Jan 2, 2019 · 48m · a16z
a16z Podcast | Getting Network Effects
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In this a16z podcast episode, venture capitalists Jeff Jordan and Anu Hariharan alongside host Sonal Chokshi explore the dynamics of network effects, distinguishing them from viral growth, brand power, and economies of scale. They examine bootstrapping tactics, monetization timing, key evaluation metrics, and real-world case studies like Facebook, OpenTable, and Airbnb to illustrate how startups build defensible market value.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The host holds 10.6% of the talking time here. How this is scored →
The host as informed peer 2.1 Guest teaching 4.0 Guest disagreement 1.5 The host pushing back 1.2
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 9:03 Jeff Rejects Accidental Growth Hack Premise
Jeff directly corrects the host's suggestion that founders stumble onto growth hacks accidentally, asserting that successful entrepreneurs rigorously theorize bootstrapping strategies.
Hardest push from the host ▶ 23:49 Host Refuses San Francisco Bubble GeneralizationHost Sonal explicitly challenges the guests, refusing to accept that early success in affluent, convenience-focused San Francisco proves broader network scalability.
Biggest teaching moment ▶ 18:00 Anu Differentiates Viral Growth from True Network RetentionAnu clearly educates the room by illustrating why Angry Birds' high download counts lacked network retention, comparing it to genuine network metrics.
The host holds their own ▶ 23:49 Host Hits Back on Silicon Valley Market BiasHost Sonal demonstrates keen analytical foresight by interrupting the guests' Silicon Valley narrative to highlight geographic sampling bias.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Viral Growth Versus Network Effects: Facebook Case Study | 3 | 4 | 1 | 1 | Host Sonal introduces the core debate surrounding viral growth versus true network effects and prompts guests for definitions. Anu and Jeff clarify that viral adoption speed does not equal long-term network value, citing Facebook retention metrics. | |
| Bootstrapping Networks and Facebook's Early Hacks | 2 | 4 | 1 | 1 | Host asks how startups bootstrap network effects before achieving scale. Anu details Facebook's disciplined Harvard rollout and friend-recommendation heuristics while Jeff describes Zuckerberg's directory hack. | |
| Solving the Chicken-and-Egg Problem: The OpenTable Model | 2 | 5 | 3 | 1 | When host asks if growth hacks are stumbled upon by accident, Jeff firmly reframes the question, asserting that founders explicitly formulate theories to solve the chicken-and-egg problem. He then outlines OpenTable's tool-first strategy. | |
| Local versus Global Expansion and WhatsApp Case Study | 3 | 4 | 1 | 2 | Host inquires about balancing local density versus fast global expansion. Guests compare OpenTable's hyper-local rollout against eBay's instant national utility and WhatsApp's early focused testing in San Jose. | |
| Overcoming Cold Start in Unconventional Markets: Airbnb & Pinterest | 3 | 4 | 2 | 1 | Host explores how platforms build networks around unprecedented user behaviors like home sharing. Anu explains Airbnb's early struggles and cereal box funding while Jeff debunks the overnight success myth. | |
| Evaluating Network Effect Metrics: Facebook, Angry Birds, and Medium | 3 | 5 | 1 | 1 | Host asks to examine concrete metrics and data slides. Anu contrasts Facebook's rising DAU/MAU curve against Angry Birds' viral decay and Medium's non-viral direct traffic retention. | |
| Micro-Indicators of Network Effects and Investor Signals | 2 | 4 | 1 | 1 | Jeff explains how investors evaluate early micro-indicators like San Francisco market productivity for OpenTable or specific collectible searches on eBay to predict macro network effects. | |
| Testing Geographic Scalability Outside Silicon Valley | 6 | 3 | 2 | 7 | Host pushes back directly against relying on San Francisco market metrics, arguing Silicon Valley is an affluent bubble. Jeff validates the challenge and discusses testing geographic scalability using cases like Cherry, Uber, and Instacart. | |
| Paid Marketing versus Organic Growth in Network Businesses | 3 | 4 | 1 | 1 | Host asks about the role of paid user acquisition versus organic growth in network businesses. Guests note that true network powerhouses like Facebook, WhatsApp, and OpenTable spent virtually zero on paid marketing. | |
| Monetization Strategies, Timing, and the Growth False Dichotomy | 3 | 4 | 3 | 1 | Host asks when startups should begin monetization. Jeff forcefully attacks the false dichotomy between growth and revenue, arguing marketplace businesses should prove value capture early. | |
| Q&A: OpenTable Competition and Surviving Economic Downturns | 0 | 4 | 1 | 0 | An audience member asks about OpenTable's competition and software feature wars. Jeff explains how surviving the dot-com funding freeze allowed OpenTable to run unopposed for five years. Host is silent during Q&A. | |
| Q&A: White-Label Utilities versus Branded Marketplaces | 0 | 4 | 1 | 0 | An audience member asks about white-labeling software versus maintaining a consumer brand. Jeff clarifies that white-label utilities lack network defense whereas branded marketplaces sell high-value demand. Host is silent. | |
| Q&A: Examining Brand Value and Sarnoff's Law | 0 | 4 | 2 | 0 | An audience member asks if brand popularity constitutes a network effect. Guests present Sarnoff's law and discuss how brand cycles in fashion and media differ from true network defensibility. Host is silent. | |
| Q&A: Data Network Effects and B2B Feedback Loops | 0 | 3 | 1 | 0 | An audience member asks about data network effects and user contributions. Guests explain how aggregated data loops power algorithmic personalization and B2B logistics optimizations. Host is silent. |
Statements from this episode (32)
Jeff Jordan: Network Effect Businesses Are Prone to Monopolies
“Businesses with network effects typically are very defensible and often are prone to monopoly”
Hariharan: Facebook spent zero dollars on user acquisition in its early years
“For the longest time, even to this day, they hardly spend money on acquiring users, right? And especially in the initial years, they spent zero dollars.”
Hariharan: Facebook's DAU/MAU ratio rose to 57% in its first 18 months
“The one metric that he really focused on in the first two years was Retention, and the way he measured retention was daily actives by monthly actives, and if you see their chart in the first 18 months, it kept going from 52% to 55% to 57%.”
Jordan: 53% of all historic Facebook sign-ups were active daily in 2006
“53% of everyone who had ever signed up for the service was active the day before, and that, by the way, has only grown as, you know, the, their installed base age, it gets bigger in ages, that number's grown”
Hariharan: Facebook penetrated 80% of US colleges in 18 months
“They did penetrate almost 80% of the colleges in 18 months.”
Hariharan: Connecting 10 friends in 14 days drove early Facebook retention
“If someone was connected to 10 friends in 14 days, they were much likely to return to Facebook.”
Jordan: Zuckerberg pre-populated Facebook by hacking Harvard house directories
“He hacked the directories in each of the houses, and so he pre-populated everyone on the Harvard campus into Facebook and then let people claim their identities, but you could then friend, it was populated when the first person showed up.”
Jordan: Virtually every marketplace relies on growth hacks to start
“Virtually every marketplace we can think of did some, had some hack or hacks that enabled them to solve the impossible chicken and egg problem.”
Jordan: OpenTable bootstrapped by selling $200 standalone restaurant software
“They build a suite of tools that they charge 200 dollars for and laboriously rolled out one restaurant at a Time throughout, you know, the country that had enough utility that a restaurant was saying, okay, I'll adopt that in the absence of a network.”
Jordan: OpenTable sales productivity grew from 3 to 20 restaurants monthly
“And so the sales rep that initially in San Francisco and New York sold three restaurants a month, The same reps, in most cases, seven, eight years later, were selling 20.”
Jordan: Restaurant marketplace network effects do not translate across cities
“By the way, that was city by city, so the fact that you had sufficient Restaurants in San Francisco meant nothing in Miami, so you started again in Miami, and in Tokyo, and in, you know, in Munich, and it's a slow roll-up.”
Anu Hariharan: Airbnb took almost 36 months to build critical mass
“It almost took 36 months for them to build critical mass and see network effect.”
Jeff Jordan: Pinterest spent two to three years finding product-market fit
“The first Ben Silverman at Pinterest, nothing happened there for two, three years. Nothing. He was just, he just kept working for product market fit, you know, with his dozens of users and then a hundred users or whatever.”
Hariharan: Medium is the primary traffic source for non-viral tail posts
“For the non-viral posts, medium is actually the biggest source for the traffic, which was, you know, early indicators that they are able to match.”
Jeff Jordan: My entire OpenTable diligence focused on San Francisco metrics
“And so in OpenTable, the entirety of my diligence on the company was, show me San Francisco. You've been in San Francisco the longest. Show me how the key metrics are going in San Francisco.”
Jordan: Benchmark's Bob Kagle invested in eBay after finding duck decoys
“And it turns out the venture guy who made the investment at eBay was Bob Cagle at Benchmark Capital. Bob, who happened to come from Michigan, and there's a craftsman in Michigan who does really beautiful, and I'll remember the name, decoy ducks for hunting. It…”
Jeff Jordan: Local Startups Must Prove Scalability Outside Home Market
“In local businesses, we typically look that they can replicate it in a market outside of the market they live in. Because that kind of suggests scalability.”
Hariharan: Point-to-point ride sharing relies on scale economies, not network effects
“So I would say the general ride sharing, we think it's more supply side economies of scale. And what that means is the more drivers you have on the platform, you know, you can make sure that you get a good quality driver within five minutes, but that's where i…”
Hariharan: Lyft Line and UberPool create true network effects
“However, if you look at lift line and Uber pool, I think those could have network effects because think of this, you're a rider, you want more riders using those because then you can share a ride to San Francisco from Palo Alto and therefore have a cheaper rid…”
Jordan: Over half of Instacart deliveries are via direct grocer partnerships
“They went back to the same grocers and are cutting deals, and now I believe well over half of their entire deliveries are not marked up. They're through deals with grocers like Whole Foods”
Hariharan: Airbnb paid to acquire supply from its early days
“I think for Airbnb, supply was really hard. So they were, they spent dollars right from the early days to acquire the host.”
Jordan: Premier networks like Facebook and WhatsApp skip paid user acquisition
“A lot of the best networks that are platformed here aren't spending anything on, on acquisition. I mean, it's just, it's kind of an interesting thing in marketplace. Facebook, no. WhatsApp, no. Medium, no. You know, just OpenTable, no. Just, you know, just, th…”
Jordan: eBay was once the largest digital ad spender globally
“EBay, I was the biggest advertiser in the world on digital advertising in the world for a number of years.”
Jordan: Portfolio companies cut spending without slowing top-line growth
“We've had a couple companies that Shifted in the current capital environments and the uncertainty there shifted from growth at all costs to, you know, kind of smart growth mode or whatever you want to call it. The amazing thing, the profitability improved dram…”
Jordan: Marketplace startups must demonstrate early monetization to prove value
“I think in a marketplace business, if there's money flowing through it, You should be able to demonstrate you can capture some of it early on, or, I mean, if not, I'm trying to figure out if you're actually adding value to both sides of the marketplace and the…”
Jordan: OpenTable achieved network effects only because its direct competitors folded
“Open table got raised enough money and spent it so slowly they were able to survive. The two direct competitors didn't open table ran unopposed for five years, which is the only reason they were able to get to their network effect.”
Jeff Jordan: Forcing website redirects was OpenTable's primary growth hack
“The other was we forced a redirect from the Morton's website to the OpenTable, Morton's page on OpenTable to create the brand awareness, and that forced off, that forced redirect, the fact that we Converted restaurant people searching on Google for Morton Chic…”
Jordan: Branded marketplaces are valued higher than white-label software
“So, and then branded marketplaces, we would value much more highly than white label, because white label becomes, you're selling a utility, you're selling software, and the marketplace standpoint, you're selling customers.”
Anu Hariharan: Brands scale under Sarnoff's law, not Metcalfe's law
“So for brands, we actually think it's more like Sarnav's law. You know, some people argue, yes, it has Some network effect, but not directly incredibly value to the user, which is why the value is not as steep as Metcalfe's law, for example.”
Jeff Jordan: Fashion and brands face dis-economies at high market saturation
“Once everyone's wearing Nikes, people stop wearing Nikes, so you're kind of I worked at Disney when it was a long time ago, it was very, very hot on the consumer product side, and, you know, we couldn't sell enough, and then five years later, you can't give it…”
Jordan: eBay transaction volume dropped 40-50% overnight following 9/11
“Our volume literally just dropped 40, 50% overnight. New users stopped completely. It was a big divot in the business for two or three or four weeks, and then everything popped back. We never regained the lost users we had during that month.”