My Take on Nfx’s 16 Network Effects (1 of 2) (Tech Strategy – Daily Article)

I recently did a podcast on nfx list of 16 Network Effects.

James Currier and his venture capital firm nfx really do go into a lot of detail about network effects as an idea. It’s worth reading his Network Effects Manual. It’s more directly useable than my approach to this topic which is the following:

  • There are 3 types of networks – which are assets.
    • Physical networks.
    • Protocol networks.
    • People and company networks.
  • There are 5 types of platforms – which are business models that can be built with these assets. Discussed many times.
  • There are 3 types of network effects – which are phenomenon that can emerge in these business models:
    • Direct network effects
    • Indirect network effects
    • Standardization and interoperability network effects

My framework is much more complete in terms of business models, but not as directly useable in terms of network effects as a phenomenon to watch for. NFX’s list of 16 network effects (with their examples) is worth using as a checklist. Here is their list, from strongest to weakest:

  1. Physical (e.g., landline telephones)
  2. Protocol (e.g., Ethernet)
  3. Personal Utility (e.g., iMessage, WhatsApp)
  4. Personal (e.g., Facebook)
  5. Market Network (e.g., Honey Book, AngelList)
  6. Marketplace (e.g., eBay, Craigslist)
  7. Platform (e.g., Windows, iOS, Android)
  8. Asymptotic Marketplace (e.g., Uber, Lyft)
  9. Data (e.g., Waze, Yelp!)
  10. Tech Performance (e.g., BitTorrent, Skype)
  11. Language (e.g., Google, Xerox)
  12. Belief (e.g., currencies, religions)
  13. Bandwagon (e.g., Slack, Apple)
  14. Expertise (e.g., Figma, Microsoft Excel)
  15. Tribal (e.g., Apple, Harvard, NY Yankees…)
  16. Hub-and-Spoke (e.g., TikTok, Medium, Craigslist)

The ones in bold are basically the ones I agree with. They are:

  1. Physical (e.g., landline telephones) – Direct Network Effect
  2. Protocol (e.g., Ethernet) – Direct Network Effect
  3. Personal Utility (e.g., iMessage, WhatsApp) – Direct Network Effect
  4. Personal (e.g., Facebook) Direct Network Effect
  5. Market Network (e.g., Honey Book, AngelList) – Indirect Network Effect
  6. Marketplace (e.g., eBay, Craigslist) – Indirect Network Effect
  7. Platform (e.g., Windows, iOS, Android) – Indirect Network Effect
  11. Language (e.g., Google, Xerox) – Standardization and Interoperability Network Effect
  14. Expertise (e.g., Figma, Microsoft Excel) – Standardization and Interoperability Network Effect

Here is the nfx summary map, which is pretty good. Note the prioritization by strength (strongest in the center). I mostly agree with this prioritization and I think it really captures a lot of their experience working with these companies.

Network Effects Are Naturally Occurring Phenomena

Network effects always remind me of forests full of trees. You walk through the forest or jungle and see lots of types of plants and vegetation. But this one type of organism (a tree) that just dominates. It is everywhere. It jumps out as a structure that is just much more powerful. And that’s how I think of network effects. It is a naturally occurring phenomenon that sometimes happens in a business model. And when it does, it is particularly powerful. It let’s on particular organism take off and dominate an ecosystem.

For trees, there is a lot going on.

  • You have the roots that go into the ground and drink water (and stabilize the structure). Hydrostatic pressure draws the water from the roots up to the branches and leaves, often hundreds of feet above.
  • The trunk is covered in bark to protect it from insects, temperature, and disease.
  • The main trunk grows up through the undergrowth, stabilizes the weight and gets the leaves into the sunlight way above the forest or jungle.
  • The leaves then capture the light and convert it into energy.

It’s a pretty amazing structure with lots of components happening at the same time. It’s a powerful business model and trees are everywhere. But it’s hard to point to one specific thing that gives it its unique power.

Network effects are the same. They are particularly powerful but it’s often hard to point to one thing that enables them. So you look for the net result. You look for services that increase in value with more users or usage.

It’s also worth noting that trees only work in a certain range. They can grow fast but this is mostly when small. They can grow high but then stop at a certain height (before the weight causes them to topple). It’s the same for network effects. They usually only last for a while. As I frequently say, trees don’t grow to the moon and network effects don’t go on forever. It’s not a perfect analogy but I think you get the point.

The power at the core of network effects is mostly the basic equation that shows that the number of linkages in a network can increase much faster than the number of nodes. See the below graphic. If you have 4 nodes and add one more, you increase the number of linkages (connections) from 6 to 10.

So, network effects have long occurred in transportation businesses (pipelines and railroads). And as the world gets more digitalized and connected, we are seeing them fairly frequently. But people tend to read too much into this equation. They like to count the number of connections, when the quality and value of connections can actually change radically. More on that below.

Why I Only Agree with 9 of Nfx’s 16 Network Effects

I think nfx rightly points out that direct network effects are the most powerful type. The first four on their list are all direct network effects. These are also called one-sided network effects. This is when you have only one user group. And more users directly increases the value to other users. This is your phone system and WhatsApp app. This is the most direct version of the above picture of nodes and linkages.

For direct network effects, each new node (say a person you can call) directly increases the value of the service. And the number of connections can go up really fast with more users. Here are the first four on the nfx, which are all direct network effects.

  1. Physical (e.g., landline telephones)
  2. Protocol (e.g., Ethernet)
  3. Personal Utility (e.g., iMessage, WhatsApp)
  4. Personal (e.g., Facebook)

These direct network effects tend to be particularly powerful when they are commodity (i.e., undifferentiated) services. Such as communications or payment utilities. Or Ethernet and Bitcoin protocols. For commodities, there are no other dimensions upon which to compete or differentiate. So, the competition for this service is almost entirely about who has the biggest network effect. Note that nfx breaks personal network effects into Personal and Personal Utility, which the utility being higher on the list.

In their manual, nfx quotes AT&T’s Theodore Vail who is sort of known as the father of network effects. He was the first CEO to talk about them (at least in annual reports to shareholders). He said he had “noticed how hard it was for other phone companies to compete with AT&T once they had more customers in a given locale…”. He noted that “two exchange systems in the same community, cannot be… a permanency. No one has use for two telephone connections if he can reach all with whom he desires connection through one.”

That’s an interesting point. When it is a commodity service with a direct network effect, there is really no reason for a user to have two services. So, the market tends to collapse to one player, and you get a natural monopoly. In marketplaces and other two-sided network effects, we usually see some differentiation in service. And you usually get an oligopoly.

Here is how nfx summarizes physical and protocol network effects, which are the top of their list.

They talk about Metcalfe’s law, which I really don’t like. Although Bob Metcalfe is really an amazing thinker and entrepreneur. He created the Ethernet and 3com. Nfx also points to Reed’s law, which I also don’t like much. I’ve ranted about this before. But this is almost always in discussions about direct network effects. And I agree with their overall conclusion. These are the most powerful.

As you move down the list, you go from direct to indirect (or two-sided) network effects. That is where my five platform business models are much better (in my opinion). That is when you start to characterized different types of interactions between different user groups as business models. So there are marketplaces, innovation platforms, audience-builders and so on. The next three on the nfx are versions of this (which different language and not particularly complete).

  1. Market Network (e.g., Honey Book, AngelList)
  2. Marketplace (e.g., eBay, Craigslist)
  3. Platform (e.g., Windows, iOS, Android)

For direct network effects, you often don’t have a platform business model. You just have the network asset and the network effect. That’s Facebook and WhatsApp. They can be pretty simple services. However, as you add user groups, you get more complicated business models and services. And platforms are a better way to talk about them.

Nfx rightfully points out that “no two 2-sided marketplaces are exactly the same. One way they can significantly differ is in the “value curve.” This refers to how fast the value to the demand side increases as supply increases, and how strong the nfx get when critical mass is reached.”

And nfx shows three types of curves for marketplaces.

Nfx’s description of an asymptotic marketplace is kind of confused. We see these curves in all types of platforms, not just marketplaces. And new types of platforms are emerging based on on new interactions. More on this below. But cutting to the chase, I don’t think 8 or 9 on their list really exist. And I think 10 is just a protocol network effect.

  1. Asymptotic Marketplace (e.g., Uber, Lyft)
  2. Data (e.g., Waze, Yelp!)
  3. Tech Performance (e.g., BitTorrent, Skype)

11, 12, 14 and 15 are more interesting but I just call that a Standardization and Interoperability Network Effect. Especially language and expertise, which I’ll go into in Part 2. The others are more of a consumer share of mind phenomena.

  1. Language (e.g., Google, Xerox)
  2. Belief (e.g., currencies, religions)
  3. Bandwagon (e.g., Slack, Apple)
  4. Expertise (e.g., Figma, Microsoft Excel)
  5. Tribal (e.g., Apple, Harvard, NY Yankees…)

And finally, the last one (hub-and-spoke) is just a different type of network asset with fewer connections. Hub and spoke models are common in airlines, trains, and pipelines. I don’t think it’s much of a network effect at the local level. But it can be important at the national and international level. Federal Express gets most of its power by connecting cities and countries, not streets within a city.

  1. Hub-and-Spoke (e.g., TikTok, Medium, Craigslist)

So basically, the two that I think are compelling are

  1. Language (e.g., Google, Xerox)
  2. Expertise (e.g., Figma, Microsoft Excel)

I’ll go into them in Part 2. And I’ll show my method for network effects.

Cheers, Jeff


Related articles:

From the Concept Library, concepts for this article are:

  • Network Effects

From the Company Library, companies for this article are:

  • NFX / James Courrier

Photo by Nastya Dulhiier on Unsplash


I write, speak and consult about how to win (and not lose) in digital strategy and transformation.

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