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by
Andrew Chen
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March 15 - July 27, 2023
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.
In its classic usage, a network effect describes what happens when products get more valuable as more people use them.
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.
The technology ecosystem is downright hostile to new products—competition is fierce, copycats abound, and marketing channels are ineffective.
Although software has been easier to build, growing products has not gotten easier. Networked products also have strong advantages in attracting new users, by leveraging their users to refer other users—this is critical as the channels to market to potential audiences have become highly competitive.
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. I’ll talk later about Zoom, Slack, Dropbox, and other pioneers in this space, many of which have resulted in business outcomes into the many billions of dollars, as large as any consumer startup’s valuation.
Metcalfe’s Law is a simple, academic model that fails the test of real-life messiness.
The Allee effect → The Network Effect Allee Threshold → Tipping Point Carrying capacity → Saturation In the upcoming chapters, while I use the vocabulary of business—network effects, Tipping Points, and market saturation—I credit the underlying ideas to Professor Allee and his mathematical models of ecology.
Humans are the networked species. Networks allow us to cooperate when we would otherwise go it alone. And networks allocate the fruits of our cooperation. Money is a network. Religion is a network. A corporation is a network. Roads are a network. Electricity is a network.
This idea can be generalized to a wide variety of products beyond Slack. How many users does your network need before the product experience becomes good?
The solution to the Cold Start Problem starts by understanding how to add a small group of the right people, at the same time, using the product in the right way. Getting this initial network off the ground is the key, and the key is the “atomic network”—the smallest, stable network from which all other networks can be built.
The networked product should be launched in its simplest possible form—not fully featured—so that it has a dead simple value proposition. The target should be on building a tiny, atomic network—the smallest that could possibly make sense—and focus on building density, ignoring the objection of “market size.” And finally, the attitude in executing the launch should be “do whatever it takes”—even if it’s unscalable or unprofitable—to get momentum, without worrying about how to scale.
Chris Dixon, my colleague at a16z, summarized the idea in an essay titled, appropriately, “The next big thing will start out looking like a toy.” Disruptive technologies are dismissed as toys because when they are first launched they “undershoot” user needs. The first telephone could only carry voices a mile or two. The leading telco of the time, Western Union, passed on acquiring the phone because they didn’t see how it could possibly be useful to businesses and railroads—their primary customers. What they failed to anticipate was how rapidly telephone technology and infrastructure would
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The first step to launching an atomic network is to have a hypothesis about what it might look like. My advice: Your product’s first atomic network is probably smaller and more specific than you think.
It may surprise you to know that all of Wikipedia—with more than 55 million articles—was written by a small group of users. Not just small, actually, but tiny. Even though there are hundreds of millions of users, there are only about 100,000 active contributors per month, and when you look at the small group of writers who make more than 100+ edits in a month, it’s about 4,000 people. As a ratio, it means that active contributors represent only 0.02% of the total viewer pool.
But spending hours to learn a new TikTok dance is difficult, and not everyone can do it. The more difficult the work needed to be part of the hard side of a network, the smaller the percentage of users who will participate.
Social feedback, status, and other community dynamics encourage editors to keep creating content. Wikipedians, as they call themselves, can show their expertise in a topic by maintaining comprehensively written pages, and people within the community will thank and appreciate them. This provides status. They can make edits to correct others, which offers another form of status and satisfaction. There’s teamwork and a feeling of camaraderie, which create bonds that retain users over months and years. Steven Pruitt, the wildly prolific Wikipedian, might have just a normal job during the day, but
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To solve the Cold Start Problem for marketplaces, often the first move—as it was for Uber—is to bring a critical mass of supply onto the marketplace.
Thus the order of operations, at least for most consumer-facing marketplaces, is “supply, demand, supply, supply, supply.” While supply might be easy to get onto the network early on through subsidies, eventually it will become the bottleneck. The hard side of a network is, by definition, hard to scale.
The key insight in the stories of Homobiles or Tinder is—how do you find a problem where the hard side of a network is engaged, but their needs are unaddressed? The answer is to look at hobbies and side hustles.
When the product concept and value is simple to describe, it makes them easier to spread from user to user, much like the “meme” coined by noted biologist Richard Dawkins in one of my favorite books, The Selfish Gene. You can copy and paste a Zoom link—it’s just that easy.
Some products, like Dropbox, are initially envisioned as consumer companies until later they become so popular in workplaces that the strategy shifts to the enterprise. Others, like Slack, are enterprise products started by entrepreneurs with consumer software backgrounds.
In recent research by a16z examining the hottest “bottom-up” enterprise startups, most were started by founders from consumer companies like Airbnb, Uber, Yahoo, and so on. The same skills that can create successful networked products in the consumer sector can be applied toward the enterprise categories.
The Cold Start Problem doesn’t need to just be solved once; it needs to be dealt with over and over. And as soon as a team can build one of these stand-alone networks, they’re ready to build more—many more—and try to take over the entire market.
Only once you get scale do the broader network effects we desire—viral growth, increased stickiness, and strong monetization—start to kick in.
This second phase was the Tipping Point—Tinder had hit a point of repeatable growth, since once the team knew how to create one atomic network, and a second, then it was repeatable from there. These growth tactics continued to scale, and the team iterated to make them more effective.
“Come for the Tool, Stay for the Network” strategy. Take Dropbox, for instance, which is initially adopted by many people for file backup and keeping files synced up between work and home computers—this is the tool. But eventually, a more advanced and stickier use case emerges to share folders with colleagues—this is the network.
the most important reason to have invites in a product with network effects. Invite mechanics work like a copy-and-paste feature—if you start with a curated network, and give them invites, that network will copy itself over and over automatically.
However, invite-only launches have been a key feature of many products precisely because for networked products, there are huge advantages. It allows the early network to gel as a community, develop a high density of connections, and grow organically via virality.
The Robinhood mechanic asked wait list users to tweet or post to social media in order to jump ahead, ultimately bringing a million users in before a widespread release. Another variation of this is to ask users on the wait list to fill out detailed information about themselves, including their potential use cases, giving the teams a way to let a curated, small trickle of users in to form the initial network.
For networked products, the curation of the network—who’s on it, why they’re there, and how they interact with each other—is as important as its product design. Starting with a deliberate point of view on who’s best for your network will define its magnetism, culture, and ultimate trajectory.
Come for the tool, stay for the network” is one of the most famous strategies for launching and scaling networks. Start with a great “tool”—a product experience that is useful even for one user as a utility. Then, over time, pivot the users into a series of use cases that tap into a “network”—the part where you collaborate, share, communicate, or otherwise interact with other users.
A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.” The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.
But for networked products, in the earliest stages, sometimes it makes sense to spend—often wildly—to pay up for growth. The goal is to get the market to hit the Tipping Point, driving toward strong positive network effects, and then pull back the subsidies.
In a page ad, I inserted a coupon, good at any store for a ten-cent can. We paid the grocer his retail price. For three weeks we announced that this ad would appear. At the same time we told the story of Van Camp’s Evaporated Milk. We sent copies of these ads to all grocers, and told them that every customer of theirs would receive one of these coupons. It was evident that they must have Van Camp’s Milk. Every coupon meant a ten-cent sale which, if they missed it, would go to a competitor. . . . The result was almost universal distribution, and at once.41
The hard side is the default place to start, and Uber began—similarly to Van Camp’s Milk—with a subsidy to the driver side. The
But once a team can reliably launch a product’s initial atomic networks, financial levers can rapidly accelerate the speed in which the market hits the Tipping Point.
it was the crucial partnership with IBM that helped Microsoft reach a Tipping Point to ultimately control the most valuable network in the computer industry. They had to create a custom product to get it started, but used that work to parlay a presence on billions of PCs, at a time when network effects were mostly unknown and underappreciated.
Once these initial networks are formed, the Flintstoning techniques evolve toward automation as the momentum builds. The goal is just to manually fill in critical parts of the network, until it can stand on its own.
The downside of Flintstoning is that it feels like it’s overly manual. You start by throwing people at the problem, but can it scale? I argue that it scales further and longer than you might think.
Famously, PayPal built bots that would automatically buy and sell items on eBay, but insist on transacting only with PayPal—it became a way to convince eBay sellers to sign up for the service.
In the end, you might ask—did Uber Ice Cream really help? As an individual stunt, it may not have had a massive impact on the company. But I argue that within the framework of taking a market from zero to the Tipping Point, these types of quick, clever tactics played a key role in getting markets off the ground. Most important, Uber created a system to quickly identify, execute, and iterate on these concepts—it was supported by an entrepreneurial team culture, robust software tooling, and an understanding that each city would be its own Cold Start Problem.
many productivity products begin by launching within online communities—like Twitter, Hacker News, and Product Hunt—where dense pockets of early adopters are willing to try new products.
B2B products have started to emphasize memes, funny videos, invite-only mechanics, and other tactics traditionally associated with consumer startups. I expect that this will only continue, as the consumerization of enterprise products fully embraces meme-based go-to-market early on, instead of leading with direct sales.
most common types of advice we give at Y Combinator is to do things that don’t scale. . . . The most common unscalable thing founders have to do at the start is to recruit users manually.
There are two reasons founders resist going out and recruiting users individually. One is a combination of shyness and laziness. They’d rather sit at home writing code than go out and talk to a bunch of strangers and probably be rejected by most of them. But for a startup to succeed, at least one founder (usually the CEO) will have to spend a lot of time on sales and marketing.45
Any product can buy Facebook or Google advertising, for instance, to attract new users, but only networked products can tap into viral growth—the ability for users in its network to tell others in their own personal networks.
Acquisition Effect are oriented around viral growth: referral features that reward users when they invite others, tapping into contacts to create suggestions for who to add to an app, and improving conversion along the key moments in the invitation experience.
“Engagement Effect” describes how a denser network creates higher stickiness and usage from its users—it is a more specific form of the classic description of network effects that I covered at the beginning of the book, “the more users that join the network, the more useful it gets.” However, the classic definition can be refined to include the underlying system that drives the value—use cases and “loops” that define how users derive value when engaging with a product—as well as the specific metrics that increase with a denser network.
Retention curves, often one of the most important visualizations of how long people are sticking around, can be improved as stickier use cases emerge.
