How AngelList Quantitatively Changes the Investing Game

Late in 2007, Circle of Friends was adding hundreds of thousands of users a day, and Ephraim and I knew the time was right to expand our office beyond my kitchen table and raise some money.

We each reached out to a few of our friends, and quickly got a number of introductions to angel investors and venture capitalists. My friend Jared introduced us to Mike Maples. Friends of Ephraim introduced us to Jeff Clavier and Naval Ravikant.

Mike, Jeff, and Naval all wound up investing in our company. Immediately after signing on, and then for years after, each of them introduced us to a number of other well-regarded investors (thanks, guys!).

That’s been a pretty typical experience in Silicon Valley over the past couple of decades. When someone wants to raise money, they reach investors through trusted contacts. When one investor signs on, she then introduces the entrepreneur to other investors she knows well. These introductions can have a strong weight, especially when the initial investor is a trusted source.

This often works pretty well, but it means that companies often raise money from just a small number of highly connected cliques. That has positive implications — those involved likely have more trust in one another — but it also leads to a process that’s relatively closed. Investors often form clusters and invest together. So if, for instance, Mike sits in one cluster and Naval and Jeff are in another, it’s likely that many of our future investors will also hail from one of those two clusters.

To understand this story quantitatively, I looked at angels’ co-investment patterns, using AngelList’s investment data to group the most prolific investors into clusters. I put the top 870 investors into 25 distinct clusters. Each cluster represents a group of people from whom co-investments are more common, for any of a number of reasons — geographic, industry-based, philosophical, or reputational.

(Methodology note: when companies listed multiple investors from a venture firm like 500 Startups, I only counted an investment from one of the partners.)

So do the clusters reflect real investor patterns? To answer this, we can compare to a “random” world, where investors find startups and make decisions on their own, without any social input.

In this random world, the second investor in a startup would sit in a different cluster from the first investor 82% of the time. But the real world is very different from that. In fact, when a second investor comes on board, there’s a 57% chance that he’ll fall into a different cluster from the first investor. In other words, in the real world, investor #2 is almost 2.5x as likely to fall in the same cluster (18% vs. 43%) as in random world.

This trend continues as the number of investment grows. When the existing pool of capital comes from two, three, or four distinct clusters, the odds that the next investor comes from one a new cluster is 47%. If it comes from five to eight of the 25 clusters, the odds go to 43%. All of these are considerably lower than one would expect in a random world: the universe of traditional angel investment is well-networked and influential upon itself (perjoratively: an old boys club).

Enter AngelList, which is increasingly important in the startup ecosystem (disclosure: I’m an adviser). AngelList functions much more like an open marketplace for startups than the traditional model. In most cases, startups make public to all investors that they are seeking investment, effectively widening their pool of potential investors. So does this actually change the makeup of a startup’s investors?

In a word, yes.

There’s a simple way to test whether AngelList is truly opening up funding (and investing) opportunities. If it is, founder-investor connections that come via AngelList should lead to more cluster diversity than those that come through other channels.

Recall that 57% of second investments come from someone in a different cluster from the first investment. If, however, the second investor is someone the founder met via an AngelList introduction, the odds rise to 63%. For startups raising money the “old-fashioned” way with 2-4 clusters represented, the odds of staying in a known cluster are 47%. If the investor comes via an AngelList intro, the odds are 59%. And with 5-9 clusters represented and only 43% chance a new traditional investor will create additional cluster diversity, the odds for an AngelList intro are 55%. Here’s the graphical comparison:

When two or more clusters are already represented, investments via AngelList introductions are about 30% more likely to yield a relationship with a new cluster than their non-AngelList equivalents.

The numbers for AngelList intro investors are still a long way from the “random” equivalents — location, reputation of other investors, sector, and other factors matter on AngelList too — but they’re clearly indicative of a big shift.

Many technology advances over the past decade or so can be put in one of two categories:

1) They make the world more social, allowing us to see what our friends do and like. Basically, Facebook.
2) They make the world more efficient, by giving people access to information and markets. Basically, Google.

AngelList sits in between those extremes. On the friends side, its follower model means that much of what people see is from the people they already know or at least know of. But on the information side, it opens up something that was almost entirely governed by word of mouth, and creates something that at least takes a big step toward being a marketplace.

I don’t pretend to know enough about the macro dynamics of investor management to predict how this will affect company operations. But the effect on the investor pool is clear. As AngelList and crowdsourcing grow, the impact of the old boys’ clubs will shrink. For companies, the pool of investors is growing.

Mike Greenfield founded Bonafide, Circle of Moms, and Team Rankings, led LinkedIn's analytics team, and built much of PayPal's early fraud detection technology. Ping him at [first_name] at