The Importance of Network Effects in Web3

The Internet has had a huge impact on the speed at which ideas and cultural phenomena can spread. Businesses that successfully harness the power of network economics are more likely to amass a significant competitive advantage, bolster their market position, and become superdominant in their field.

For founders looking to make an impact with an Internet business, network effects are an essential concept to master. Network effects are ubiquitous in the software industry across various technologies and products including operating systems, search engines, social networks, instant messaging systems, online marketplaces, ridesharing applications, and much more. Outside of their impact on software and technology, the spread of certain religions, cultures, and languages can be partially attributed to network effects.

Network effects have a significant influence on the competitive landscape of the Web3 industry. Network effects can decrease the viability of new, technologically superior entrants to the market, helping incumbent projects maintain a competitive advantage and continue to expand.

This article outlines what network effects are, explores some historical examples of network effects, and provides an overview of how network effects can help Web3 projects establish a strong economic moat.

What Are Network Effects?

A network effect is a phenomenon where an increased number of participants in a system increases the value or utility of the underlying network and the service it underpins. The phenomenon results in users deriving more value from the service as more users join the network, creating a positive feedback loop that induces more demand for the service. In short, network effects make a product more useful as more people join its underlying network.

Network effects have often been illustrated by the example of the telephone: A single telephone is useless, but the utility of the telephone network increases with each new telephone added. As the total number of connections a given node (telephone) can establish increases, the utility of the network is enhanced. Similarly, the more users a social media platform can attract, the more valuable it may become as a service to connect with others. The effect is comparable with payment networks such as VISA or ridesharing networks like Uber—the greater the number of users, the more valuable the network.

Direct, Indirect, and Negative Network Effects

Network effects can be direct or indirect. A direct network effect is when increased usage directly contributes to more network utility, such as in the case of the telephone. This can also create secondary effects that arise as a result of a network effect being present. For example, if a new cryptocurrency aims to become a competitor to Bitcoin, it not only has to compete with its well-established foundational ethos and time-tested security but also with its surrounding ecosystem of developers, tooling, evangelists, and users, which its network effects have helped attract over time.

An indirect network effect happens with two different participant groups, whereby a network effect generated by one group creates a comparable impact for the other group. For example, the hardware of a video game console might become more valuable as additional third-party game developers create high-quality games for the console.

A negative network effect occurs when a new user subtracts value from the network instead of adding to it. For instance, most modern cities have a traffic problem. In some cities—due to car-centric planning—a car may be the only way of effectively moving around. As a larger and larger portion of the population gets cars, the less throughput the highway system can handle, further diminishing the utility of the highway system. Common sense may dictate that the solution is to simply build more highways, and that is often what urban planners end up doing. However, building more roads for cars may even worsen the situation by inducing more demand and ultimately generate more congestion by reinforcing the existing negative network effect.

What Looks Like a Network Effect But Isn’t?

Network effects should not be confused with network externalities, which dictate how the demand for a product is dependent on its apparent demand from other buyers. A restaurant with a full house and large queue may attract more customers than an empty one, as customers are willing to trade the convenience of being served instantly for the potential of a better experience, as influenced by the buying patterns of other customers.

Network effects are also often mistaken for economies of scale, in which the production cost of a good decreases as a function of the total number of units produced. While this is true in industries such as manufacturing, network effects only concern themselves with the demand side of economies of scale—they increase customers’ willingness to participate, not necessarily the ability of the supplier (or the underlying network) to create more of the product.

How Is a Network Effect Established?

In order for a network effect to take hold, a critical mass must be achieved. Since network value increases with each additional participant, existing users are incentivized to find new participants and generate exponential growth. While it’s not often clear why network effects occur, they often have staying power once established, even though diminishing growth is expected as the network expands.

Networks that are more open and neutral are more likely to amass long-lasting network effects—if bias is encoded on the base level, a network effect may be harder to establish. The Internet is built on open standards that don’t distinguish between one user or the other. One can imagine a parallel universe in which the Internet is gated for select participants—it’s hard to see the Internet having such a transformational impact on society while only being able to penetrate a portion of it. With that said, many large entities would have an interest in a permissioned Internet as they would be able to control the information flowing through the network, which is why the idea of a decentralized Web3 is critical for preserving the Internet as an open-access public good.

Network effects also have an impact on economic diversity, especially when it comes to software and communications technology. Despite the overall size of the World Wide Web continuing to expand and new products and services continually being created, the number of dominant players on the Internet seems to shrink. As the incumbent players establish strong network effects, switching costs for users become higher, ultimately meaning Web2 converging toward an increasingly monopolistic status quo.

Network effects can also unwind. This can be attributed to a number of factors, such as achieving close to maximum penetration in the target demographic, an inability to adapt to changing circumstances, an unsustainable economic model, and more.

Historical Examples of Network Effects

Facebook vs. MySpace

Whether or not it was a conscious decision, Facebook had a highly effective way of bootstrapping its initial network effects. The platform started out as a way for university students to connect with each other using their real identities—an uncommon feature for a social media platform during the early days of the Internet. This created a two-fold boost in helping the now-dominant platform establish a lasting network effect.

First, it helped create immediate connections between users right when they joined the platform. Since more connections between users led to better service utility, Facebook could produce a more interconnected social graph quicker than other social media platforms that incentivized anonymity. If a new user joins a platform to connect with others but there’s no one they can connect with, they might never log in again, and the escape velocity for achieving critical mass is never reached. In addition, people are more likely to make new connections on the network if they can already “screen” each other through mutual acquaintances.

The second advantage of Facebook’s business model was the psychological effect this system created, akin to the fear of missing out (FOMO). If a student’s friend group is already on Facebook, they might feel like they’re missing out on key social activities and have a harder time nurturing relationships with their friend group—unless they also join the platform. By establishing a social network on the basis of real-world relationships, Facebook was able to bootstrap a strong network effect with student relationships and apply the same formula to essentially any community.

While there were other factors at play, MySpace—the dominant social network during the early days of Facebook—couldn’t keep up with the explosive growth of Facebook. Largely due to Facebook’s effective network mechanisms, which focused on generating user growth, MySpace faded into relative obscurity while Facebook became one of the most transformative platforms in social networking.
‍

Microsoft Encarta vs. Wikipedia

The importance of network effects is also well illustrated by the example of Microsoft Encarta—a digital multimedia encyclopedia published between 1993 to 2009. While the concept of an encyclopedia continuously updated with new information was an enticing idea in 1993, Encarta ended up having less staying power than an open competitor that launched almost a decade later.

Launched in 2001 with a considerable competitive disadvantage, Wikipedia is a free encyclopedia written and maintained by a community of volunteers through open collaboration. While its editing system does introduce its own flaws, Wikipedia has become one of the most popular websites on the Internet, with a permanent spot among the top-10 most visited websites in the world.

In theory, Microsoft Encarta’s paid subscription service could have achieved the same feat. However, due to Wikipedia’s focus on community maintenance as a free and open “knowledge network,” it attracted like-minded contributors who believed in the idea that information should be free, inspiring them to spend their free time making this vision a reality. The larger the knowledge network gets, the more readers it attracts, who contribute increasingly useful information, which in turn attracts more readers who become contributors, and so on.

Compared to Microsoft Encarta’s paid subscription model and maintenance by employees, Wikipedia’s network effects gave it a considerable competitive advantage. Microsoft could cease to exist or the company could discontinue the product (as it ended up doing), but the idea behind Wikipedia and its core values will likely live on. A for-profit endeavor could potentially be higher quality, more comprehensive, and provide a better user experience, but it’s ultimately supported by weaker network effects. Once a platform like Wikipedia starts gaining critical mass, it becomes prohibitively resource-intensive for a company to compete with the open encyclopedia’s growing utility.

Money Network Effects

Money is a collective myth, and network effects impact the propagation of these myths through society. Just as a fax machine is valuable only if other people also have fax machines, a currency is valuable only if people are willing to accept it as payment.

In broad terms, money has gone through the following iterations:

• Commodity money: Commodity money has intrinsic value—value that is independent of its value as money. Common examples are gold and silver coins, silk, salt, or instant ramen noodles in modern-day prisons.
• Representative money: Representative money is currency that can be exchanged for a fixed value in a commodity (most commonly, silver, or gold) that it represents.
• Fiat money: Fiat money has no intrinsic value nor can it be exchanged for a valuable commodity—its value is backed by the government issuing it.
• Cryptographic money: Cryptographic money is the newest form of money. It derives its value from being a type of digital commodity secured by math and physics.

For a new iteration of money to compete with a previous one, it has to overcome its well-established network effects and large userbase, which generally takes a long time. Even so, new iterations can even bolster a previous version’s network effect. Representative money can build upon the network effects established by commodity money, with a different myth attached to the underlying object (the commodity) and represented in a different way (paper currency). Another example is the euro, which effectively replaced many existing money networks while also harnessing their underlying network effects.

While one could argue that commodity money doesn’t require a network effect since its utility as a commodity already gives it value, this doesn’t give the commodity value as money. For a commodity to also become money, the underlying myth (and its network effect) must be established. For example, ramen noodles are commonly used in prisons as a form of currency. They are divisible, fungible, easily stored, and have underlying utility as a non-perishable good. Even so, ramen’s network effect as money doesn’t extend to the tax system but remains tied to a specific circumstance.

How Network Effects Can Help Build a Competitive Moat in Web3

Blockchain-based tokens—a fundamental primitive of the Web3 ecosystem—have immense power to align incentives between different participant groups and organize communities through economic and game-theoretic mechanisms. Many Web3 projects don’t aim to build a traditional product or service but rather establish a network that organizes users, value, or data in an economically sustainable manner without the need for a centralized administrator to ensure continual operation.

Since the Web3 ecosystem is built upon the foundational values of decentralization and trust-minimization, network effects play a key role in determining the success or failure of many Web3 projects, such as DeFi protocols, blockchain games, decentralized organizations, NFT projects, and metaverses. Simply put, the more users a crypto network can amass, the more utility it may provide to its users. In addition, due to the majority of Web3 projects being open-source, projects need to make themselves resilient against antagonistic forks. Code can be easily forked in an attempt to capitalize on the success of an established platform, but other aspects of a successful Web3 project with strong network effects are impossible to replicate—its community, for example.

Without the benefit of hindsight in such a nascent ecosystem, it’s difficult to determine whether the current network effects one can observe in Web3 will have lasting staying power over the coming decades. However, there are a few key considerations that can be taken into account by Web3 projects looking to establish network effects.

Community

A ”community-first” approach is incorporated into many modern businesses’ marketing strategies. However, the concept’s relevance in Web3 is more intricate than simply wanting to convince as many people as possible to buy a product—Web3 projects aren’t just community-focused but sometimes entirely community-run. Creating, growing, and managing a community requires a lot of effort but can be extremely powerful in attracting more developers, users, evangelists, and enthusiasts. New participants apply their unique skills to further grow the network and its community, helping create a positive feedback loop that ultimately creates more demand for the network. By being comprised of not only consumers but consumer-owners, Web3 communities help establish strong flywheel effects that can generate exponential user growth.

Economic Model

Creating a communication or value network in Web3 requires a delicate balance of incentives engineered to create and sustain alignment between a set of distributed participant groups. As there is no centralized administrator to make decisions, the underlying economic model needs to establish a sustainable framework for standardizing decision-making processes, enhancing the cryptoeconomic security of the network, and further incentivizing the network’s continual operation. A sustainable economic model sets up the network with a strong foundation for long-term success.

Liquidity

Blockchain tokens are a fundamental building block of the Web3 economy, facilitating standardized interactions between participants through deterministic logic. Optimizing the efficiency with which these interactions can happen is key to achieving sufficient decentralization.

First-Mover Advantage

As network effects take a long time to establish, some projects opt to launch as soon as possible to start bootstrapping a network effect, especially when it comes to emerging business verticals. Even if competitors launch technologically or otherwise superior services, the established network effects may be enough for the first mover to maintain its competitive advantage and establish an economic moat. With that said, when it comes to value networks, taking a security-first approach is key. A loss of user funds due to a hasty go-to-market strategy can instantly reduce trust in a product that may never be regained.

Composability

It’s also important to note that due to the benefits of permissionless composability, the Web3 ecosystem as a whole may create stronger network effects than its existing counterparts. The ability to arbitrarily combine distinct decentralized applications through interconnected loops can produce previously unimaginable products and boost innovation through emergent effects that centralized business models can’t compete with.

For instance, in the Web2 world, users are forced to create separate accounts for each social networking service with no interoperability between different platforms. Web3-enabled communication and social networking protocols will enable users to leverage their existing social graph across different applications, effectively shifting the competition to the application layer while keeping the underlying set of protocols decentralized and credibly neutral as a settlement layer powering social interactions.

Conclusion

Whether it’s social media, money, or play-to-earn gaming, network effects play a critical role in the spread of ideas and the adoption of new technology, and are one of the key considerations underpinning the success of communication and value networks in Web3.

It’s impossible to say which protocols making up the current technology and application stack of the blockchain industry will be able to amass long-lasting network effects, but projects that successfully apply network economics to their business model will likely have a better chance of garnering and maintaining an economic moat.