Bigger is Better: Blockchains as Network Technologies
Like many other information technology products, public blockchains can be thought of as networks. Networks have the special feature that their value to any one user depends on the number of additional users—often referred to as “network effects”.
Network effects predict a nonlinear relationship between user adoption and network value. This is why assets tied to a network’s economic value—like social media stocks or blockchain tokens—may produce outsized returns during certain phases of network growth.
Despite the crypto industry’s short history, we can already observe these patterns in the data. Token valuations point to network effects both across protocols and over time.
The number of active users of public blockchains today is still very low compared to mainstream internet applications. Grayscale Research believes rising adoption could result in nonlinear token price appreciation for blockchains that reach a critical mass.
Because networks depend on the connections between users, they often feature a nonlinear relationship between adoption and value. There is a simple mathematical reason for this: the number of possible connections between users rises much faster than the number of users. For example, a network with two users has one possible connection; a network with four users has six possible connections; a network with eight users has 28 possible connections; and so on. Therefore, if a network’s utility is also a function of the number of possible connections, there should also be a nonlinear relationship between the number of users and network value.
Through this mechanism, the addition of each additional network participant initially has a large effect on network value. However, eventually the law of diminishing marginal returns takes over, and each new participant has a smaller and smaller effect on network value. Therefore, in network industries we should expect to see an “S-Curve” pattern, where network value initially rises exponentially as network size passes through a critical mass, then later levels off with diminishing marginal returns (Exhibit 1).
Exhibit 1: Network effects predict an “S-Curve” relationship between size and value
Network Effects and Blockchains
Exhibit 2: Blockchains with more users trade at higher market capitalization
Exhibit 3: Bitcoin and Ethereum market caps have tracked user adoption
Implications for Investors
The fact that public blockchains are network technologies with observable network effects has a number of implications for crypto investors. Most importantly, these patterns can help explain why we may see outsized token returns if network adoption continues to grow. Like social media platforms, blockchains that reach a critical mass of network participants typically would see value grow more than one-for-one with the number of users (up to the point of diminished marginal returns).
At Grayscale, we believe we are approaching this moment for a number of public blockchains and their associated tokens. Although blockchain adoption has grown steadily over time, the number of users of the largest chains today are still very low compared to mainstream social media platforms. According to data from Token Terminal, Ethereum has just 6.1 million monthly active users.[9] To put this into context, the number of monthly unique visitors to Yelp is more than 20 times higher (Exhibit 4). If Ethereum or other blockchains can garner mainstream adoption, the number of additional potential users would be very large. And, because of the nonlinear relationship between adoption and network value, the implication for token valuations could be meaningful.
Exhibit 4: Ethereum adoption still very low compared to major social media applications
Risks and Limitations
Second, we do not yet fully understand how competition among blockchains will play out, especially for the smart contract platform category. If it is easy for consumers to switch between networks, it may be harder for any one project to dominate the market and potentially benefit from network effects. Moreover, even if eventual market dominance is attractive for a token’s investors, the resulting concentration could have negative implications for competition and possibly users.
Third, network effects can work in reverse. If rising user adoption can result in nonlinear increases in value, falling user adoption can result in nonlinear declines in value (“death spirals”). Network effects help defend incumbents, but do not necessarily insulate them from competitors offering superior products.
Investing in network technologies like public blockchains requires an understanding of network effects. Network effects predict that size will be an important determinant of network value: like in many information technology industries, bigger is better. Therefore, active addresses and other measures of blockchain users should be considered an important fundamental for token prices. Moreover, network effects suggest that token valuations may experience nonlinear increases with growing adoption (up to the point of diminishing marginal returns).
[1] Economists also refer to this phenomenon as “network externalities” or “demand-side economies of scale”, to contrast with supply-side economies of scale in traditional manufacturing industries. For additional background on these topics see, for example, The Economics of Information Technology: An Introduction, by Hal Varian, Joseph Farrell, and Carl Shapiro, 2004.
[2] See, for example, Nobuhiro Kiyotaki and Randall Wright, “On Money as a Medium of Exchange”, Journal of Political Economy, 1989; or Marc Flandreau and Clemens Jobst, “The Empirics of International Currencies: Network Externalities, History, and Persistence”, The Economic Journal, April 2009.
[3] Robert Metcalfe, “Metcalfe’s Law after 40 Years of Ethernet.” Computer, December 2013.
[4] See, for example, Antonio Scala and Marco Delmastro, “The Explosive Value of the Networks.” Nature, August 2022.
[5] This extends beyond information technology to other network goods. Economist Charles Kindleberger famously noted: “For better or worse—and opinions differ on this—the choice of which language and which currency is made not on merit, or moral worth, but on size.”
[6] Data for March 2023 through September 2023, when active users and market cap were available for the selected tokens.
[7] This figure is the point on the fitted line where the slope equals one.
[8] We exclude two ten-day periods in Q4 2016 when Ethereum active addresses were very high, and should be treated as outliers.
[9] As of September 2023; Token Terminal defines active users as active sender addresses.
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Logarithmic scale is defined as a method for graphing and analyzing a large range of values in a compact form
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