Concentration
How big should a VC portfolio be?
How big should a VC portfolio be? It’s surprisingly still debated, so let’s walk through the dynamics to understand why.
This is part of a series of helping founders understand VC incentives and dynamics. Subscribe to get the rest.
Ownership Math
First let’s look at the basic math. A fund will spend around 20% on overhead with management fees (2% yearly for 10 years), and with the remaining 80% then decide to invest a share in follow-on funding, maybe 25%.
First checks amount = Fund size * 80% investable * 75% first checks
First check size = First checks amount / number of investments.
If you wanted 25 vs 50 first checks, for a $75M fund, that math would work out to:
25 checks: $75M * 80% * 75% / 25 = $1.8M
50 checks: $75M * 80% * 75% / 50 = $900K
This already makes clear that you should ask the firm you’re talking to about check size. If they want to typically invest $2M, that is an easier check to win than $1M. Demand curves in venture don’t slope downward.
Let’s assume only 20% earn a follow-on check, so respectively 5 and 10 checks. Similar math dictates the check size:
5 checks: $75M * 80% * 25% / 5 = $3M
10 checks: $75M * 80% * 25% / 10 = $1.5M
This is another area to ask about when you talk to a fund: do you follow on? How often? What criteria? How much?
You can see how this changes your concentration by assuming some typical round values. Let’s say the first check is in a seed round at $20M post and the follow on is in a Series A at $60M post. Fully doing this math requires paying attention to dilution and option pools, which I’ll do in a table here. To summarize the outcome, here is ownership after the Series A and after a few more successful rounds to Series D.
Concentrated
Series A: 11.6%
Series D: 4.4%
Broad
Series A: 5.8%
Series D: 2.2%
Now on paper, if you’re looking at a $1B company, then owning 4.4% is better than owning 2.2%. How sure are you that it will be $1B?
Marginal Confidence
Powerlaws dominate venture returns, where a small number of investments produce most of the earnings of a portfolio. This doesn’t just mean that only a small number of companies win. It’s worse: only a small number of companies a VC decides to back will return more than all the rest combined.
If you ask a VC to rank companies in their portfolio when they make their first investment, they will not do a good job. Consider their follow on funding. Wouldn’t it be better to just pick the head of the power law at Seed and make your extra investment then at a better price?
So now consider the ordering of those 25 investments. How sure are you that the head of the power law isn’t the 26th where you passed? Maybe if you made 50 investments, the top company in your portfolio won’t be worth $1B but $10B or even $100B. A 10X or 100X outcome is a bigger difference than only owning half as much at exit from lower concentration.
Does this mean that VCs are just monkeys throwing darts? Yes, but that’s the plan. Great investors show that their outcomes on average are consistently good despite not knowing whether an individual investment will matter.
I was reminded of this recently when folks on Twitter were reacting negatively to a prediction Clubhouse would be worth $100B in 10 years, when in fact it lost its footing after explosive growth. It’s normal to be overconfident and wrong, and just right on average.1
So the rules appear to be 1) more concentration means more ownership, but 2) on the margin you’re probably missing better investments.
In a way, this second rule proves too much: why not just keep backing more and more companies?
Well, Y Combinator actually settled on just that dynamic. They have fixed investment amounts, but a disproportionately large ownership at seed. They back as many companies as seem potentially great, rather than a target portfolio size. They scale the organization to match the company supply. I wish they let YC alumni like me be an LP because the model is awesome and effective.
Second Order Dynamics
There are other considerations over the long term. One I’ve noticed is that the founders you back become an asset to find new great companies. That matters intensely because within their domains founders often know a lot more than VCs. That’s why many transition from founder to investor. But the fresh network of founders even while they are running their startups proves to be an excellent way to find new great companies.
Portfolio size can also be a burden. How close tabs are you keeping on each company? How responsive can you be if you’ve backed two or three times as many founders? Does that hurt a founder’s interest in your fund?
If you’re spending management fees on helping the portfolio, then those resources could spread thin. If those tools scale with insider usage, maybe they have a network effect by themselves. For example, if you build a way for founders to get introductions through others in the portfolio, more is better.
I hope you can see why this issue is still actively debated. Some investors in funds will look at a basic regression from older funds and decide what works, but I think that doesn’t match the complex dynamics.
This is one reason to not trust VCs about political predictions. They aren’t in the business of understanding the best policy outcomes or in delivering what voters want. Investing is fine if you’re right only sometimes, unlike most of the rest of the world.