Get the newsletter · Featured · Recent · 🎉 New book: The Cold Start Problem

Solve a Hard Problem (Tinder). Chapter 8 of my upcoming book, The Cold Start Problem

Hi readers,

I’m so, so excited that my upcoming book is dropping in one week, on December 7!! I’ve been working on it for years, and am thrilled that it’ll finally be out.

The Cold Start Problem, as the book is called, is about the secret that drives many of tech’s most successful products. It’s the story of how messaging apps, marketplaces, workplace collaboration tools, multiplayer games, all share a common thread of being products that connect people with each other. And launching and scaling these products requires a mastery of “network effects,” one of the most-used but misunderstood jargon terms in the industry. My book aims to change that, systematically laying out concepts for startups and folks launching new products to consider.

One of the crucial concepts to understand this is the concept of an “Easy Side” and a “Hard Side” of a network.

To describe this more detail, below, I’ve included a full-length chapter called “Solve a Hard Problem” focusing on the idea that every network has an “easy side” and a “hard side.” Here’s how I define each side:

There are usually a minority of users that will create disproportionate value and as a result, they will have disproportionate power. This the “Hard Side” of your network. They do more work, contribute more to your network, but are that much harder to acquire and retain. For social networks, these are often the content creators that generate the media everyone consumes. For app stores, these are the developers that actually create the products. For workplace apps, these are the managers that author and create documents and projects, and who invite coworkers to participate. For marketplaces, these are usually the sellers and providers who spend their entire day attracting users with their products and services.

In order to make your product and its network launch successfully, it’s not enough to build a killer set of features. It’s also important to solve something crucial for the “hard side” of your network. Cheekily, I call this, “Solving a hard problem” — and I use the example of Tinder to illustrate this concept.

Hope you enjoy!



Chapter 8: Solve a Hard Problem (Tinder)
The hardest problem to solve in creating the first atomic network is, well, the hard side. Focus on attracting content creators to a new video platform, or sellers to a new marketplace, or the project managers inside a company to a new workplace app. The other side of the network will follow. The question is, how?

The answer is by building a product that solves an important need for the hard side of the network. Let’s look at online dating, which evolved over time to better solve the matchmaking problem that has bedeviled humanity since the beginning of time. Dating apps are network effects-driven products that grow city-by-city, and the more folks that join the network, the better the chances that people will find matches. But at the beginning of the product category, the experience was terrible, especially for the hard side of the network.

The problem of too many love letters
Online dating was invented at the beginning of the web, in the early 1990s. They were designed like newspaper classifieds, where men and women browse large databases of profiles, and could message each other if they were interested. Match.com and JDate were successful pioneers in this category, which worked despite its flaws. The classifieds-based design created a poor product experience since the popular members — particularly women — would become overwhelmed with a large number of messages, and they would struggle to reply. At a bar or club, potential suitors might be dissuaded if they saw a line of people waiting to talk to an attractive man or woman, but online, there was no such signal. So in turn, the experience for everyone else also ended up poor, because it seemed like no one would write them back.

The lesson is, unsurprisingly, that attractive people — particularly women — are the Hard Side of the online dating network. A few years later, the next generation of online dating would emerge, led by products like eHarmony and OKCupid. These products used quizzes and matching algorithms so that the system could decide who got which matches, and how often. This ensured women got fewer messages, and hopefully more of the right ones. And the men got more replies too, so that it didn’t feel like it was devolving into a copy-paste messaging exercise.

It wasn’t until 2012, at the beginning of the explosion in mobile apps, that yet another generation of dating apps would emerge. These apps, exemplified by Tinder, would innovate even further for the hard side of the network. I had a chat with Tinder’s co-founder, Sean Rad, about how Tinder innovated on the previous generation of products. He described the combination of new ideas:

The older dating sites made it feel like you were doing work, like you were inside the office. You’d go and do work emails during the day, then go home and write more messages at night. Only to prospective dates rather than work colleagues. Tinder was different — it made dating fun. You could sign up without filling in a bunch of forms. It’s visual, you just swipe back and forth, and you could take 5 minutes to do it while you were waiting in line or something like that. It’s a form of entertainment.[^1]

The other problem was how to wade through all the replies. In real life, you’re often introduced to potential romantic partners through friends, or you had a shared context — like work or school — that helped filter. For online dating, the most attractive members of a network needed some additional signals to help sort through their matches. Tinder did this by integrating with Facebook, and Sean also explained how the app was able to build trust:

Tinder started by making everyone connect their Facebook, so that we could show the number of mutual friends you had, which built trust. We also made it so that you could only be matched with people who lived around you — we used the GPS location from your phone, which was new. These were people with mutual friends living around you, the sort of person you might meet in real life! Connecting with Facebook also made sure you would never be shown to friends, or vice versa, if you were worried about that. This all created trust. Tinder also had built-in messaging so that you didn’t have to give out your number. If the conversation didn’t go anywhere, you could just unmatch without worrying about getting harassed.

And of course, the mechanic of swiping itself is a way to make sure people don’t feel overwhelmed. Whereas men tend to swipe right (that is, to indicate interest) on about half of women’s profiles — about 45% to be exact — the ladies in the product swipe on only 5% of profiles they see. As a result, women mostly match with the guys they select. However, if they feel like they are in too many conversations, they can stop swiping for a while and just focus on the messaging their existing matches. All of these insights made Tinder a much better experience for most important side of their network, solving one of the most important obstacles in the Cold Start Problem.

The Hard Side for marketplaces is usually the supply side
Marketplaces tend to revolve around its sellers. I’ve seen the difficulty of managing the hard side for rideshare first-hand, where drivers are the ones selling their time and effort in the market. For Uber, in any given market, so-called “Power Drivers” constitute 20% of the supply but create 60% of the trips. These are some of the most valuable users on the planet, as they are the core of Uber’s business.

Uber’s drivers are just one example of a broader set of workers that drive most marketplace companies. For marketplaces, the hard side is usually the “supply” side of the network, which refers to the workers and small businesses who provide the time, products, and effort and are trying to generate income on the platform. They use digital marketplaces as a side hustle, selling collectibles or coaching sessions, or otherwise. They do this often as an alternative to hourly jobs, of which there are nearly 80 million in the US. These are folks often living in the middle of the country, who work in hourly retail jobs that turn over 100% year over year, and are struggling for additional income. Marketplace startups often provide these opportunities to this group.

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. For a marketplace like eBay, you start with sellers of collectibles. For a marketplace like Airbnb, you might start with people with a few extra rooms in their place. For a social platform like YouTube, it might be video creators. For a more esoteric category, like Github, it’s helpful to bring on some prominent Open Source projects and key developers. But once the supply has arrived onto the network, it’s time to bring in demand — the buyers and users that will form the bulk of the network. Once that’s working though, it becomes all about supply again. 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.

Uber had to get creative to unlock its Hard Side. Initially, Uber’s focus was on black car and limo services, which were licensed and relatively uncontroversial. However, a seismic shift soon occurred when rival app Sidecar innovated in recruiting unlicensed, normal people as drivers on their platform. This was called the “peer-to-peer” model that created millions of new rideshare drivers, and was quickly copied and popularized by Lyft and then Uber. Jahan Khanna, cofounder/CTO of Sidecar spoke of its origin:

It was obvious that letting anyone sign up to a driver would be a big deal. With more drivers, rides would get cheaper and the wait terms would get shorter. This came up in many brainstorms at Sidecar, but the question was always, what was the regulatory framework that allows this to operate? What were the prior examples that weren’t immediately shut down? After doing a ton of research, we came onto a model that had been active for years in San Francisco run by someone named Lynn Breedlove called Homobiles that answered our question.[^2]

It’s a surprising fact, but the earliest version of the rideshare idea came not from an investor-backed startup, but rather from a nonprofit called Homobiles, run by a prominent member of the LGBTQ community in the Bay Area named Lynn Breedlove. The service was aimed at protecting and serving the LGBTQ community while providing them transportation — to conferences, bars and entertainment, and also to get healthcare — while emphasizing safety and community.

Homobiles had built its own niche, and had figured out the basics: Breedlove had recruited, over time, 100 volunteer drivers, who would respond to text messages. Money would be exchanged, but in the form of donations, so that drivers could be compensated for their time. The company had operated for several years, starting in 2010 — several years before Uber X — and provided the template for what would become a $100 biillion+ gross revenue industry. Sidecar learned from Homobiles, implementing their offering nearly verbatim, albeit in digital form: Donations based, where the rider and driver would sit together in the front, like a friend giving you a ride. With that, the rideshare market was kicked off.

Nights and weekends
The key insight in the stories of Homobiles or Tinder is — how do you find a problem where the Hard Side a network is engaged, but their needs are unaddressed? The answer is to look at hobbies and side hustles.

There are millions of content creators, app developers, marketplace sellers, and part-time drivers that power the hard side of networks. They are smart, motivated, early adopters who are finding opportunities to make themselves useful. They are the developers behind the Open Source movement who have built Linux, WordPress, MySQL, and many of the other technologies that underpin the modern internet. They are the millions of eBay sellers that have created jobs and companies by buying and selling goods that people want. For photo sharing and messaging products like Instagram and YouTube, they stem from the countless amateur photographers and videographers that like to record travel, special occasions, architecture, beautiful people, and everything else.

What people are doing on their nights and weekends represents all the underutilized time and energy in the world that if put to good use, can become the basis of the hard side of an atomic network. Sometimes the army is built on people with excess time, but sometimes it is built on people with underutilized assets as well. Rideshare networks, for example, fundamentally depend on the underutilization of cars, which generally sit idle most of the time besides the daily commute and the occasional errand. Airbnb is built on the underutilization of guest bedrooms, second homes, combined with the time and effort of the hosts. Craigslist and eBay are built on letting people sell their “junk” – the stuff that people don’t value anymore – to new owners who might value them more.

Usually the Hard Side will continue to use Airbnb or TikTok because that’s where the demand is, and thus, are locked into the positive network effects on those platforms. However, the trick is to look closer — it is better to segment the Hard Side of the network and figure out who is being underserved. Sometimes this is a niche, like a passionate sub-community of content creators for makeup or unboxing that might be better served with additional commerce features. It could be a low-production quality, amateur part of the community, like those who are doing #whateverchallenge of the week, who would benefit from basic video editing tools. For networks that are derived from underutilized assets, it might be the niche of who like having new side-hustles every weekend to make money online. Or perhaps there is a new platform shift coming soon that feels niche, but might upend the entire ecosystem.

The idea is to start with these underserved segments — which may not be very attractive customers on their own, and to apply Clayton Christensen’s disruption theory. New products often disrupt markets by starting on the low-end of the market, providing “good enough” functionality, and growing them there. They use a different technology foundation that allows them to eventually roll up the market from low-end into the medium, and eventually into the core market of the incumbents. Or, there has been a recent trend in the opposite — products like Uber and email company Superhuman, where you start at the top of the market as a luxury product, and work your way down.

When we combine disruption theory with that of network effects, it makes even more sense – atomic networks often start at the low end in terms of functionality, in a niche market. But once they establish an atomic network, then often the Hard Side of the network is willing to extend their offerings and services to go into the next vertical — attracting an incrementally higher-end opposite side, which in turn, spurs it even further. Airbnb may have started with airbeds, but the same hosts that might be willing to rent out an airbed might be willing to rent out their room, or their entire apartment. This changes the potential nature of the supply in the marketplace, attracting a higher end demand-side, which in turn attracts higher-end inventory. No wonder today, Airbnb hosts a wide variety of high-end offerings, from luxury penthouses to boutique hotel rooms. In that way, network effects can play a key role in disrupting new industries — creating the momentum for a low-end atomic network to slowly build out into higher-end offerings over time.

The Hard Side of dating apps
Let’s go back to online dating for a moment — when viewed as a networked products, the apps bring together two sides in a romantic context. In that way, Tinder, Bumble, Match, eHarmony, HotOrNot, and the line of dating apps reflects something that existed as a human behavior for eons. It’s long been a hobby of amateur matchmakers to introduce their single friends to each other, both demonstrating a deep need for this service as well as the skills needed to make it successful. In the the modern age we have digitized dating, using algorithms to match people, dating profiles so that thousands of profiles can be swiped through, and real-time messaging to make communication easier.

All of these improvements are great for any product, but most importantly, they help attract and maintain the most desirable members of a dating network — the Hard Side. The matchmaking algorithms need to find them equally attractive matches, and the profiles they browse through must help them decide between princes and frogs. The in-app messaging experience has to cater to their needs, with an option to get out of conversations quickly if needed. Without these types of features, desirable people will churn from the product, degrading the network and worsening the experience for everyone else. The best products have to solve a problem for the Hard Side of the network.

While dating apps — and really, all networked products — need to find a value proposition for the Hard Side of the network, what about all the other users? Well, it’s a high bar, but you need to nail the experience for the rest of the network. You need to build a “Killer Product” that sits at the heart of any network.

PS. Get new updates/analysis on tech and startups

I write a high-quality, weekly newsletter covering what's happening in Silicon Valley, focused on startups, marketing, and mobile.

I’m on the Tim Ferriss podcast — talking my new book, growth hacking, metaverse, creator economy, and more

Previewing a full chapter of The Cold Start Problem — my upcoming book dropping in December

Why the best way to drive viral growth to increase retention and engagement

What to look for when you’re hiring a Head of Growth

Why premature scaling fails: The Traction Treadmill

What today’s social apps can learn from Web 2.0, the social network revolution from 15 years ago

The next generation of the SF and LA tech ecosystem

My first book, The Cold Start Problem. Plus Clubhouse, and more. It’s 2021, and I’m back!

The Adjacent User Theory

My top essays/tweetstorms in 2019 on product/market fit, investing, KPIs, YouTubers, and more

“Is your startup idea taken?” — and why we love X for Y startups

The Passion Economy (Guest essay by Li Jin)

10 lessons from a serial entrepreneur – Justin Kan, Atrium, YC, and Twitch

28 ways to grow supply in a marketplace — by Lenny Rachitsky, ex-Airbnb

Why startups are hard — the math of venture capital returns tells the story

The Podcast Ecosystem in 2019 – a16z’s 68 page analysis

The Dumb Idea Paradox: Why great ideas often start out by sounding dumb.

Announcing Pietra and a16z — my first ex-Uber investment!

What do you look for an investment? How long should a founder be without salary? And other Q&A

2018 essay collection on growth metrics, marketplaces, viral growth in the enterprise, and more (PDF included)

Silicon Valley network effects, OKRs for your personal life, and more: Podcast Q&A with Product Hunt

Consumer startups are awesome, and here’s what I’m looking for at a16z (70 slide deck)

What’s next for marketplace startups? Reinventing the $10 trillion service economy, that’s what.

How to build a growth team – lessons from Uber, Hubspot, and others (50 slides)

The red flags and magic numbers that investors look for in your startup’s metrics – 80 slide deck included!

a16z Podcast: When Organic Growth Goes Enterprise

a16z Podcast: Why paid marketing sucks, Network effects, Viral Growth, and more

Why “Uber for X” startups failed: The supply side is king

The Power User Curve: The best way to understand your most engaged users

DAU/MAU is an important metric to measure engagement, but here’s where it fails

Required reading for marketplace startups: The 20 best essays

Conservation of Intent: The hidden reason why A/B tests aren’t as effective as they look

The Startup Brand Fallacy: Why brand marketing is mostly useless for consumer startups

The Scooter Platform Play: Why scooter startups are important and strategic to the future of transportation

The IRL channel: Offline to online, Online to offline

How startups die from their addiction to paid marketing

Podcast Q&A: Dropbox’s viral growth, Uber’s tricky funnels, and future growth channels

Update: I’m joining Andreessen Horowitz!

Hello 2018! Books, essays, and more from the past year.