Whenever we look at an investment now, we will not move forward in deep diligence until we have a one pager. It can literally only be a page. If you’ve ever tried to write a one pager on something complex, it’s really hard to capture saliently the few things that matter.
At the top, we always ask the same questions repeatedly.
The first one is, is this really addressing a fundamental human need? We’ve done a lot of work to try to actually unpack what we think these fundamental psychological human needs in society are, and we try to say whether we’d meet them or not.
We ask ourselves, in 2045, could this be used by a billion people? In 30 years, could this be a $100 billion plus company? We say 30 years explicitly because it gives you the time horizon to see what could be possible.
The last question we ask is, is there somebody really special here?
When we’re trying to get to ground truth, those are the questions that we’re really trying to answer. And by “answer,” the best examples of investments we’ve made, the answers are, “Possibly,” “Possibly,” and, “Probably.” Many times, the answers are, “No,” “No,” and, “Unlikely.”
“Possibly,” “Possibly,” and, “Probably,” is a fantastic set of answers for those questions.
[$100 billion] is intellectually tractable, without being crazy. I first started with, “Could it be a trillion dollar company in 35 years?” I just kept iterating until somebody [accepted it.]
That allows us to clarify quickly how to spend time. In the case of this CRISPR thing, it’s like “Yes, Yes…” and now it’s like we don’t know, because we don’t know how to judge those people who are capable today because we don’t know what the true benchmarks on the road will be to genetic engineering.
For example, in that company: How do you judge someone’s morality, or long-term ethics? How do you judge the ways that those ethics will change if they are the ones that are in control of something like that? I don’t know how to judge that.
The second outcome, was we looked back internally and every quarter we do this thing called an expectations analysis. We ask everyone at the firm to give a low, medium, and high expectation for every single company we’ve invested in. What’s interesting about that exercise is that you’re talking about a very diverse, eclectic, group of people, meaning that we have data scientists, engineers, associates, partners, investors, CFO, GC… What you get with that is a very asymmetric amount of information. The companies where I’m on the board, I have a lot of information. Even though I’m sharing the information out with everyone else, they don’t have time. And other people have different senses of it. Some people may use our products, some people may not.
What’s amazing now is that we have 18, 20, people, that’s enough signal to produce these really interesting data sets that occur. And we’ve been doing this now for four years.
When you look at all of the companies that are generating all of the returns for us, they’re all the ones with these large upper bounds and lower bounds of outcomes; the error bars are huge.
So, wherever there’s disagreement, there’s the ability to generate massive outcomes.
Wherever there’s consensus, there’s still a lot of value, but they tend to not surprise anybody.
Typically what happens is that companies spend many years in these periods of disagreement and lack of consensus about what they’re really doing, and then they get into this breakout velocity and all of a sudden everyone says, “Oh!” They anoint them, whatever.
So I said, “Look. As a series A and B investor, we need to live in that world of ambiguity. How do we do that?”
The only way that I saw that we did that is by putting a bunch of different people together that don’t necessarily think the same way and act the same way.
You may have heard this term, but when you’re a public company there’s something called GAAP financials? Generally Acceptable Accounting Principles, and that’s how you report numbers. Profit, loss, revenue… you know, all of this stuff. So there’s a set of rules that the industry uses to make sure that you can compare apples to apples across companies.
So we said, well, why can’t we create that exact same thing for non-financial metrics? For product health. For user engagement. For all of the things that we care about in the early stage that can really help us think through product-market fit.
And so we built those into tools and we were using them so often, internally, to help make decisions. It was crazy, because we had this debate, “Well, this is our secret sauce. We should not release it.” And then it was like, “Actually, no, that’s not the secret sauce. The secret sauce is being able to look at it and then make a better decision.”
It’s now available. You can use it if you want, you can actually send us the data automatically and we’ll actually look at it with you, and give you our interpretation. Over the next few months we’re gonna be able to allow you to benchmark the data. So, if you want to understand how your MRR growth stacks up to 60 of our best companies, you’ll be able to do that. If you want to figure out whether your cohorts, and users, are as good as Facebook’s publically available data, we’ll be able to allow you to benchmark it to that.
So it’ll just be this ongoing thing and, what we’re trying to do is create, for non-financial metrics, an automated system that allows [entrepreneurs] to just be better informed. And again, that makes the ecosystem better, it improves your odds of getting financed, because now you have a better way of presenting the information, be more sophisticated about what you’re building. Maybe at the edges, you decide that it makes a lot of sense to work with us because we know that stuff too. If that happens, so be it.