More on this book
Community
Kindle Notes & Highlights
At first glance Uber might just look like a simple app—after all, the premise was always to hit a button and get a ride. But underneath its deceptively basic user interface was a complex, global operation required to sustain the business. The app sat on a vast worldwide network of smaller networks, each one representing cities and countries. Each of these networks had to be started, scaled, and defended against competitors, at all hours of the day.
Adding more drivers to the Uber network was one of the most important levers we had to grow the business.
Too much surge, and riders would switch to competitors.
This was a senior group of executives, but the granularity and level of detail was incredible.
Those aggregate metrics were regarded as mostly meaningless. Instead, the discussion was always centered on the dynamics of each individual network, which could be nudged up or down independently of each other, with increased marketing budget, incentive spend for either drivers or riders, product improvements, or on-the-ground operational efforts.
Too much surge, and riders stop taking trips. Too little surge, and drivers start to go offline and head home after a long night.
The firm built a foundation of nerdy street cred by hiring longtime Silicon Valley founders and executives who promoted a philosophy of hands-on operating expertise.
My blog would be read by hundreds of thousands, and due to this as well as the natural serendipity of the startup ecosystem, I came to become acquainted with a broad community of entrepreneurs and builders.
Conversations with startups begin with a “first pitch” meeting, where the entrepreneurs introduce themselves, show the product, and talk through their strategy. These are pivotal meetings, because when they go well, the startup could eventually receive an investment in the millions or even hundreds of millions of dollars. It’s high stakes.
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.
While “network effects” and its related concepts were often invoked, there was no depth to the idea. No metrics that could prove if it was really happening or not.
There’s a gap between the practitioners and the rest of the business world. For practitioners who work on specific networked products, the focus is on improving the mechanics within their very particular domains.
What are network effects, really? How do they apply to your business? How do you know if your product has them—and which other products don’t? Why are they so hard to create, and how do you create them? Can you add a network to your product after the fact? How do they impact your business metrics, at the tactical level?
What happens when two networked products compete—what makes one player win over another? Why did we see big networks often
three years of research and synthesis,
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.
Based on the foundational theories of network effects, I’ve taken these lessons and put skin in the game, focusing my venture capital investing at a16z toward products that have networks at their core.
The first phase of the core framework, naturally, is called the Cold Start Problem, which every product faces at its inception, when there are no users.
launched. If there aren’t enough users on a social network and no one to interact with, everyone will leave. If a workplace chat product doesn’t have all your colleagues on it, it won’t be adopted at the office. A marketplace without enough buyers and sellers will have products listed for months without being sold. This is the Cold Start Problem, and if it’s not overcome quickly, a new product will die.
In its classic usage, a network effect describes what happens when products get more valuable as more people use them. This is a simple definition, which I will deepen the framework of in later chapters, but it’s a good starting point.
Its value depends on the connection with the other telephone and increases with the number of connections.2
Whether it’s a photo-sharing app where you’d want to see their photos, or a file-sharing service you use to access your coworkers’ latest documents, you want the right people on the network with you. It’s a simple idea, with profound implications for everything from product design to marketing to business strategy.
A successful network effect requires both a product and its network, and that was true in the age of the American Telephone & Telegraph Company, and true today. For Uber, the “product” is the app that people run on their phones, and the “network” refers to all the active users at any given time who are connecting with Uber to drive or ride. (There’s no physical wiring, in this case!)
Dropbox, Slack, and Google Suite are workplace collaboration products built from the network of your teammates and coworkers. Instagram, Reddit, TikTok, YouTube, and Twitter are networks of content creators and consumers (and advertisers!). Developer ecosystems like Android and iOS make it possible for consumers to discover and pay for apps, and the developers that build them.
When software connects people in this way, the network effect can be defined by breaking the term into its constituent parts—the “network” and the “effect.”
The “network” is defined by people who use the product to interact with each other.
It is the content uploaded by creators and the viewers that watch them—and the software platform sits in the middle, making recommendations, organizing the video with tags, recommendations, and feeds—so that the right videos are shown to the right consumers.
These networks are counterintuitive in that they connect people, but don’t own the underlying assets.
developers
The entire ecosystem stays on because the value is in bringing everyone together. That’s the magic. The “effect” part of the network effect describes how value increases as more people start using the product.
Given these definitions, how do you tell if a product has a network effect, and, if yes, how strong is it? The questions to ask are simple: First, does the product have a network? Does it connect people with each other, whether for commerce, collaboration, communication, or something else at the core of the experience? And second, does the ability to attract new users, or to become stickier, or to monetize, become even stronger as its network grows larger? Does the user face a Cold Start Problem where retention is low when there’s no other users?
The technology ecosystem is downright hostile to new products—competition is fierce, copycats abound, and marketing channels are ineffective.
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.
It’s not enough to be a good, useful app—they have to actively take away attention from other hyperaddictive apps that have been optimized over years to engage users.
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.
Although software has been easier to build, growing products has not gotten easier.
Thus, the fewer competitors the better, but predictably, this did not last. As apps learned to monetize effectively, and venture capital funding poured into the system, the advertising auctions grew more competitive.
Larger competitors are often able to copy the product, but find it difficult to capture the network.
Knowledge workers increasingly have the same “it just works” expectations on enterprise software, as they do with the apps they use at home. Increasingly, this means the enterprise is becoming “consumerized” with software that is adopted by individuals, then spread within the company’s network—with network effects.
Yet the ideas that dominated dot-com thinking still exist. The tech industry still talks about winner-take-all markets and first mover advantages, when in practice, these are myths and in practical terms have been disproven.
If you stopped reading right here in this book, you would have absorbed nearly all of the high-level strategic thinking that is commonly referenced for network effects. I covered a bit of history, defined a few bits of jargon, threw in a couple of case studies where a big network wallops a small one, a definition for Metcalfe’s Law, and some strategic implications. And yet it is not even close to enough for those in the industry who seek to create, scale, and compete using this powerful force.
user
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.
Sardines have network effects, and Allee’s curves are useful in thinking about how networks can unwind and collapse. Just as crossing the “Allee threshold” is important for a school of sardines to switch from being low/negative growth into a self-sustaining population, when you harvest the sardines more aggressively, you can push them under the threshold.
When there are very few drivers in a city, it takes a long time to get a ride—this is called having a high ETA (estimated time of arrival). As a result, conversion rates are low, because who has time to wait thirty minutes to get a ride? Thus, until you have a few dozen drivers—let’s say fifty for the sake of this example—the value to the user is nearly zero. They won’t really use the app, and drivers won’t stick around, either, so the entire network will collapse on its own. Once you pass the Tipping Point, however, things start to work. Riders start getting cars in 15 minutes, and it becomes
...more
in most cases the network effects that startups love so much actually hurt them. I call these “anti-network effects” because these dynamics are downright destructive—especially in the early stage as a company is getting off the ground.
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.
as a network grows, each new network starts to tip faster and faster, so that the entire market is more easily captured.
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. This is why we so often see the most successful network effects grow city by city, company by company, or campus by campus as rideshare, workplace apps, and social networks have done.
