It’s a knowledge base. And it’s a knowledge base that grows independent of, and outlives, any current set of partners that may work at Social Capital. That’s the key.
I don’t view my role as instrumental. I view my role as part of a process of creating that artifact. 15 or 20 years from now, there’ll be an entire suite of employees and partners at Social Capital that then continue to build that knowledge base, and then 50 years and 100 years from now.
Because the output of that will be better entrepreneurship. The odds of success go up. Your ability to now differentiate yourself goes up. Then, going back to how we started, it then does the job not just of capitalism, but of governance to the extent that governance continues to be great.
Well, I think economically, actually, it behaves the same way [as glomming on a consulting company to a money giving company] in the following way. This is part and parcel of a comment about what you said earlier as well. Why are VC’s reticent to do it? It’s a money game. When you have nine people running $10 billion, that’s way better than having 90 people run $10 billion because the fee income is so gi-normous that you’d rather just chop it up amongst nine people.
So if you go to the entrenched establishment and say, “Hey, you know what, I think what’s in the best interest of the entrepreneur is not that you make eight million bucks a year, but instead that you hire a bunch of machine learning and data science people to help support them.” The answer is, “Yeah, in theory that’s right, but you know what, they should do that on their own.”
Okay, well the problem is, they can’t do it on their own individually.
And it’s not their fault, you know why? The problem, why they can’t do it? Just in the last five years, do you know how much money has gone into Silicon Valley and China?
One trillion dollars. How does one trillion dollars find a home without the following market conditions emerging:
A bunch of companies getting overfunded, many who should otherwise be going out of business so that the talent can then be attracted to the winners. Now, instead of a two-year life cycle from starting to failure, now you have a four- or five- or six-year life cycle where the outcome is the same.
So any one company now, just statistically has a much lower chance of getting the talent they need to solve these problems. Whereas what I can say is you know what, that infrastructure that can help you do massive amounts of machine learning on top of massive amounts of data to drive real outcomes exists in three companies: Facebook, Google, Amazon.
It just so happens that I was, at the worst case, an accidental tourist that helped build one of them. I can attract the same kinds of people to work with us across 50 companies.