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“rocketcoaster” experience, which is an appropriate description for a company that had gone from an idea to a tiny startup to a massive global company with more than 20,000 employees in less than a decade.
It was a big effort for Uber—we spent hundreds of millions just on driver referrals programs, and nearly a billion in paid marketing. Adding more drivers to the Uber network was one of the most important levers we had to grow the business.
This was a senior group of executives, but the granularity and level of detail was incredible. But this was a requirement to run a complex, hyperlocal network like Uber where supply and demand depended on the dynamics of popular neighborhoods and frequent “lanes”—like Marina and the Financial District—that
In my several years there, it was unusual to ever hear about an aggregate number—like total trips or total active riders—except as a big vanity milestone at a company all-hands.
The Uber team monitored and responded to the health of their local city networks with speed and precision. And with that, the next step was clear.
didn’t happen automatically—there were tens of thousands of people working hard to deal with network dynamics in hundreds of markets around the world, and we learned all the hard lessons from
The term “network effect” has almost become a cliché. It’s a punch line to difficult questions, like “What if your competition comes after you?” Network effects. “Why will this keep growing as quickly as it has?” Network effects. “Why fund this instead of company X?” Network effects. Every startup claims to have it, and it’s become a standard explanation for why successful companies break out.
why has it also been so critical to launch products in the right way? To get your product in the hands of influencers, or high school students, or aspirational technology companies—if B2B—if all that matters is the product? What’s the right way to launch, and what’s the sequence of ways to expand?
This is a critical topic. I’ve come to see network effects—how to start them, and how to scale them—as one of the key secrets of Silicon Valley.
In contemporary parlance, the product is typically made of software whereas the network is typically made of people.
Dropbox, Slack, and Google Suite are workplace collaboration products built from the network of your teammates and coworkers.
Although the networks don’t own their underlying resources, it’s the connection that matters.
We are now in a zero-sum era of attention with minimal defensibility for a vast swath of mobile apps, software-as-a-service (SaaS) products, and web platforms.
Most products these days are low technical risk—meaning they won’t fail because the teams can’t execute on the engineering side to build the products—but they are generally also low defensibility. When something works, others can follow—and fast.
Network effects are one of the only protective barriers in an industry where competition is fierce, and defensive barriers are weak.
Larger competitors are often able to copy the product, but find it difficult to capture the network.
Look at the reality: there are weak advantages to being first, since the winning startup is usually a later entrant. And the winner usually doesn’t take all, and instead has to battle a number of other networked products over control of different geographies and customer segments.
Said plainly, each time a user joins an app with a network behind it, the value of the app is increased to n^2.
However, if for whatever reason the population of these social animals declines, the benefits can quickly go away, making them more susceptible to collapse.
Starting to sound familiar? Yes, it’s true: social animals have network effects, too.
When there are not enough meerkats in a mob to warn each other of danger, it’s more likely an individual in the mob will get picked off by a predator.
The technology product metaphor here is obvious—if a messaging app doesn’t have enough people in it, some users will delete it. And as the user base shrinks, it becomes more likely that each user will leave, ultimately causing inactivity and collapse of the network.
As the population increases, eventually there is a natural limit based on the environment—often called a carrying capacity.
The network effect version of this in the technology industry happens when there is “overcrowding” from too many users. For communication apps, you might start to get too many messages. For social products, there might be too much content in feeds, or for marketplaces, too many listings so that finding the right thing becomes a chore.
But add the right features to aid discovery, combat spam, and increase relevance within the UI, and you can increase the carrying capacity for users.
Why use a texting app that none of your friends are using? Open an empty app enough times, and you’ll quit, too. Pretty soon, the network effects unwind as it speeds toward collapse.
Solving the Cold Start Problem requires getting all the right users and content on the same network at the same time—which is difficult to execute in a launch.
From these case studies, I describe an approach that focuses on building an “atomic network”—that is, the smallest possible network that is stable and can grow on its own.
who are the first, most important users to get onto a nascent network, and why? And how do you seed the initial network so that it grows in the way you want?
Imagine a network launch as tipping over a row of dominos. Each launch makes the next set of adjacent networks easier, and easier, and easier, until the momentum becomes unstoppable—but it all radiates from a small win at the very start.
SaaS products often grow inside of companies—landing and expanding—also jumping between companies as employees share products with partner firms and consultants.
The Escape Velocity stage is all about working furiously to strengthen network effects and to sustain growth.
the Acquisition Effect is powered by viral growth, and a positive early user experience that compels one set of users to invite others into the network.
The Engagement Effect manifests itself by increased engagement as the network grows—this can be developed further by conceptually moving users up the “engagement ladder.”
And finally, the Economic Effect—which directly affects a product’s business model—can be improved over time as well, by increasing conversions in key monetization flows and ramping up revenue per user, as the network grows.
In many narratives about network effects, by the time a product has hit the Tipping Point, that’s the fairy-tale ending of the company—it’s won.
This is when a network “hits the ceiling,” and growth stalls. This is driven by a variety of forces, starting with customer acquisition costs that often spike due to market saturation, and as viral growth slows down.
The solutions are difficult—a successful product inherently comes with various degrees of spam and trolls. These are problems to be managed, not fully solved.
This dynamic drives a unique form of rivalry—“Network-based competition”—that isn’t just about better features or execution, but about how one product’s ecosystem might challenge another’s.
When you start a new product with network effects, the first step is to build a single, tiny network that’s self-sustaining on its own.
The company’s CEO, Stewart Butterfield, and his cofounders, Eric Costello, Cal Henderson, and Serguei Mourachov, would successfully pull off one of the startup industry’s most incredible reversals of fortune.
In Slack’s case, it took nearly four years to go from founding Glitch to giving up on this first product, with nearly all of the company’s funding spent and employees laid off.
In fact, I will describe to you in case study after case study that building a product with network effects can both be difficult and slow. But there is a pattern to their success that can be studied and repeated.
No, not at all. I just had friends at other companies and I would try to convince them to use us. We didn’t have any teams for demand gen, field marketing, or anything else at the time—it was just me. Sometimes it would take dozens of meetings to convince people why it was cool.
Each increase in team size required a rethinking of the design, in order to form stable atomic networks that would grow.
These companies pioneered a new style of “bottom-up” growth, where individual contributors seeded a product’s adoption within a customer company.
New products die when they flub their initial entry into the market, and their networks collapse before they even start.
Small, sub-scale networks naturally want to self-destruct, because when people show up to a product and none of their friends or coworkers are using it, they will naturally leave. What solves this? “The Atomic Network”—the smallest network where there are enough people that everyone will stick around.
However, the most important part of any early network is attracting and retaining “The Hard Side” of a network—the small percentage of people that typically end up doing most of the work within the community.
To attract the hard side, you need to “Solve a Hard Problem”—design a product that is sufficiently compelling to the key subset of your network.
