Social Capital CEO Chamath Palihapitiya is a big fan of Amazon and Tesla. But the goal for the special purpose acquisition companies (SPACs) that he launches is to find the companies with the potential to be the next Amazon or Tesla. In this Motley Fool Live video recorded on Nov. 16, 2020, Bill Mann, director of small cap research for The Motley Fool, talks with Palihapitiya about what he looks for when evaluating companies.

Bill Mann: I was going to ask a bi-directional question because you are getting at something in terms of that analysis and what the value of the lead investor should be for you. We have a process here internally at the Motley Fool, we call it the DeLorean. You jump into Doc Brown's DeLorean, you go forward 10 years, you try and do an analysis of what that business looks like at that point in time.

For me, it's a really excellent thought process and how you would want to go about analyzing a company. What is it that you all saw in each other when you've jumped out of Doc Brown's DeLorean in 10 years from now, what is it that you all saw?

Chamath Palihapitiya: This is an incredible question and the framing, that's the key. You just identified, I think, the biggest secret hiding in plain sight to successful investing. It's only been amplified because of zero rates but I think the people that have always done really well have done this irrespective of where rates were. What I mean by this is like, it's all about trying to project your swap actually into the future and trying to figure out what is going to stay the same and what's going to be different. Can I just explain to you in my language, not using your words?

Bill Mann: That's great. You don't want to go with Doc Brown? I'm a little insulted.

Chamath Palihapitiya: But I think the evolution of these technology businesses is really about three phases, and the real opportunity is in realizing where we are and where the scale of returns are going to come in the future. What venture capital does, is there about experimentation. Even though the value accretes, and a company can go from one million to 100 million to a billion, in many ways, it's actually all experimentation, and all we're validating is incremental data points on a spectrum of experimentation. But then there is this really important transition that happens somewhere along the way which is that you go from experimentation to product market fit. In product market fit, then you actually have organizational stability and growth and you're something that can survive.

Then if you figure out how to make that next transition to scale, then you thrive. If you look back on all of these great companies, it takes 5-10 years to really get the experimentation phase right. Then the next 10 years, or roughly 10-15 years, are actually about product-market fit, and then the next 15-20 years are about scale. The returns decay, but the thing is if you can get in earlier, by the time it's only generating a 10 or 12 percent return, you still look like a genius because your blended returns are in the high 20s, low 30s.

Now, look at, for example, a company like Amazon. It was completely reasonable up until about 2010 to not think of that company in the highest light, because it was still experimenting. It was tremendous amounts of experimentation, highly subsidized in a way that the strategy wasn't really clear. But somewhere along the way, it made this really important transition, from selling a book then to selling everything that fit into roughly the same size box, maybe a slightly bigger box. Then at some point, they're like, "[Expletive] it, we're selling everything." You needed to be, if you were to generate the super returns, right at that point where they went from experimentation to product-market fit.

If you look at Tesla, where the extreme return has been generated, it's been right at that point where they go from experimentation to the initial product-market fit phased. The reason is because that phase of product-market fit is so poorly understood by so many people, and that the velocity of dollars don't come until the end of that phase and the beginning of the scale phase. But when they come, it's so violent and it prices the company forward by many years, and on a dollar-cost average basis, that's what gives you an enormous margin of safety.

If I'd to take your analogy, in my language, it resonates so much because every company that I look at, I'm thinking to myself, where are they in these three phases of company building? Can I get there after experimentation is roughly done and when we are about to start scaling product-market fit? Because at some point, and because it's impossible to judge, everybody else realizes that product-market fit is at hand and they pull the company forward in valuation by a big number of years, and then it allows that company to then become a self-fulfilling prophecy and get to scale.

When you saw the Model 3 working, and you could have underwritten an entry point into Tesla, you would have been at that point, in my opinion, looking back about when they transition from experimentation to product-market fit. When you saw Amazon commercialize AWS and commercialize the e-commerce business beyond early adopters. When you started to see Fortune 500s and Fortune 1,000 sign-up for AWS and the number of Prime subscribers crest over 25 or 30 million, again, in the mid-2000s, you should have realized we got lucky in those bets because we did them. But that was the process that we went through.

If I had to translate that to Clover (NASDAQ:CLOV), let's be honest, these guys, when I first saw them, they were trying to do something which seemed impossible, which is they actually wanted to rewrite the incentives of healthcare, where if you have to just look at Uniteds (NYSE:UNH) of the world, you are paid to just be a part of the entrenched established infrastructure of how healthcare works, which is that the velocity of dollars multiplied by price inflation, multiplied by small increases of costs, makes the system go up, everybody gets rich. We, as individuals, get put at some risk, but the system gets rich.

When Vivek was like, "No, I'm going to basically do the exact opposite," part of you is like, "Well, good luck, but it's too much of a risk for me." He had to go and experiment for seven years. But then earlier this year, when I reconnected with him again and we restarted the process, it's like one of these aha moments where you're like, "Holy [expletive], are they about enter this product-market fit phase?"

When you double-click and you basically just look at the product, how it's used, how the doctors use it, then the real thing is what happens to the cohorts over time, meaning the patient population pools that are treated with this relative to a counterfactual that are not? Can you extrapolate from that to see how something that works in the tens or hundreds of thousands could be allowed to work in the millions, and does the economics threat together? My conclusion to that, it was.

A lot of what I've tried to describe to folks is a belief that we are at the early part of product-market fit. In the next couple of years, there's going to be some tipping point where some number of people that every traditional, entrenched, conservative, buy-side person will then also say, "Okay, [expletive] it," and they're in.

Bill Mann: At least, of course, that's how it works.

Chamath Palihapitiya: Or they'll say, "Oh, I knew it all the time. Of course, we wanted better outcomes at lower costs. Of course, that's what we wanted." But they're not willing to do that upfront. Where the real money is made is finding and putting bets on the table that represent early product-market fit before late product-market fit is identified by institutional buy-side and before the funding of scale happens. That's where the extreme returns come.

This article represents the opinion of the writer, who may disagree with the “official” recommendation position of a Motley Fool premium advisory service. We’re motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.