For an ecosystem that so values first-principles thinking, the Valley surprisingly reasons via parallels. If this person knows this smart person, then they must be smart. If they dropped out of Stanford CS to build, they must be backable. VCs deploy capital to not miss the hype. After all, if you invest in hype at the pre-seed/seed stage, you have a fair chance of getting a write-up in the Series A/B rounds. Spray and pray does not require precision; be generally in the ballpark—i.e. the right networks, Stanford, or the right markets, *cough* AI—and you will likely make decent returns.
Fund Economics
Spending four years at Stanford drove me to pursue the first-principles thinking and clarity of thought that the Valley so deems it values, yet seems to lack in its investment approach. Surprisingly, this frustration led me to buyout investing at a white shoe private equity firm in New York. In the land of buyouts, you cannot afford to lose money. You write one, maybe two, billion-dollar checks a year. The economics are different—to win, you have to 2-3x, heck, even 1.5x, your $1B check. In venture, to make the equivalent of 1.5x a $1B check (read: $500M of profit), you’d write something like ~100 checks of $1M each, lose $99M from the 99% of companies that go to zero, and make net $599M on the one that 600x’d to arrive at a profit of $500M. Excuse these illustrative figures, which are non-representative, but demonstrate the general difference in early-stage vs. buyout fund economics.
To hit the one VC home run, there is an element of luck, but luck is rather a hand-wavy notion of being in the general vicinity of where good things happen. At Stanford GSB, there are ~5.7 unicorn founders per 1000 graduates. Given that ~38% of GSB graduates are founders, this means that there are ~5.7 unicorns per 380 GSB grad founders. In other words, there’s a 1.5% chance that any GSB grad’s company is a unicorn. Probability then tells us that if you invest solely in companies founded by GSB grads, even if you close your eyes and randomly pick 100, you should hit 1.5 unicorns. Your job, then, is to be in this general vicinity of luck and build relationships to get on every one of these 100 GSB cap tables. To succeed, you do not need a great deal of intellectual rigor in evaluating a market, idea, or product; rather, it is a game of relationship management. I believe that later stages of private market or public market investing are where intellectual horsepower (honesty + rigor) becomes more highly rewarded. The former indexes heavily on the art of relationship management; the latter relies on the science of data analysis in underwriting.
That said, even when a buyout firm acquires a public company, there is still a proprietary relationship advantage (e.g., a great relationship with the banker or board can help you win the deal even if you’re not the highest bidder). What matters a lot more, though, at the later stage and larger scale of investing, is data-driven diligence, given that one cannot afford to lose money on a billion-dollar deal. I find that the partners I work with therefore focus more on precision and first-principles diligence—what are the root drivers of growth/risk/returns, and what are the implications? Can we quantify these implications? What is the likelihood that this assumption is wrong? Can we build a probability-weighted valuation?
Clarity of thought
In the pursuit of becoming a more rigorous investor, especially one in businesses rather than in talent (in this sense, VC feels almost like HR), I joined the world’s oldest private equity firm post-grad. The firm raised a ~$17B fund in 2023 and is closing another ~$17B fund this year. To give a sense of scale, Sequoia raised a $750M early-stage fund targeting Series A startups and a $200M seed fund this year. As you can see, one fund at Sequoia is about one check for Warburg, and hence, the economics are vastly different between the two, and I argue that one rewards more rigorous investment acumen than the other.
Larger fund sizes expand our addressable market and require us to invest beyond pure technology to deploy capital at the scale we target each year. As a result, I also get to evaluate other industries, such as services (e.g., white-collar real estate brokerages) and payments, in addition to AI/software businesses. I find that exposure to other business models lends itself surprisingly well to establishing my base-level understanding of what makes a good business, which carries well into becoming a disciplined technology investor.
Three months into this gig, I already feel more confident about angel investing in (early stage!) startups. When speaking with Stanford friends who have built companies and raised millions in the past few years, I know what questions to ask, hold a stronger framework on why it would/would not become a great business, and feel less dependent on signals—e.g. who the founder knows, who I got the intro from, who has already invested—which although can be valuable, especially from a probability / being in-the-right-ballpark standpoint, cannot be all that one relies on to score differentiated outcomes. I feel stronger in first-principles reasoning about the fundamental market, founder/market fit, and future product/service potential. I feel—literally—sharper. I can cut my reasoning more succinctly. For example, in a recent conversation with a founder who raised $10M from a16z, I thought, the product they are building will become commoditized in the next three years as there is little differentiation between L1 blockchains, so customers will become very price sensitive; plus, interoperability between L1s, which is inherent to the structure of the blockchain, makes revenue not very retentive, and therefore, high risk of churn lowers quality of revenue which will lower multiples for the business in the years to come.
Angel Investing
All this to say, I have a growing interest and confidence in angel investing, which I believe is taking the purest of bets on my own ability to judge markets and businesses, combined with leveraging the power of my own network. On the latter, I feel the power of my network even more now outside of my Stanford bubble. At Stanford, it felt like what was around me was “normal”—however, after living in London and New York, I realized that the bubble I was in, on a quantum basis, is a tiny handful of folks worldwide. I also now hold the belief (which may be arrogant, and I stand to be corrected) that I know the smartest 20-25 y/os in the world. If a 20-25 y/o does not have a mutual with me, then chances are they are not the caliber of smart/ambition I would seek out.
Before spending time at Warburg, I faced unknown unknowns—what questions do I even ask to get at this unknown question of whether it’s a business I’d like to invest in? Now, they feel more like known unknowns—I must ask about the x, y, and z to understand if this business is one I should invest in.
Let this be my manifesto in becoming an angel investor. I hope to continue writing in the coming weeks about what I learn in my day job, sharpen my intellect, and subtly / not so subtly pose to you that I, too, should be on your cap table.
Thanks for reading my first Substack! I’d love to hear your thoughts, especially pushback or counterpoints to what I laid out above. Feel free to get in touch: iristfu [at] gmail [dot] com, and enter your email below to get future posts. Next up I have planned is my current thinking around stablecoins.
Great to see more people sharing thoughtful work around markets and investing.
I’m documenting how I use AI, fundamentals and price action in my own investment process.
Always happy to connect with others trying to become better investors.
Beautifully articulated, you're at the start of a generational run!!