Naval: I credit a lot of my investing acumen to having a loose understanding of complexity science, as pioneered by everyone from Brian Arthur to Benoit Mandelbrot to Nassim Taleb. Complexity theory explains network effects, scale advantages, complex systems, emerging properties and so on.
Silicon Valley’s dirty secret is that a lot of the great technology businesses are natural monopolies. The #1 winner can be worth hundreds of billions of dollars, or even $1 trillion. The #2 finisher might be worth a few billion dollars. And #3 might as well have not shown up.
This happens for a number of reasons. For example, you should understand how, in a proper network effect, each new user adds value for all the existing users.
You should understand how communities display network effects and how to determine their stickiness. You should understand scale economies and brand-based scale.
A lot of people think brand-based scale is just as powerful as a network effect, so they end up investing in companies that have a solid brand but no real stickiness, or moat, as Warren Buffett calls it. When something goes wrong with the product or a better competitor emerges, the brand effect fails.
Data moats tend to be weaker than other kinds of moats. It’s helpful to understand when accumulating data—versus users—is more useful.
Real-time network effects happen when users simultaneously use the product. They can be far more powerful than asynchronous network effects, which happen when people come in whenever they want—though some asynchronous network effects can be even more powerful.