Reminder: the next opportunity to join @earnestcapital as an investor, backing founders of calm profitable software at the early stage, is coming up on 1 Oct. Looking likely we'll hit our $1.25m/quarter cap for this fund.
Our funds run on a quarterly subscription model.
Minimum investment is just $5k/quarter (some lower exceptions made for unrepresented folks): earnestcapital.com/quarterly-subs…
Ok folks, Feb marks 4 years since we started investing at @calmfund and it's time for a big update thread. It's been a challenging but insightful few quarters but I'm absolutely fired up about the road ahead. I'll cover what we've been up to, our strategy & the game plan for 2023
If you're new here, @calmfund is a fund built to support founders of calm companies that are capital efficient, grow sustainably, & built for the long term. Here is an overview of our high level investment thesis (we used to be called @earnestcapital btw):
"Calm company" is both an investing strategy and a mindset for founders looking to build a company in a manner that I call "being long-term ambitious" ... here are some of the key principles to give you a sense of what it means:
I think folks are sleeping on one of the biggest and most obvious "zero interest rate phenomena" that's going to get vaporized slowly and then all at once: Andreessen Horowitz
Here's my thesis:
In recent years a16z built an incredible machine to accumulate "assets under management" (AUM) to the tune of tens of billions of dollars. Nominally they were supposed to be doing venture capital—investing in startups and entrepreneurs—but really they were making a different play
As I laid out in 2021, they (along w hedge funds like Tiger) identified there were vast pools of capital searching for *any* returns in the zero-interest-rate wasteland and came up with a fundamentally different strategy from the traditional VC model
Good morning, AI-based tools will be a massive opportunity for calm companies, but a dud for VC. Here's my thesis:
1/ this market map will become an iconic case study of "was a feature not a product" with all of the value ultimately being split between the actual core AI platforms in the top left corner and incumbent software tools with distribution (mostly not shown) who will add AI features
2/ It's almost a meme/joke at this point, but the vast majority of these products are just (a) an API call to GPT-3 or similar platforms and (b) a thin layer of "prompt engineering"
This is the most nonexistent moat in the history of tech waves with no clear way to improve
✨ Cultivating a Calm Company ✨
A running thread of the most important things I have learned about building a calm company and being long-term ambitious as an entrepreneur. Let's go 🙏👇
People keep asking me "why aren't more investors using a strategy similar to @calmfund's"... the answer is very simple: it is *unbelievably* difficult to raise capital for an early stage fund that doesn't fit the traditional VC model. That's it.
For our first 3 small funds we had to raise money from nearly 200 individuals. I had to write 200,000 words of content and build a fully automated fundraising funnel such that for Funds 2 & 3, I didn't even speak to >half of the investors before they committed.
But those funds were "sub-scale" (<$10m)... in order to really address the market need, build a fully staffed team, and get the best returns for our LPs, we need to raise substantially larger funds (like $100m... still tiny in the scheme of things)
I looked at dozens (100s?) of crypto/web3 startups and projects the past few years. I felt obligated to not miss an opportunity for my LPs. With each one I asked a simple question: "does this do what it says it does?" and every. single. time. the answer was "it does not."
I'm talking about technical and logical due diligence of the basic claims being made about the technology with regard to utility, security, and decentralization. And every time I did a little homework, the answer was an obvious no.
I'm not a technical wizard or domain expert, but I (a) tried to learn as much as I good (b) asked dumb questions (c) paid attention to technical experts and (d) just thought hard about it a little bit. Turns out that was far more than 99% of investors were doing.