Jan 2, 2019 · 1h 0m · a16z
a16z Podcast | Not all Network Effects Are Created Equal
Fred Benenson · 16m spoken Sonal Chokshi · 12m spoken James Currier · 10m spoken Jenny Lee · 10m spoken Anu Hariharan · 6m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
This dual-part a16z podcast episode analyzes the strategic mechanics, taxonomy, and defensibility of network effects in technology, followed by an exploration of the technical governance, diversity politics, and cultural impact of digital emojis.
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 21.7% of the talking time here. How this is scored →
The host as informed peer 4.5 Guest teaching 4.5 Guest disagreement 1.5 The host pushing back 2.4
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 13:40 James refutes equating bandwagon effect with brand
James directly corrects Sonal's premise that bandwagon effects are identical to brand defensibility, emphasizing that bandwagon social adoption happens prior to brand establishment.
Hardest push from the host ▶ 13:40 Sonal challenges bandwagon effect as just brandSonal actively interrupts James's taxonomy breakdown to challenge whether his new category of bandwagon network effect is simply traditional brand defensibility.
Biggest teaching moment ▶ 24:56 Jenny details Unicode Consortium voting governanceJenny educates Sonal on the corporate inner workings of Unicode, revealing that twelve voting members pay $18k annually to control emoji approvals.
The host holds their own ▶ 2:31 Sonal clarifies Metcalfe's Law formulation and originsSonal demonstrates expert technical knowledge by correcting the mathematical description of Metcalfe's Law and noting that George Gilder originally articulated the formulation.
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 |
|---|---|---|---|---|---|---|
| History and Early Evolution of Network Effects | 6 | 4 | 1 | 2 | Sonal demonstrates solid context by correcting the mathematical definition of Metcalfe's Law and noting that George Gilder originally described the square formulation. The guests build on this history smoothly without friction. | |
| Four Defensibilities and Technology-Native Networks | 5 | 3 | 2 | 2 | Sonal contributes Doug Engelbart's observation regarding silicon connectivity. James offers a mild reframing on what native technology connection means compared to Anu's mobile-focused explanation. | |
| Taxonomy and Nine Types of Network Effects | 4 | 5 | 1 | 3 | The guests educate the host on their refined 9-type taxonomy of network effects. Sonal asks key clarifying questions regarding why granular taxonomy matters for product strategy. | |
| Bandwagon Effects, Influencers, and Network Launch Playbooks | 5 | 5 | 3 | 5 | Sonal challenges James by claiming bandwagon effects are just traditional brand defensibility. James directly counters, explaining that bandwagon adoption happens for social reasons before a brand is established. | |
| Tipping Points and Why Networks Fail: MySpace vs. Facebook | 4 | 5 | 2 | 3 | James reframes MySpace's collapse away from simple competition to a failure of utility and real identity compared to Facebook. Sonal probes into why capped networks like Path struggled. | |
| Virality Versus Network Effects and Growth Mechanisms | 4 | 4 | 1 | 1 | A brief collaborative clarification distinguishing viral user acquisition loops from network effect retention. The conversation remains highly agreeable and educational. | |
| The Dumpling Emoji Campaign and Unicode Governance | 2 | 6 | 1 | 1 | Jenny educates Sonal on the inner workings of the Unicode Consortium, detailing voting costs and corporate membership. Sonal acts mostly as an interested audience member. | |
| Unicode Emoji Criteria and the Shibuya Moyai Head | 4 | 5 | 1 | 1 | Fred explains the origin story of the Moyai head emoji and platform design divergences. Sonal brings up cross-platform interpretation studies to support the point. | |
| Cross-Platform Rendering, Compound Emoji, and Gender Diversity | 5 | 5 | 1 | 2 | Sonal helps unpack how emoji restore lost vocal/nonverbal nuance in text culture. The discussion flows into technical code points and gender representation issues without dynamic friction. | |
| Emoji Politics, Flag Systems, and Skin Tone Scales | 5 | 4 | 2 | 3 | Sonal raises political issues like the rifle emoji suppression and shares personal anecdotes about Crayola skin tone representation. The dynamic is reflective and collaborative. | |
| Stickers, the Mood Graph, and Digital Identity Masks | 7 | 3 | 2 | 4 | Sonal displays strong editorial knowledge, citing her colleague's thesis favoring stickers over emoji and referencing her work coining the 'mood graph'. Fred defends open standards against proprietary ecosystems. | |
| Crowdsourcing Emoji Dick and Library of Congress Acquisition | 5 | 5 | 1 | 2 | Fred details crowdsourcing Emoji Dick on Mechanical Turk. Sonal offers a creative comparison to Hamilton as a cultural mashup and highlights the curatorial significance of the Library of Congress acquisition. | |
| EmojiCon, Open Standards, and the Future of Visual Media | 3 | 4 | 1 | 2 | Jenny highlights EmojiCon's effort to democratize emoji policy decisions outside corporate Unicode meetings. Sonal asks introductory questions as the episode wraps up. |
Statements from this episode (31)
AT&T First Used the Term 'Network Effect' in 1907
“The first time it was used was in 19 oh seven in a annual report by AT&T.”
Network Effects Originated in Hardware Before Evolving to Software
“Network effects began with hardware. It was first the telephone, then the Ethernet, and in today's world, we talk about it in the context of software companies like Facebook, right?”
Network Effects Are One of the Few Remaining Software Moats
“Network effects are One of the few ways we have left for software businesses to have a moat, to create a moat around their business so that competitors can't eat away at their margins.”
Currier: Network Effects Are the Only Software-Native Defensibility
“The way we look at it is the network effects is really the only one that's native to what we're doing, and therefore it's the most powerful.”
Hariharan: Building Network Effects Is Easier on Mobile Than Web
“It was easier for platforms to build a network, I would argue, on mobile more than web.”
Currier: Messaging Network Effects Will Outlast Social Networks Like Facebook
“Facebook may be around in 10 years, but it might not be, but it's pretty clear that messaging will be around because we're always going to be able to scan text with our eyes. That's a good interface for us. And so messaging is probably a longer term thing for …”
Hariharan: Marketplaces Do Not Inherently Possess Network Effects
“So I don't think that all marketplaces have network effects by definition. They have the potential, but different marketplaces are different at different stages of development in their evolution.”
Airbnb Saw Sluggish Growth for Three Years Before Achieving Liquidity
“The first three years of Airbnb was a real slog, because it's a global marketplace and they were trying to build supply. At the same time, they were also trying to build demand, and they needed to sign up the homes. They needed to make sure that people were, p…”
Asymptoting Marketplace Network Effects Leave Incumbents Vulnerable to Price Competition
“One of them we call the asymptoting two-sided marketplace network effect. It means it doesn't keep going. Doesn't get better past a certain point. We're capable of seeing someone come in and compete with them pretty effectively on a lower price.”
LinkedIn Beat Ryze Because Reid Hoffman Recruited Silicon Valley's Top 4,000
“There was a guy named Scott who started Rise, R-Y-Z-E, in 2000, which was LinkedIn two years before LinkedIn. But he wasn't Reid Hoffman. And Reid knew the top 4000 people in Silicon Valley and emailed. And then once we were on, all the guys in New York wanted…”
Three College Social Networks Existed Before Facebook Launched at Harvard
“Look, there was three college social networks that came before Facebook, but it was the first one started at Harvard.”
Currier's Pre-Facebook Social Network Reached 30 Million Users
“We had thirty million people before Facebook ever launched.”
Currier: Path Would Be a Major Company Without Mobile Competition
“Had there been no competition like there was for Facebook when they were growing for two years in colleges, path would have been a big company.”
Currier: Virality Drives Acquisition While Network Effects Drive Retention
“So the viral effect is about getting new users, and the network effect is about keeping those users, and they're very, very different things.”
Hariharan: Platforms Do Not Require Viral Growth for Network Effects
“Platforms or marketplaces don't need to have network, viral growth to have network effects.”
Unicode Consortium Has 12 Voting Members Paying $18,000 Annually
“Emoji are regulated by the Unicode Consortium, which is a non-profit organization Based in Mountain View, California, it now has 12 full voting members that pay 18,000 dollars a year just to vote on issues, including, like, emoji and other kind of, like, techn…”
Unicode Voting Power Concentrated Among US Tech Giants, SAP, and Oman
“No, so of those 12, so of those 12, nine are U.S. Multinational tech companies. Oracle, IBM, Google, Yahoo, Adobe, Facebook, Microsoft, and Symantec. Then of the other three full voting members, one is the German software company SAP, another is the Chinese te…”
Lee Successfully Passed Unicode Proposals for Dumpling and Takeout Emojis
“In January of 2016, we submitted a full proposal for dumplings, takeout box, chopsticks, and fortune cookies, and got those all passed.”
Unicode Emoji Criteria Explicitly Forbids Celebrities, Deities, and Logos
“Also, there's one of the more interesting rules, which is no celebrities, deities, or logos.”
Lee: Existing Vendor Adoption Is Key to Unicode Emoji Approval
“One of the easiest things, actually, to get emoji passed is showing that a vendor uses it.”
Benenson: Emoji Popularity Stems From Text Saturation in Digital Communication
“And I'll go further and say that a lot of people ask me why emoji have become so popular. And I think it's tied to the fact that we now are just inundated with text. We live in a text culture, right? We communicate via text. Our Careers are run over email. We …”
Benenson: Emoticon Punctuation Usage Dates Back to the 19th Century
“Some of the earliest references to emoticons go back to the 19th century as well, where people, yeah, yeah, people were using colons and dashes and parentheses to express like a wink.”
Complex Emojis Combine Multiple Characters Using Zero-Width Joiners
“Is the gay family emoji originally, where they're not, it's not actually one emoji, you know, the one's like man, man, kid, kid. That is actually a compound emoji of four characters glued together using something called a quote, zero with joiner, which is basi…”
Lee: Standard Emoji Set Offers Only Four Gendered Roles for Women
“Because one of the big problems right now on, on the existing set of women as represented by emoji is like, there are only like really four roles for women to play compared to men. You know, men, you can be a sleuth or you can be, you know, a policeman. You ca…”
Unicode Delegated Political Flag Rendering Decisions to Phone Manufacturers
“What they did was they built this kind of like Meta country system so that you would actually be pairing these country letter emojis together. So CNN would go together, and then it would be up to your phone to decide if you showed the Chinese flag. They pushed…”
Unicode Considers Annual Emoji Expansions Unsustainable, Foresees Inline Images
“Emoji probably won't ever have that amount of, like, customization, and Unicode gets this, and they'll, they actually say, like, we're adding, like, 60 emoji a year. This is unsustainable. We feel like the future is inline images”
Lee: Kim Kardashian's Kimoji Are Image Stickers, Not Real Emojis
“So stickers, I mean, so Kim Moji, for example, Kim Kardashian's quote emoji, they're not actually emoji. Those are just stickers or images that you can text back and forth.”
Benenson: Traditional Sentiment Analysis Is Fundamentally Broken
“Traditional sentiment analysis is really broken because you're using these kind of like stale, rigid semantic definitions.”
Benenson: Facebook Reactions Generate Highly Effective Sentiment Training Data
“What's really interesting about Facebook reactions is, you know, you think you're saying, I love this thing, or I'm sad about this, or I'm angry about this, but what you're actually doing in conjunction with that Is giving Facebook really great label data for …”