We had an AMAZING session with @UntappedVC LPs this morning on the "Future of AI in VC".
Here's an abbreviated version as a Twitter thread!
👇
To set the stage, here's our job as a VC.
- Strategy: Thesis, portfolio construction, etc.
- Fundraise: LP comms, fund mgmt
- General Ops: Marketing, legal, HR, etc.
- Investing breaks down into: Source, Decide, Win, Support
History of AI in VC
AI in VC isn't new, but it's primarily been in two areas.
History of "AI" in Sourcing
It's basically a filter for things like keywords, past jobs, university, or revenue.
History of AI in Selecting
Algorithmic scoring has been going on for a while. It works at late stage, where there's lots of data - but even then, it's good for outperforming averages, not being the top 1%.
At pre-seed, not enough data.
Future of AI in VC
We're applying AI to every aspect of the business.
Our Approach - People focused.
- Why use AI: to meet and learn
- How to use AI: to enable and empower people
- What is the output: AI+human output beats AI or human alone.
Built to Learn and Meet
- We "Build-In-Public" to meet people.
- We build tools to support founders at scale.
- The learning from building are passed onto our portfolio companies
Here are a few examples of ways we've passed on our learnings to portfolio companies.
They love it!
"Augmented Intelligence"
Here's a specific example, Mini Yohei is a tool for portfolio founders in our portal where they get:
- Immediate response from AI (faster than me)
- Reply from me to the AI response (more accurate)
We do this by having me CC'd in the reply to founders.
Our AI Capabilities
We'll walk through a few examples...
Mini Yohei 2.0
VC Expert who provides answers grounded in VC blog posts.
This is a similar workflow to how I found articles to share with founders, but automated with AI.
Chatbot Experiments
We have many chatbot experiments, both public and private.
All chat logs and summaries are available to us as we are using this to build relationships. (Some founders leave their email in the chat for us to reach out)
Web/Deck/Email Parsing
Basically the ability to scrape and extract data from any type of content. We use this capability across many of our tooling.
Quick Memo
Our investment memo drafting tool is amazing.
Can generate tons of insights as well as similar startups, unicorns, and acquisitions. Also sentiment analysis from ProductHunt and many other features to be added.
Our LPs can use this.
Search ProductHunt
We embedded the most recent 22k ProductHunt descriptions so we can find any product we're looking for easily.
We share this with our portfolio companies and LPs.
Interaction Summaries
We're experimenting with a tool that auto-generates interaction summaries for every email address, and at the domain level.
A powerful tool for anyone maintaining a large network.
OpenAI x Zapier
This was cool. Our unofficial @OpenAI x @zapier integration turned into an official integration.
Thinking about how we've enabled millions of non-coders to leverage building with GPT-3 makes me happy.
Talk about impact.
Other Tools
We list a few more here, but this isn't even everything.
You can see most of the stuff I've built at yohei.me (build-in-public log).
Fuzzy Compiler
This turns abbreviated code or instructions into clean well formatted code.
This and the Zapier integration are examples of tools we build to help us build faster.
Modular Tech Stack
Our tech stack is built modularly with a combination of code and no-code tools.
We designed it so parts can be easily added, replaced or upgraded.
For example, it took us 5 min to add the ProductHunt search tool to our LP portal and Founder portal.
Impact on our firm
Tying this back to the beginning of the prezo, this is how our AI tools touch every aspect of our business.
And for our secret demo...
Actually, let's keep this redacted 🤫
And there you go! That was...
"The Future of AI in VC" (abbreviated)
Hope this inspires some ideas on how AI can apply to your business.
[#dalle image prompt: minimalist rainbow splatter art unicorn, black background]
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ooooh yess i got this working in 244 lines of code
- single llm loop with three tools
- installs required packages
- creates new tools and loads it for itself dynamically
- uses the tools to handle user request
in this case, i asked it to scrape techmeme:
iter 1: install 'requests'
iter 2: install 'beautifulsoup4'
iter 3: create_tool (error'd)
iter 4: create_tool (worked)
iter 5: use new scrape_techmeme tool
iter 6: write summary
task completed!
😮 ohhh... it's less code but can do the same thing* ditto can... it just created a create_directory tool and so on...
*almost the same - it can't create a flask app because it accidentally initiates the flask app and kills the loop. but it can write multi-file apps.
for clarification, this is different (and simpler) than ditto, which i just shared
ditto just created a python flask app
this one creates it's own tools
here’s babyagi 2 - a weird Python framework for building a self-building autonomous agent
- stores and executes functions from a database
- auto logging (as graph)
- built-in dashboard & chat playground
- prototype self-build functionality
wanna see? 👇
friendly reminder: babyagi is a personal side project being shared publicly. i am not a dev, never studied cs, this is not secure, not meant for production, and meant for playing/as inspiration for developers.