Some of the most successful companies and products — from the phone era to the internet era — have all been predicated on the concept of network effects, where the network becomes more valuable to users as more people use it.
But how do you tell network effects apart from scale effects, brand preference, or increasing returns (an idea popularized by W. Brian Arthur) — or other phenomena that are not actually network effects? What about “data network effects”, which startups powered by machine learning like to highlight? And are network effects really durable competitive moats?
Consumer deal & investing team partners D’Arcy Coolican and Li Jin — who recently co-wrote a pair of posts on the dynamics and metrics of network effects — share their insights with a16z operating partner Frank Chen in this three-part video miniseries… We’re all about network effects, after all.
D'Arcy Coolican Prior to joining a16z, he co-founded Frank, a social lending platform that used behavioral economics to make it easy to lend and borrow money with friends and family. He began his career at McKinsey & Co, where he was an engagement manager in the TMT practice.
Frank Chen currently leads the a16z Seed Program, which helps early stage (typically pre-seed, seed, and Series A) founders build great companies on their way to their next fundraise.