. I've made pretty good progress, but I've realized that it's less about the CRM itself and more about building the tools that I want to see, with AI/document processing at its core. I've come to remember that I'm opinionated on making the tools as easy to use as possible, but also insistent on clean audit trails and ease of troubleshooting, as well as clean data structures and modular code. 

What's done so far:

• Core data models -- I've built out the core data models and data tables into Supabase. Users can manually build companies, investment firms, and people into the system. 
• Document uploads -- Users can upload documents -- which can then create new companies, etc. in the system and link them automatically. I think this is the holy grail -- just upload documents and have it update your internal notes! 
• Audit trail -- I have a good audit trail mechanism built out, telling you how a company was created (manually vs. automatically), as well if individual fields were updated. May seem like overkill now, but I think a must longer-term (and something we can build on). Good foundations!
• Authentication -- started but not quite working. Google OAuth login enabled through Supabase. Something not 100% working
• Designed to be plug-and-play -- I've designed this to be plug-and-play for any investment firm. All you'd need to do is plug in a few of the company's own things (e.g. Supabase information, OpenRouter API key, and a few more things), and you'd be off and running! (This could also be deployed in a Docker and shipped to multiple customers!)
• Data pipelines -- Much of the data gets piped in from somewhere, on some sort of cadence. Examples: (1) some VCs look at a16z's website weekly to add to their sourcing queue, (2) CT Innovations could pull companies from the CT business registry weekly, (3) newsletters delivered to my email might contain start-ups I should check out. I have a simple data structure to schedule these pipelines, which will need to be built on with more use cases.

What's next:

• Vision of email-driven + automated workflows -- From what I've seen, investment workflows and communications are driven largely by email. Therefore, a good CRM should integrate into existing workflows, while having the capacity to augment them. What this looks like: the system tracks email flow, downloads documents (and automatically uploads them to CRM), and the CRM interprets where we are along in the process. A concrete example: if we're doing diligence on a fund, the CRM tool should (1) automatically download the pitch deck and supporting docs, (2) interpret them, (3) interpret and import important dates (e.g. expected closing date or other funds circled), and (4) ensure we follow up (either with a rejection or a request for updates, if it's been a few months since last communication). The system should drive the investor to do this, without having to configure anything additional in the system. 
• More granular company/investor data -- Next up from the data model side will be adding in things like revenue (for a single company), funds (for investment firms), and other data (portco allocation, percent ownership, cash flows, etc.) The challenge is finding a good, flexible data structure that can account for all the pieces of info you might want to store discretely. 
• Showing provenance of data -- Data can come from multiple sources e.g. public/published articles, hearsay from other investors, and source documents from the company itself. The system should be smart enough to track the data provenance -- revenue numbers from the company themselves are much better than a guesstimate from a published article, which are much better than a rough estimate. This distinction is crucial for contextualizing the system data (and thus for downstream AI processing)
• Handling Excel and better text extraction -- Right now, the text extraction (i.e. PDF -> text) is pretty good but not bulletproof, which'll be important for trusting the numbers. Likewise, we don't handle Excel yet. (Excel documents are universally tricky for LLMs to handle.) Both are fixable but more technical problems; better to get a rough version 1 of the entire system before honing in on these challenging minutiae. 
• Integration with other vendors -- So far, I have integration with Google OAuth for login. Perhaps some integrations with other parts of the investment stack (e.g. CRMs, knowledge management tools, financial tools) to allow this tool to orchestrate everything together.

What I've learned/observed about AI and sustained human superiority:

• Humans still need to make decisions -- I can use AI to help me build the data models and SQL tables, but I (as the human) still need to make decisions on how simple or complex the data models are. LLMs often over-engineer, so it's still up to me to set a good foundation by building a simple but strong core data model. (These data structure tradeoffs are nearly impossible for AI to handle -- it doesn't know exactly what I want to build next, so it doesn't know what foundation to lay!)
• A silly example: if we had robots that could build a house, we would still need expert humans to help guide us through the trade-offs (e.g. how many bathrooms, what materials to use, cost vs. quality of material, which type of siding, etc. etc.). No difference in engineering code.
• AI lacks taste -- For some decisions, like on UI layout, the AI adds a bunch of crud which makes the app look/feel distasteful. I'm no designer, but I think on many decisions, I have taste that AI will never quite be able to capture. 
• Humans know when to quit (and where to be creative) -- My AI coding agent was dogged in trying to fix a particular issue -- it tried to brute force its way through the problem, without success. After about 20 minutes (and a few failed updates), I pushed it to try a different solution.  (Technical detail: I asked it to make a synchronous call asynchronous, and update/centralize code to make this work.) The new solution worked well. It's a case where a human (me) knew when to say: "This is taking longer than it should, and I have other good ideas on how to fix this that match my longer-term vision better." This is also a case where knowing how the underlying system works is crucial -- if this were vibe-coded, there'd be no way to know the root cause of the problem, or approaches to fixing it. 
• AI coding agents are great at execution -- AI coding tools are very good at writing code. The more prescriptive you can be, the better the execution.

Claude Co-Work vs. In-House Tools

Just a month or two ago, the tech world boasted about how many tokens it burned (i.e. how large its operating expense was). That party didn't last long. The consensus now is that for many jobs, hiring a person is easier/cheaper than hiring an AI agent. 

This has been my fear with agents all along: (1) what kind of agent truly needs to run automatically 24/7 and (2) how many tokens would this eat up needlessly. Side note: I got burned with cloud hosting on both Snowflake and Databricks for a personal project -- billed for cloud/GPU capacity that I wasn't using at all -- so I'm more sensitive than most to trusting tech companies with my credit card.

I've also consistently heard great things about Claude Co-Work. I've been tinkering with building my own tools for a while, and I had a moment of panic: is there any use in what I'm building, or am I reproducing things that Claude (and thousands of other smart developers) are already building?

ChatGPT helped drum up where the tradeoffs are. I put them below, as a reminder to myself that in-house tools -- ones that you know how they work, that link to the data you want, etc. -- have lasting value. Maybe not as a venture-backable company, but real time-saving workflow value. 

Anyways, here are the top dimensions where building your own software really shine over what Claude (or similar) offers. 

Dimension	Claude / Chat UI	Custom Pipeline
Time-to-value	Extremely fast	Slower
Repeatability	Weak/moderate	Strong
Flexibility	Limited	Full control
Structured data extraction	Okay	Excellent
Audit trails	Weak	Strong
Multi-stage workflows	Awkward	Natural
Integration with database	Limited	Native
Cost at scale	Can become expensive	Often cheaper at volume
Vendor lock-in	High	Low
Human-in-loop review	Limited	Fully customizable

Intuition vs Process

In the investing world, there's a tension between intuition and process. 

Some of the "greats" that I've met seem to eschew frameworks or process. The thinking goes: process makes your lazy -- you think better if you start from "first principles" every time. On the face of it, it feels like it should be correct; great investors are great because they've had to teach it to themselves from the ground up. What makes an investor great is their ability to think and diligence. 

It feels like some investment firms (especially smaller ones) are designed with the solo investor in mind. There is no firm standardized process, no standardized sourcing pipeline, no general training. To do so would constrain the investor, who needs to use their gut to make their decisions (so the thinking goes). A framework would stand in the way of intuition.

However ... when you assess funds as an LP, a lot of focus is on process: can we trust this manager to produce alpha, and do they have a replicable process? To assess this at larger investment consultants is the other end of the extreme. The investment framework is truly a process, a 200-plus-line Excel spreadsheet of investment criteria which is then synthesized into a memo. The intuition purists would say: yes, you've checked every box, but you've missed some je ne sais quoi about the investment, something not in your checklist that makes it stand out (or makes it fall apart). There are parallels to Atul Gawande's The Checklist Manifesto, where checklists improve surgery (and air flight safety) despite being initially despised by surgeons (and pilots, etc.)

All this to say: the investment firms that will be most successful with AI will be those that translate process into software-codified systems -- i.e. investment in AI will be all about process. One example: right now, as a firm, sourcing at some places feels spontaneous -- every investor has their own set of connections, resources, rules, etc. Some of this could/should be codified in agentic workflows; I've started to collect a list of "high quality" sources (e.g. a16z speedrun, etc.). Another example: we have a light investment memo template, but many questions are asked beyond what the template contains. These questions should ultimately be subsections of the template, which can then a yardstick by which to measure investment diligence. This means updating a core investment framework template, to ensure that that questions is answered every time. 

On one hand, I hate it -- filling out a 200-question survey (and adding questions to it) seems to take the joy out of reading, learning, and investing. On the other hand, it's a bit embarrassing to miss certain pieces of diligence over and over again. And as a newer investor, it's a bit bewildering to be given tens of documents to read, without a rough mental framework in mind. 

Ultimately, I'm coming to believe that "investment experience" might just mean "I've built a really solid investment framework in my head." It means you know where to look first when doing diligence, or what the top questions are to ask -- those sections of the framework are raising red flags. So why not hand out this framework to earlier-career investors? I think it's ultimately what we'll be training investment LLMs on, a long-time reckoning that investment process is essential.

Beginning a larger project: creating a CRM

 I've been putting off creating a CRM tool, but I think it's about time. Maybe CRM is the wrong word: I need a tool to store info about all companies, investors, people, etc. to be able to build on down the road. The tipping point: I want to create a sustainable sourcing framework that scrapes data from X source weekly. (Again, I am too "lazy" to do this weekly myself -- I'd rather list all the sources I want to pull from, and have it run automatically.) 

Part of this project is to see how long it takes to build something like this. I know a lot of folks are building these kinds of tools in Notion, but who wants to build a "Notion database" when you can build a more robust SQL one? I also think this sort of tooling is going to be critical scaffolding to build other AI tools on top of (e.g. document processing). 

My full vision makes this a little clearer:

1. Create core data models. Create data models for "organizations" (companies and investors), funds, people, programs (e.g. a16z speedrun, DARPA) and cohorts, as well as all the relationships between them (company-to-person, investor-to-company, program-to-company, etc.). Allow users to create/update these. All built in Supabase (backend), frontend in Streamlit (for now).

2. Create data pipelines. Populate companies from websites. For example, a16z speedrun cohort 6 was recent; the pipeline should be able to pull from their website and pull companies and relationships.

3. Audit trail. See who updated the companies (pipeline? user?) 

4. Add authentication, other usability tools. Let users log in via Google, email users when things are updated in the pipeline, make the scheduler work.

5. Add in investment details and company details. Right now we have the basics (basic company demographics, binary on whether an investment was made). Expand out to support a more full investment structure (e.g. X invested $Y in Z in Series A) as well as more robust company reporting ($X in revenue in 2025, etc.)

6. Add database partition? Find best way to make the database specific to a single investor, so that you can layer on multiple companies who can't see each others' data? (And try to stay in Streamlit for simplicity?)

6. Add in document processing. This is where it gets investor-specific. Process documents to extract revenue, investment #s, etc.

7. Add in other fun workflows? Allow users to connect their emails so you can see if you've emailed X company? Layer in automated meeting notes?

Hoping to be able to get through #1-3 this weekend; would be great proof-of-concept. The hard parts: database design (need well-designed database to withstand all future additions I'm planning!), knowing how to layer in the additions, knowing which order to tackle these in, knowing what the benefits/weaknesses of the tools (like Streamlit, SQL, scraping tools, etc.) are, hoping Google Antigravity is able to suss out what I want it to build with my instructions. 

The various modes of investment work

I've worked on a few deals in the past month, and I now finally have a lull to think a little more about how to use AI to automate/replicate some of the work I've been doing. I've been thinking a lot about the types of work that I do. Investment memo work falls into a few buckets:

• Information distillation - given a data room of information, distill it down into the main points
• Information search and aggregation - given a topic, try to find as many pieces of info on it and aggregate them together
• Information verification - given a piece of information, verify whether it's correct 
• Financial analysis - digging into the historical financials as well as checking the assumptions in the financial projections
• Anomaly detection - given a document or set of data, see if any piece of it is off/wrong
• Interviews with founders, experts, customers, etc. - requires a lot more EQ and conversational skills, but also preparation to tee up the highest impact questions for the investment (and the best fitting questions for the interviewee)

This all feels a little abstract and unnecessary, but teasing out these modes of investment work is the way a developer would look to approach a broader fix to the problem. And, more importantly: AI is going to be used in a different way for each of these modes of work. I think that LLMs are sufficiently strong where a lot of problems can be resolved through clever infrastructure (i.e. not just throwing everything into the chatbot and hoping it does everything we want). 

Some examples of the investment memo process that are particularly time-consuming:

• Industry background - a lot of "information search and aggregation" -- Googling, ChatGPT'ing for trusted sources, listening to podcasts, reading consultant market maps, etc. Requires a lot of exploration and breadth; feels a little like climbing a mountain, and the surrounding landscape becomes clearer bit by bit. 
• Competitors - "information search and aggregation" -- Google/ChatGPT for competitors, then look up info on each of them (market niche, traction, last fundraise, etc.)
• Public comps and recent M&A - "information search and aggregation" - Google/ChatGPT for this info, then look each up (e.g. in Bloomberg/Yahoo Finance for public comps, internet verification for recent M&A)
• Company (e.g. team, product, GTM, moats) - "information distillation" -- taking the whole data room and compressing it into a few pages of info. There's additional critical thinking (e.g. does their GTM actually make sense? are the moats really moats?), but otherwise a lot of depth here.
• Term sheet review - most terms are typically standard (or within reason), so this is both "information distillation" (taking a 30-page legal document and boiling it down to a few key points) and "anomaly detection" (seeing if there are any particularly egregious or atypical terms). 
• Traction/Financials - "Financial analysis", but then a lot of critical thinking and gut-checking on top of that.
• Investment benefits and risks - this feels like it should be a lot about "gut" ... but I think an LLM could surface many good benefits/risks (and a human could then review and prioritize them)

I've been working on a verifiable, AI-driven "information distillation" process. Given a data room of information (or a pile of industry reports from McKinsey plus a few internet articles I dug up), can I get the AI to synthesize the info rooted in the files I give it? 

Part of me feels a little foolish for doing this: the Googles and Anthropics of the world are already doing this, and they have teams that are much smarter than me! However, I think the tech giants are solving a fundamentally different problem. Their chatbots are generally interested in: "can I answer any question given to me well?" and "if the user uploads documents, can I answer any question he asks about it?" The challenges are at least twofold: (1) the chatbot has to retrieve info about any topic I might throw at it, and (2) it has to be able to answer any question I ask. 

My little tool is much simpler: given documents, read the info and file it away neatly into folders that I ask it to. The process looks something like this:

• Go through each document and extract information relevant to different buckets I give it (e.g. "company background," "team," "moats," etc.)
• After processing all the documents, take one final pass to synthesize the extracted data together

Advantages: 

1. You can apply this process to any document in the data room (just need to dial in the "folders" you file info into)
2. The extracted info and process are verifiable (unlike typical LLMs which are black boxes)
3. You can apply this process to many use cases -- just swap out the extraction schema and synthesis prompt

I'll have another post with more details, once I tidy up the development.

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AI pulse check

 Tech is changing week to week, so seems like it would be good to journal the in-the-moment sentiment. Tech news — especially those that get clicks — tend to be sensational, absolutist, … and conveniently, good marketing for tech products. 

Token consumption: one thing I remember hearing a few months ago was “don’t think about token count at all when you build, LLMs will get continue to get cheaper and go to zero.” I was a little skeptical — I’ve gotten burned on cloud hosting costs, and am skittish about letting pay-as-you-go LLMs go unchecked. There’s been some pullback on this sentiment: (a) agents run amok, churning through tokens without real value, (b) announcements of companies trying to maximize their token usage, a clearly misguided goal, (c) realization that smaller models (and gasp Chinese models) may be good for some use cases, and (d) the realization that cheap tokens are subsidized by VC money, and over time prices might increase just as Uber prices have. 

Pure vibe coding enthusiasm also has been tempered. The market believed (believes?) that vibe coding would end SaaS, but hitting the realization that software is ~20% code (the rest is infrastructure, marketing, support, installs, etc). I buy that vibe coding is lowering the barrier for designers to build MVPs, but I don’t think it’ll be able to build full apps well within next few years. Too many software architecture decisions etc. that will be hard for it to do.

The hot topics on my mind today: 

- Claude Cowork — I need to explore this more; it’s gaining early adoption. I’m little nervous about it having access to all local documents, but I can see the appeal of having one provider access to all your docs.

- Skills — excellent marketing term for a “template of instructions for the LLM.” Lowers the barrier for LLM entry, adds abstraction in a way non-programmers can understand. Need to play with this more to see if there’s more to it than that. 

- Process and clear instructions — interestingly with “skills,” the hard part is the boring stuff: process, governance, clear instructions. I heard one good analogy: treat AI like a new employee — you have to onboard it with culture, rules, norms, all the boring stuff. I think this will continue to be a trend; humans will be more and more important, just doing (what is to me more) boring work. But AI as with great companies: governance, structure, feedback win.

- Death of SaaS — world still seems split on whether SaaS is dead. Maybe it’s moving from a seat-based model to an outcomes-based model? I think one way SaaS dies if if (a) you can build your own software or (b) there comes along a superapp that can do everything for you. I’ve tried (a), and that approach may work for a small subset of technical people (who probably were building their own tools pre-AI). I could imagine a world where all computer commands are routed through an Anthropic or OpenAI who has access to your whole digital world. Hard to tell now if this will be mass-adopted or just confined to the most technical. 

Example of AI coding speed + implementation

My start-up, Transpose Health, has a core data conversion engine, but data work also has a lot of random stuff that needs to be done. AI coding has been transformative for this. What might've taken a few hours to code (and test) now takes a couple minutes to prompt ChatGPT for, then a minute for it to code. 

My prompt:

write me a python script that: 

 - opens up <excel file> (an excel file) 

 - loops through all CSVs in <folder> that are CSVs and do not contain the word "Copy" (table 2) 

 - inner joins table 1 to table 2 on "Legacy Rx" and "PrescriptionNumber" (to only get the rows from table 2 that are in table 1) 

 - prints out all of the messages from the "hl7_str" column in table 2 into a txt file (separate the "hl7_str" rows with a return character)

Of course, ChatGPT was not perfect the first time around. I had to (a) prompt ChatGPT it to be less verbose (I needed a quick-and-dirty script I could review quickly, not an industrial-grade one), (b) run the script, (c) debug it (it had a small bug, so I had to do a deeper code review), (d) then test the outputs and iterate. Overall, the end-to-end process took ~30 minutes, but I saved significant time and brain energy on the coding part. 

(Interesting that my value-add shifts from understanding/writing the code to understanding how to prompt the LLM and QA the outputs. In other words: AI is taking all the technical implementation work from me, for better and for worse!)

AI: the boring things will be the most valuable

 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!?)

= = 

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.

= = 

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. 

Cash Flow Tooling for Fund Analysis

I've gotten quite "lazy" after working at a tech company for a while -- I know automation exists, and so I try very hard to avoid doing rote tasks. (Classic software engineering trap: spend 10 hours to save 1.)

That being said, AI coding tools should be able to help me do this quickly and automatically. There's two big steps to this process (we're only tackling the second here):

1. Retrieve data (manual step for now) -- from PDFs, Excel files, etc. For now, I've done this manually, because this is a surprisingly hard problem to do at scale. (The files can be in so many different forms, and PDFs are notoriously tricky to read! There's also an open question on how much AI should process this vs. other means; e.g. if it's in Excel already, why use an LLM and run the risk of it hallucinating data?)
2. Analyze data and build pretty graphs (AI-assisted code) -- Once the data is clean, this step requires no LLMs, just regular code. (This also means no private info is sent to any LLM.) Again, I'm using Google Antigravity; I've copied my initial prompt below.

Initial Google Antigravity prompt to build IRR tool:

help me build another tool -- "Cash Flow Analysis" -- that helps to analyze a VC/PE Fund's cash flows.

This should take in as input an Excel sheet with the following sheets/columns:
"LP Gross Cash Flows" - columns: Fund, Company, Date, Gross Cash Flow, Notes

"Unrealized Value" - columns: Fund, Company, Date, Gross Cash Flow

"Unlevered Cash Flow" - columns: Fund, Transaction, Type, Date, LP Net Cash Flow

"LP Capital" -- columns: Fund, Date, LP Net Cash Flow


Outputs:
- a graph of cash flows, usin ghte LP Gross Cash Flows chart, with $ amount on Y axis and time period on x-axis. DIsplay negative cash flows as a red bar (down) and positive (i.e. distributions) as blue. also have a "net cash flows" line graph

use altair to build this. allow filtering by Fund and Company. also allow a parameter for the x-axis to be quarterly, half-year, and year increments.

- generate a table showing for each fund/company the total investment, total distribution, whether exited completely, gross IRR.

- calculate total gross IRR per fund, using unrealized value in the equation as well


build a similar graph as above with unlevered cash flow tab. use altair to build this. allow filtering by Fund. also allow a parameter for the x-axis to be quarterly, half-year, and year increments.

calculate fund-level IRR using the current unrealized values in LP Capital slide

Errors and Clean-Up

AI coding can be finicky. (Equally finicky are my prompts -- there's a ton of room for AI to "interpret" what I'm asking of it.) This is where a lot of the extra time with AI code comes in -- refining, revising, pivoting, etc. Here are all the additional prompts I added on, typos and all (took ~2 solid hours in total):

• I'm hitting this error: Error processing Excel file: 'Company'
• Error processing Excel file: Invalid frequency: QE, failed to parse with error message: ValueError("for Period, please use 'Q' instead of 'QE'")
• Error processing Excel file: 'Column not found: Gross Cash Flow'
• meh roll back that change. Instead can you abstract all of these column headers to the top of the routine as variables? maybe use a naming convention like "LP_GROSS_CASH_FLOWS__FUND" to designate "SHEET__COLUMN". this will help when we expand this later on
• can you remoe comapny filter option from LP Gross Cash Flows? Maybe jus filter the company summary table by that ... also -- if it's yearly x-axis increments, can you just show the year and not months in between? .... also the fund filters tabs are cut off so it's ahrd to see the full fund name, can you fix? ... i also get this error in company summary table: Error processing Excel file: complex exponentiation.
• can we replace this with a standard py function? def xirr(cashflows, dates):
• remove the company filter entirely ... for 2022 for one of the funds, i have both investments and distribution and i expect to see both, please update ... in the company summary table, add in "earliest investment" "last distribution" and "unrealized value" columns
• can you also adjust the green "net cash flow" to be the disributions minus investments? also label this line. can you also disable zooming and out of the graph, and make it a little taller?
• move the "gross IRR per fund" into a streamlit metric ... below company summary table, add in a filter to allow filtering by company to see all investments/distributions/unrealized value
• in the "detailed company data" -- can you sort the list alpahbetically for ease?
• can you update Fund Filter to single-select dropdown
• okay - let's remove the x-axis iincrements -- let's just keep it at "year" roll-up. ... i am still seeing two years per year (e.g. 2017 2017) in the x-axis.... Let's rename x-axis to "Year' also .... add a box around the metric ....
• at the fund level: can you add as metrics "total invested", "total distributed" , "DPI" (which is distributed / invested), MOIC, earliest investment, latest investment .... at the "detailed company data" level, can you add in IRR per fund, total invested, total distributed, unrealized value as metrics?
• rename "Positive/Distrubtion" to just "Distribution" and "Negative/Investment" to just "Investment" ... make the bars in the bar graph skinnier (more aesthetic)
• split the metrics onto two lines with 4 metrics wide (the full $ amount gets cut off). maybe also abbreviate $5,000,000 to $5.0M (maybe create a shared tag to do that, if a library function doesn't exist?)
• can you add all the same metrics to the unlevered cash flow screen? (total invested, total distributed, unrealized value, dpi, moic, gross irr, latest investment) ... can you also add the vintage to both
• can you actually add the vintage in the name of the fund as well? ... in a collapsable section, can you also print out all the numbers that go into the gross fund-level IRR calculation? (i need to check #s) ... remove "Micro Metrics" (I think it's not needed)

Result

Here's a redacted version of the output. Overall -- fairly successful project, with a good foundation (i.e. not vibe coded). Certainly can be refined more, but a good look at "what's possible today" (and hopefully a time saver for me in the medium- and long-term!).

Emotions and Investing

The current global geopolitical situation is distressing to me. That we went to war with Iran -- seemingly on a whim, with a first strike, and with the belief that we'd solve the Middle East in just a few days -- has to be amongst the dumbest, most unforced error that I can imagine, a level of hubris only enabled by yay-sayers following Venezuela. 

4/2/26: Yet another memorable (and jaw-dropping and frightening) Truth Social post today ...

I couldn't get past the sheer stupidity of our Iran entanglement, which I believe made me not notice it as a good potential investment opportunity. The smart investors, however, are able to think through these types of events a little more clearly. In hindsight, there was probably a decent chance that Trump would do something in Iran, especially after his Venezuelan conquest, which would inevitably drive up the price of oil. A simple bet, then, might've been long oil -- low-ish downside but high potential upside. 

More broadly, I think it'll take a little more time and experience to extricate my emotions from the act of investing. It brought me back to my days working in healthcare IT: inevitably someone would do something stupid, and we (I) would have to help clean it up. I got really good at just ignoring who caused the issue and instead diving in and fixing it. Perhaps it's a little harder with investing, especially because politics are inherently emotional (especially today). Nevertheless, still a good weakness to identify and an aspiration to work towards.

AI Notes - AlphaSense and Future Home Grown AI Tools

In the past month, I've written a few investment memos from soup to nuts, so now have strong opinions on what LLMs might be good for. 

First, AlphaSense. We have access to AlphaSense, so I've been able to give it a whirl. My takes:

• Mediocre but voluminous investment memo writer -- We've come up with a decent investment memo prompt, and it can pull together a 20-page investment memo in ~15 minutes. However, some info isn't relevant, some isn't well supported, and overall ... it's 20 dense pages. More detailed gripes below.
• References to internal sources -- One thing I love and hate is that it links to internal materials (e.g. Affinity, Sharepoint). Upside: when I've had trouble finding a single piece of info from the data room, AlphaSense did a good job on hunting down the sources of truth. Downside: it'll link to old memos or in-flight memos, which is not helpful if I'm trying to verify or double-check details for the in-flight memo. 
• Expert calls -- I've been diving into some topics I have little prior knowledge in, so I've tried to make use of what I see as AlphaSense's primary edge: expert calls. I've tried to ask it something like: "Find me all expert calls that talk about X industry or Y company or its competitors" to get better situated from insiders. Overall valuable, but not worth the price of AlphaSense alone.

My current thoughts on tools that'd be (a) buildable and (b) useful:

• Draft investment memo writer -- I think a well-constructed LLM tool can write a good investment memo. The keys are (a) asking the right sequence of questions to the LLM, (b) linking out to external sources for some pieces (e.g. for public market comps), and (c) knowing which pieces to delegate to LLM and which pieces should be 95% human. (I'll dive more into in another post, but one example: the "Investment Thesis" section should 100% be human-written -- if not, what is your value as an investor??)
• Data room analyzer -- I wrote a little tool to help spill out all the contents of the data room (i.e. show me all the files in all the folders in an easy way), which is a good first start. Next step would be to get a rough summary of each files, and step after would be doing some heavier analysis. It'd also be great to be able to ask questions of the files, or at least have it help me find where X company is referenced. (i.e. what I use AlphaSense for). Should be buildable with LLMs and RAG (at least in a rudimentary form) -- big question is if 80% of max capability (what I-plus-AI am capable of) is good enough.
• Investment memo cleaner upper -- Would love to have a tool to go through a memo and tidy up tables, help with resizing, flag areas to review/condense, etc. Not sure how much you can integrate into a Word doc. Will explore.

VC Notes (Part 2 of ?)

 Busy past month -- lots of deals. Trying to jot down a few notes from the "inside" before I forget, things that are hard to really see as a student or outsider:

"Good Fundraisers" vs. "Bad Fundraisers"

We've been looking at (or working with) a few companies that have great fundraisers, and a few are less enthusiastic. There's things to like -- and be fearful -- about both.

• Good Fundraisers
• Who they are: The founders tell compelling stories that connect with investors, making you feel like the TAM they state is not only real but attainable. 
• Pros: If you invest early, you can have some confidence that (1) they can fundraise themselves out of cash burn troubles and (2) if they execute well, the next round will be marked up handsomely
• Cons: The valuations (e.g. on a revenue multiple basis) are rich, and you're essentially a momentum investor in the company. Rocket ship will go up (until it crashes).

• Bad Fundraisers
• Who they are: the founder has a hard time fundraising and/or doesn't love it. A few archetypes: a company that has been just treading water looking to raise a bridge or a new round (company problem), a technical founder who sees fundraising as a necessary evil but isn't great at it (founder problem), a founder who doesn't love fundraising so does the bare minimum to raise, potentially underpricing their raise (founder-role fit?). (There's surely more.)
• Pros: Essentially the opposite of good fundraisers. If you invest early at a "discount to fair market" (because they're a bad fundraiser) and the team executes, the next round should be up as the valuation catches up to intrinsic value. You're essentially a value investor, betting on the company and founder's abilities (as opposed to their fundraising prowess). 
• Cons: If company is doing just okay, next fundraise might be challenging. A good company could be killed early because of poor fundraising skill.

I imagine these general fundraising skills apply at all levels (i.e. both start-ups and funds) and have variations by sector. For example, a "bad fundraiser"/"deep tech" start-up combo seems okay, but a "bad fundraiser"/"B2B SaaS" combo might signal the founder will have a hard time selling and with G2M. 

Founder / Investor Relationship

A study in contrasts again:

• One seasoned founder (fourth start-up) sees the VC investor as a partner -- money is fungible, but prioritizes getting a small handful of strong investors on board. There's still negotiations, but no petty arm twisting. Philosophy is that it's better to have great partners on board, even if at a lower valuation. 
• Another (first-time) founder gives an underlying sense that money is the end goal. There's light arm twisting (if you can invest $X more, we can give you X rights that you're asking for). This can result in small "lapses" in full transparency with investors or haggling over insignificant things (like board observer rights). 

I imagine: (a) some of this is coachable, (b) some of this can only be learned over time after being burned, (c) some of this is hardwired into the founder, and (d) some of this comes from "greed" (i.e. after seeing your shares are now valued at $X million and the investor is trying to "take" $Y million from you). 

Not all investors are created equal, too. CI has the advantage of being an evergreen investor, so can invest pre-seed (say, $500K) and continue investing for the next 10 years (up to $10M in a single company). It allows us to have a more long-term approach, perhaps allowing for a tighter founder/investor relationship than other VCs in the industry.

Yale Cooling Conference

 Notes from Yale Cooling Conference. Overall -- seems like a cool market I've never heard about, with a strong environmental case but still figuring out how to make the capital case. Will require mix of big industry investment (project financing and debt) and some new venture ideas (venture capital). There is a PE-backed services company in the list, too. Brief notes below

= = 

The environmental case for caring about cooling is clear:

• Superpollutants (CFCs, etc.) are responsible for 45% of global warning to date
• Cooling will be responsible for pollutant gases than cement, airplanes, etc.
• Cooling is mission-critical to many areas and businesses (i.e. “national security”) and subject to geopolitical risk, supply shocks, etc.

 

The business case for cooling is harder:

• Policy alone won’t scale, need to have market design and private capital to have a meaningful impact
• Some areas are challenging to deploy capital to
• Frances Lodge mentioned multifamily and C&I as challenging – split incentives b/t tenants and owners, and putting new stuff in old buildings is difficult … they’ve tried things like Cooling-aaS, DOE loan programs for methane, etc. … most get stuck in pilot hell
• One other idea: can you make A/C “cool” (like Tesla did with EVs) to change value prop for consumers?
• Industry wants durable solutions with predictable demand
• (my take) lifecycle coolant management is not venture-scalable, more project-financeable; there are some alternative cooling solutions that look … cool (pun intended)

 

Speakers

• Industry panel: Willem Vriessendorp, John Hurst (Lennox), Samantha Slater (AHRI), Wayne Rosa (Ahold Delhanze), Dale Waund (Orbia)
• Randy Spock: industry leaders (Google, Amazon, Salesforce, Autodesk, JPM, Figma, Workday) just announced “superpollutant action initiative”, $100M over next few years to address cooling
• Capital panel: Anastasia O’Rourke, Megan Phelan (AccelR8 Ventures), Frances Lodge (Galvanize), Randy Spock (Google, carbon credits and removals)

 

Startups

• “LCM innovators” (mix of start-ups of all sizes and public companies): Trakref, Therm Solutions (leak detection and repair), Hudson Technologies, A-Gas, Lee Winter (Winter Lab @ Yale), Tradewater, Recoolit, Effectera
• Alternative cooling technologies (ACTs): Magnotherm (refrigerant without cooling, piloted by Coca Cola and UofMD, etc.), Pascal (solid refrigerant tech, focused on affordability, backed by Khosla and Engine), Mimic, Exergyn, Artyc, Clema, SkyCool, ChillSkyn, LuxWall
• (my take) I chatted w Magnotherm and Pascal. Pascal seems promising because it focuses on off-the-shelf components and ease-of-install and adoption. Magnotherm’s tech looked cool too. Both seem venture scalable.

 

Key Vocab

• Lifecycle refrigerant management
• CFCs/HFCs
• Superpollutants
• Montreal protocol on CFCs (1987),l AIM Act Framework (2021), Kigali Amendment

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.